The signal transducer and activator of transcription 3 (STAT3) signalling pathway is activated through phosphorylation by Janus kinases in response to a diverse set of immunogenic and non-immunogenic triggers. Several distinct lines of evidence propose an intricate involvement of STAT3 in neural function relevant to behaviour in health and disease. However, in part due to the pleiotropic effects resulting from its DNA binding activity and the consequent regulation of expression of a variety of genes with context-dependent cellular consequences, the precise nature of STAT3 involvement in the neural mechanisms underlying psychopathology remains incompletely understood. Here, we focused on the midbrain serotonergic system, a central hub for the regulation of emotions, to examine the relevance of STAT3 signalling for emotional behaviour in mice by selectively knocking down raphe STAT3 expression using germline genetic (STAT3 KO) and viral-mediated approaches. Mice lacking serotonergic STAT3 presented with reduced negative behavioural reactivity and a blunted response to the sensitising effects of amphetamine, alongside alterations in midbrain neuronal firing activity of serotonergic neurons and transcriptional control of gene networks relevant for neuropsychiatric disorders. Viral knockdown of dorsal raphe (DR) STAT3 phenocopied the behavioural alterations of STAT3 KO mice, excluding a developmentally determined effect and suggesting that disruption of STAT3 signalling in the DR of adult mice is sufficient for the manifestation of behavioural traits relevant to psychopathology. Collectively, these results suggest DR STAT3 as a molecular gate for the control of behavioural reactivity, constituting a mechanistic link between the upstream activators of STAT3, serotonergic neurotransmission and psychopathology. Despite half a century of intensive research, our insight into the pathophysiological principles underlying these excruciating conditions remains incomplete, thus limiting the development of urgently needed novel diagnostic and therapeutic strategies. Efforts originating from the Research Domain Criteria concept, launched by the National Institute of Mental Health in 2008 [Globally, 350 million people suffer from major depressive disorder (MDD), which is estimated to constitute the number one cause of disease burden by 2030. However, MDD is not the only severe mental illness presenting with an epidemic propagation. 60 million people worldwide are confronted with a diagnosis of bipolar depression (BP) and patients suffering from schizophrenia (SCZ) are estimated at 21 million , for behavioural phenotypes with relevance across traditional psychiatric disease entities and to interrogate the underlying neural principles. Activation of the transcription factor STAT3 is consequent to an intracellular molecular cascade involving activation of Janus kinase (JAK) in response to a diverse set of immunogenic and non-immunogenic stimuli, ultimately culminating in the phosphorylation and dimerisation of STAT3. Upon phosphorylation and dimerisation, STAT3 translocates to the nucleus to regulate gene expression directly, or by forming complexes with other transcription factors .SertCre/+; Stat3fl/fl). A thorough behavioural characterisation of this mouse model, together with in vivo electrophysiological recordings and transcriptomic analysis of the dorsal raphe (DR), where most serotonergic cell bodies are located [The focus on STAT3 and its dependent molecular pathways originated from a set of observations which collectively propose an intricate involvement in neural function relevant to behaviour in health and disease: (i) STAT3 is a major orchestrator of cellular immune processes in response to cytokine activation, which has been highly implicated in psychopathology within the framework of the “immune hypothesis” –10; (ii) located , was comPlease see the supplementary materials and methods for a comprehensive description of all procedures. A list of sample sizes employed in each experiment can be found in Supplementary Table SertCre/+; Stat3fl/fl (“KO”) and littermate SertCre/+; Stat3fl/+ (“control”) were bred from founder pairs of B6.129S1-Stat3 < tm1Xyfu > /J  [Sert+/+; Stat3fl/fl were employed for viral knockdown experiments. Mice were kept on a 12/12 hour light/dark cycle in a temperature-controlled colony room (22 ± 1 °C) and provided with food and water ad libitum. Single-housed male and female littermate mice (8–16 weeks at experiment onset) were used for all experiments. All animal experiments were conducted adhering to the European Communities Council Directive of September 22, 2010 (2010/63/EU) after approval by the national authorities .Conditional STAT3 knockout mice ME, USA)  and B6.1ME, USA) . Sert+/+In vivo recordings of DR 5-hydroxytryptamine  neurons were essentially conducted as previously reported .An Axoclamp-2B amplifier was used to obtain extracellular recordings and analogue-to-digital data conversion was done using the Digidata-1440 interface. Data processing and analysis were performed using the software pClamp-10 and Clampfit, respectively .Preliminary analysis of STAT3 KO behaviours revealed no genotype × sex interactions, therefore mice of both sexes were collapsed into a single group of each genotype, and mixed-sex groups were used in all other experiments.The SPT followed an established protocol  and relaThe NSF was conducted according to a published procedure  and the The FST was carried out using automated movement tracking software  as reported earlier and the relative immobility (%) was calculated .The OFT was performed to measure spontaneous locomotor activity   in arenaThe LDB was used to evaluate anxiety-like behaviour  , 42 emplAnxiety-like behaviour in the EPM  was determined as previously described , 43 usinMotor coordination was tested using an automatic RR system for rodents (MedAssociates) . Averaged-amphetamine  (protocol adapted from [Locomotor sensitisation (% change in distance travelled from day 1) was evaluated in the open field (apparatus as above) for 30 minutes after i.p. injection of ted from ).d-amphetamine  or saline injections . Side preference (%) on the test day was calculated after 6 days of training.CPP was performed using a biased experimental paradigm  employinD-amphetamine sulphate  was dissolved in physiological saline (0.9%) for administration during amphetamine sensitisation and CPP. To obtain the indicated free base concentrations, a conversion factor of 1.36 was applied to calculate the amount of d-amphetamine sulphate needed.Mice were anaesthetised  and transcardially perfused using 4% paraformaldehyde in PBS. 30 µm sections of the DR (Bregma −4.0 mm to −5.2 mm ) were co2; ventral to cerebral aqueduct at midline) and obtained values were compared between groups.Images were obtained using a Nikon A1 laser scanning microscope and the software NIS-Elements AR  using 20X, 60X or 100X objectives. Quantification of STAT3 immunoreactivity within the ROI or within specific cell types was performed using the software ImageJ , 48. Bri2) directly below the aqueduct [Mice were killed by cervical dislocation, whole brains were extracted, rapidly embedded in O.C.T. medium  and stored at −80 °C. The DR was collected from four 300 µm-thick coronal sections (Bregma −4.0 mm to Bregma −5.2 mm ) by dissaqueduct . Tissue RNA extraction was performed using miRNEasy Mini Kit  by following the manufacturer’s protocol precisely (“Quick start guide”). RNA was eluted from the column filter in the final step using 40 µl of RNAse-free water.p values were corrected by applying the Benjamini-Hochberg method and a false discovery rate of 5% to produce q values.Samples were quality-checked using a Bioanalyzer  and showed RNA Integrity Numbers above 7.0. Library preparation and sequencing was performed as previously described . Data weThe full dataset is available online in the NCBI GEO (gene expression omnibus) functional genomics data repository under the accession number GSE146101.450 ng of DR RNA were used for cDNA synthesis using the DyNamo cDNA Synthesis Kit (ThermoFisherScientific) by following the manufacturer’s instructions. STAT3 expression relative to β-actin expression were then determined using qRT-PCR using the ΔΔC(t) method . Primer Stat3fl/fl mice were deeply anaesthetised with isoflurane. 0.5 µl of AAV-Cre  or AAV-GFP  were infused over 8 min at each of the following coordinates  x = 0.0, y = −4.5, z = −2.7.Male and female Upon completion of behavioural testing mice were transcardially perfused and 30 µm DR sections were examined for the overall spread of GFP+ cells.t test was applied wherever two groups were being compared  and in the case of a repeated measures design, repeated-measure ANOVA was applied.GraphPad Prism 7.0  was used for all statistical analyses and preparation of graphs. Statistical outliers falling outside the interval mean ± 2 SD were pre-excluded from analyses to avoid skewing the data, as this interval represents >95% of values around the mean of a normally distributed dataset. Two-tailed, two-sample Student’s SertCre/+; Stat3fl/fl; hereinafter “KO”) bearing a STAT3 deletion in all SERT-expressing cells, i.e., predominantly serotonergic neurons of the midbrain DR [STAT3 conditional knockout mice . However, STAT3 immunoreactivity in microglia  and astrocytes  was generally low and showed no differences between groups.STAT3 expression in DR 5-HT neurons of KO and control mice was evaluated by specific immunoreactivity in brain sections. STAT3 fluorescence intensity was quantified within 5-HT-labelled cells , suggesting an impact of STAT3 deletion on intrinsic excitability features of 5-HT neurons.In vivo electrophysiological recordings of extracellular action potentials in this region revealed significantly higher average firing rates of serotonergic neurons in KO compared to controls . Furthermore, KO animals did not display any alterations in motor coordination in the RR . Serotonergic STAT3 knockout did not influence hedonic behaviour in the SPT . In contrast, STAT3 KO mice did behave differently than control animals in the tests probing emotional behaviour under stressful conditions: in the NSF, KO mice displayed shorter latencies to feed from a food pellet placed in the centre of a brightly lit arena, indicating that when the drive to eat is contrasted by the fear of an illuminated open space in a novel environment, KO animals are more ready and able to override fear in order to terminate the conflict situation . Importantly, this behavioural effect was not driven by overt differences in metabolism or hunger between groups: Neither individual body weight , nor relative weight loss during the 24-h food restriction period , nor food consumption immediately post-NSF  were impacted by serotonergic STAT3 deletion. In the FST, mice lacking serotonergic STAT3 displayed lower levels of immobility than controls , evidencing an augmented proactive coping style of the KO group when faced with the adversity of inescapable stress [t47 = 1.324; p = 0.192; n = 24–25/group; EPM: t47 = 1.324; p = 0.192; n = 24–25/group), suggesting that STAT3 knockout in serotonergic neurons does not influence anxiety-like behaviour in a situation of free choice.In light of the reported association between STAT3 signalling and mental illnesses –29, we nt11 = 3.556; p = 0.005; n = 6–7/group). Of the 194 DEG, we found that 111 are experimentally validated STAT3 targets (DEG×STAT3) according to ChIP-Atlas [F1,15 = 5.763; p = 0.029; sensitisation – F7,105 = 36.310; p < 0.0001; STAT3 knockout x sensitisation – F7,105 = 2.652; p = 0.015; n = 8–9/group). In contrast, in the CPP both groups showed similar levels of preference for the drug-paired chamber after conditioning .We next examined molecular patterns paralleling the behavioural consequences of STAT3 deletion in the DR. mRNA sequencing was chosen as a bias-free approach to determine differences in gene expression between controls and STAT3 KO. Initial analysis revealed 194 significantly differentially expressed genes (DEG) Fig. . We specIP-Atlas , 58 reveIP-Atlas ) for DEGIP-Atlas , this asStat3fl/fl mice . NeuN and DAPI immunoreactivity were employed to assess the average efficiency of viral transfection and neural tropism in the AAV-Cre group . Behavioural experiments focused on paradigms in which genotype effects in the STAT3 KO experiments were observed. Similar to our observations in the STAT3 KO experiments, viral-mediated STAT3 knockdown did not lead to any alterations in baseline locomotor activity in the OFT . Likewise, hedonic behaviour in the SPT was unchanged in AAV-Cre-injected mice compared to controls . However, also mirroring STAT3 KO behaviour, AAV-Cre-injected mice exhibited shorter latencies to feed in the NSF  than the AAV-GFP group. We controlled for basic differences in metabolism and appetite which may impact this behavioural trait by comparing body weight , relative weight loss after food restriction  and post-trial food consumption , none of which were impacted by DR STAT3 knockdown. In the FST, the AAV-Cre group showed less immobility , once again reminiscent of the results obtained in germline genetic STAT3 KO mice. Locomotor activity changes in response to amphetamine administration were significantly blunted in the AAV-Cre group when compared to AAV-GFP mice, phenocopying STAT3 KO behavioural performance .In an attempt to disentangle possible developmental effects of STAT3 deletion from short-term or acute effects, we proceeded with a viral approach in order to knock down STAT3 in the DR of adult mice using AAV-GFP (control group) or AAV-Cre (STAT3 knockdown) in ice Fig. . STAT3 eice Fig. , which cWe here sought to systematically investigate the relevance of STAT3 in the midbrain serotonergic system for behavioural elements relevant to psychopathology. Integrating behavioural, electrophysiological and molecular units of analysis, we aimed to shed light on the functional consequences following the disruption of STAT3 signalling in the DR under consideration of neurodevelopmental aspects.We identified serotonergic STAT3 as a modulator of DR neuronal excitability, determinant of behavioural reactivity and as a molecular switch for a transcriptional program determining intercellular communication with relevance to psychopathology.In the first step, we focused on neuronal activity in serotonergic cells and revealed an impact of STAT3 deletion on the function of serotonergic cells reflected in increased 5-HT firing activity in KO DR neurons in vivo. This finding not only provided a first proof that the transcriptional program orchestrated by STAT3, respectively by the loss thereof, controls serotonergic cell activity but also indicated possible consequences at the behavioural level. Indeed, the “serotonergic vulnerability” theory postulates that the neurobiological correlate of increased susceptibility to environmental stressors could be a disrupted and therefore functionally dampened 5-HT system –63, as dOur data are also well in agreement with existing literature. It has been reported that systemic blockade of STAT3 by the small molecule stattic reduced immobility in the FST  and thatThe STAT3 KO line used in the present study constitutes a functional STAT3 knock-out with a Cre-dependent deletion of exons 18, 19 and 20 harbouring the coding region for the SH2 domain, essential for STAT3 function . The immqRT-PCR analysis confirmed the significant decrease of this specific region of the STAT3 transcript in DR tissue of STAT3 KO versus control animals. Since bulk tissue of the DR and not serotonergic cells only was used as input for the qRT-PCR, the observed decrease is only modest, but highly significant.The transcriptional patterns revealed by RNA-Seq in STAT3 KO DR tissue are not only surprisingly rich in processes and pathways relevant to psychopathology, at the single gene and the gene set level, but also corroborate the relevance of STAT3 for intercellular communication, with a striking 13 out of the 16 significantly enriched categories and pathways across both analyses related to neurotransmission-relevant processes. These include genes pertinent to ion transport, the synaptic vesicle cycle, calcium signalling, and neurotransmitter transport. The significant increase in spontaneous firing of DR 5-HT neurons in STAT3 KO also ties in perfectly with the reported transcriptional changes in pathways mediating synaptic transmission and neuronal excitability.Guided by the RNA-Seq analysis, we next extended the behavioural examination to paradigms pertinent to amphetamine addiction, which are therefore also relevant to behavioural constructs of the positive valence system. The blunted sensitisation response of STAT3 KO mice is notable considering the previously discussed evidence for reduced stress sensitivity, as the interrelationship between stress and psychostimulant action is solidly documented and cross-sensitisation between repeated exposure to amphetamine and stress is well-described –79. NotaAn important aspect for the interrogation of the involvement of STAT3 in psychopathology is the potential relationship of the behavioural phenotype of STAT3 KO mice to neurodevelopmental processes. As the acute viral-mediated, location-specific knockdown of STAT3, which indicated that short-term depletion of STAT3 in the DR had comparable effects to the constitutive knockout of STAT3 in 5-HT neurons, we concluded that developmental mechanisms are likely not involved.Taken together, by integrating behavioural with cellular and molecular units of analysis we are in a position to propose a role for STAT3 in the DR in the display of behavioural elements relevant to psychopathology. Collectively, the herein presented data allow for the postulation of a tentative model in which serotonergic STAT3 acts as a molecular gate to tune behavioural reactivity. According to this model, the transcriptional program controlled by STAT3 in the DR constitutes a mechanism regulating sensitivity and behavioural response to aversive conditions, possibly via modulation of central serotonergic function. The diversity of stimuli capable of inducing STAT3 activity further highlights the context-dependent pertinence of our findings, which collectively postulate a mechanistic involvement of serotonergic STAT3 in the regulation of behavioural reactivity, with relevance for psychopathology across boundaries of distinct disease entities.Suppl Materials and MethodsSuppl Fig 1Suppl Table 1Suppl Table 2Suppl Table 3ASuppl Table 3BSuppl Table 3CSuppl Table 3C


Uniparental genetic systems are unique sex indicators and complement the study of autosomal diversity by providing landmarks of human migrations that repeatedly shaped the structure of extant populations. Our knowledge of the variation of the male-specific region of the Y chromosome in Native Americans is still rather scarce and scattered, but by merging sequence information from modern and ancient individuals, we here provide a comprehensive and updated phylogeny of the distinctive Native American branches of haplogroups C and Q. Our analyses confirm C-MPB373, C-P39, Q-Z780, Q-M848, and Q-Y4276 as the main founding haplogroups and identify traces of unsuccessful (pre-Q-F1096) or extinct  Y-chromosome lineages, indicating that haplogroup diversity of the founder populations that first entered the Americas was greater than that observed in the Indigenous component of modern populations. In addition, through a diachronic and phylogeographic dissection of newly identified Q-M848 branches, we provide the first Y-chromosome insights into the early peopling of the South American hinterland (Q-BY104773 and Q-BY15730) and on overlying inland migrations (Q-BY139813). The Americas were the last continents to be colonised by modern humans. For decades, scientists of different disciplines have debated the modes and times of their peopling ,2,3,4,5 Although autosomal genome-wide data allow researchers to investigate genetic admixture ,21, and Although mtDNA founding haplogroups have already been well-characterised in the literature ,32,33,34Here, we gathered Native American MSY sequence data from different approaches and sources, which were analysed through a novel workflow. The aim was to obtain a comprehensive and ascertainment-bias-free phylogeny as well as a diachronic view of continental Indigenous MSY variation.Our dataset was obtained by retrieving modern and ancient Y-chromosome sequences potentially belonging to Native American haplogroups C and Q from the literature. In addition, worldwide samples from the Human Genome Diversity Project  represenN = 147) and Low Coverage . The ancient dataset includes 293 samples belonging to haplogroups C and Q [The modern dataset consists of 425 males with Y chromosomes clustering within haplogroups C or Q ,41,44,45 C and Q ,53,54,55Given the heterogeneity of the dataset, sequences were converted to the BAM format, unless only VCF files were available. The BWA-MEM algorithm v. 0.7.17  was usedCoverage estimation was assessed using the SAMtools depth command. Furthermore, we calculated the percentage of positions covered in the total of ~10 Mb regions of interest defined by Poznik et al. , by usinFreeBayes 1.3.4  was usedFinally, the multi-VCF was converted into PHYLIP format using the python script (vcf2phylip.py) introduced by Ortiz .https://github.com/qiyunzhu/AfterPhylo, accessed on 3 December 2021) was employed to cut off from the final tree all the branches that were drawn in less than 10% of bootstrap repetitions. Finally, the tree was re-rooted with the Ete3 python package [RAxML v.8.2 (Randomized Axelerated Maximum Likelihood)  was used package  setting  package .https://github.com/richarddurbin/phynder, accessed on 3 December 2021) was used to assign SNPs to the re-rooted tree, and pathPhynder [Phynder software . The trees were summarised using TreeAnnotator v.1.8.3 . The highest posterior densities (HPD), which collect the most likely age distributions, were calculated for each TMRCA considered, taking into account the Effective Sample Size (ESS) parameter. Coalescent time estimates were obtained by combining .log files after discarding the first 2000 generations of each replicate as burn-in, and the results were visualised in Tracer v1.7 [The effective population size analysis was estimated on consensus multi-FASTA files, produced from the combined multi-VCF dataset (assembled as described above) using a reference-free python script created for this purpose, based on scikit-allel . Molecul−10 , a relaxcer v1.7 . Bayesiacer v1.7 .N = 51), or they were too poor in quality to be placed in the phylogeny (N = 63).Out of the 832 assessed Y chromosomes, 15 fell into haplogroup C and 703 into haplogroup Q . The remHaplogroup C is a non-African haplogroup observed in Eurasia and one of the two main clades characterising Native American Y chromosomes. It has been observed at low frequencies in Athabascans and Algonquians from North America and in Indigenous groups from north-western South America ,71,72,73The phylogenetic structure of haplogroup C highlights the lack of American Hg C Y chromosomes that could be useful to further resolve the phylogeny . Out of Due to public raw data unavailability, our dataset does not include sequences belonging to C-P39, a branch that stems from C-F1699 according to Wei et al.  and PinoInterestingly, the ancient Brazilian sample CP19 fell into C-L1373 but carried ancestral alleles for C--MPB373, C-F1699, and downstream markers, including B473 that characterises the North American C-P39 branch . TherefoHaplogroup Q chromosomes are currently found in Eurasia and the Americas , where tQ-L275 characterised two Central American samples (NA56 from Panama and AMZ139 from Colombia) belonging to its sub-branch Q-L245, which was previously reported across Eurasia .Q-F1096 was represented by Q-M120 (frequent in South-East Asia) in the Peruvian HG01944, and by Q-B143 (found in Siberia and North America) in ancient samples from Alaska  and Greenland . Moreover, a 1.9 ky-old sample from Canada (I10427) was also classified as Q-F1096 but could not be better defined due to the lack of informative markers for downstream haplogroups. Interestingly, a modern sample from Alaska (Tsimshian) branches off prior to the Q-F1096 node, sharing only two (out of 30) of its distinguishing mutations. Finally, haplogroup Q-YP4010 (present across Asia), in America was only found in two ancient samples: a 1.8 ky-old from Lovelock Cave, Nevada (Lovelock4) and a 500-year-old Paleo-Aleut from the Aleutian Islands (I0719). Both were classified outside a cluster of ancient Siberians dated between 7 and 4.6 kya. The vast majority of the American samples , a 0.8 ky-old ancient sample from Chile (I1754), and two modern individuals from Peru (9597_Aymara) and Brazil . Moreover, Q-FGC12244, the other Q-YP937 sub-lineage, was only observed from Mesoamerica to the Isthmo-Colombian area. The structure of this sub-branch was noticeably improved in comparison with previous studies, with the identification of Q-BZ1700 and its sub-lineages Q-Y166140 and Q-Y166140d, which comprise almost exclusively Mexican samples. It is of note that the previously reported Q-SA02 ,78, paraOverall, although Q-Z780 was observed throughout the double continent, its frequency remains consistently inferior compared to Q-M3. In fact, Q-M848, the most represented Q-M3 sub-lineage, is by far the most frequent and widespread haplogroup throughout the Americas ,38.In this work, 18 Q-M848 sub-lineages were observed, six reported here for the first time. Most of Q-M848 sub-lineages include few samples, but some of them  appear well-structured and locally differentiated, with no major signs of propagation in neighbouring areas.Q-M925  is most The newly identified Q-BY104773 lineage  includesThe newly characterised Q-BY15730  partiallThe remaining geographically well-structured Q-M848 lineages are distributed along the Pacific Coast: the minor Q-Y780 sub-haplogroup is confined to the Andean territory, whereas Q-Z5906 and Q-Z5908 include a fairly small number of individuals  on the other side of the mountain range . For Q-Y780 a single sub-lineage, Q-Y817, was identified leaving nearly half of the Q-Y780 sequences classified as Q-Y780*, including a 5.84 ky-old Peruvian sample.In contrast, Q-Z5906 and Q-Z5908 are more frequent and more structured compared to Q-Y780 and are highly frequent in Peru. Notably, Q-CTS4000, a major sub-branch of Q-Z5906, was expanded with the identification of seven sub-lineages that comprise modern and ancient Andean samples from Ecuador to Chile and an isolated Mexican individual classified as Q-CTS4000 xB37) as previously observed in [ as previThe distribution of Q-Z5908 resembles that of Q-Z5906 and it is mainly made up of present-day Andean individuals. However, unlike Q-Z5906, Q-Z5908 includes more samples out of the mountain range and a larger number of ancient individuals. In particular, the 3.28 ky-old CUN008 from Peru defines a new sub-lineage, Q-BZ2005, upstream of the previously identified Q-Z5910 ,38. Q-BZFinally, other smaller and geographically poorly structured clusters were identified downstream of Q-M848, namely: Q-Z19432, Q-Y165186, Q-FT336377, Q-CTS2731, Q-SK1965, Q-BY65986, Q-M19, Q-Z35840, Q-SK1963, Q-Y210513, Q-MPB117, and Q-MPB096 . NotablyAges were estimated for all the main haplogroups of the phylogeny and, when the number of represented samples allowed it, for their sub-haplogroups . In addiFinally, the effective population size estimates of Q-Z780 and Q-M848 are shown in The effective population size of Q-Z780 rapidly increases between 15 and 14 kya, slowly reaching a plateau around 10 kya, until a second, slight sign of increase is observed at 4 kya. In contrast, Q-M848 shows two rapid population growths, at 12.5 kya and at 9 kya, occurring in less than one millennium and separated by a very low, but steady, three millennia expansion.From the analysis carried out in the major Q-M848 sub-clades, it appears that the rapid growths observed for Q-M848 are mainly due to the Q-M925 branch , which mFor decades, scientists have tried to shed light upon America’s first peopling and the time and modes of the subsequent dispersal events. One of the main hurdles in disentangling the genetic history of the American populations is the lack of detailed information on their gene pool. This is due to the impact of European colonisation and the subsequent slave trade. These events, associated with wars and diseases, led to a dramatic reduction of the Indigenous people and therefore of their contribution to the present-day American gene pool. This decrease was particularly accentuated for the Y chromosome, due to the uneven male/female Native population decline and the high historical rate of male-mediated admixture into Native American communities ,80,81,82In this work, Y-chromosome data from several studies were employed to construct the most up-to-date phylogeny of the Native American founding lineages. A dataset of the Y-chromosome sequences potentially ascribable to American Indigenous clades was assembled and a computational workflow was developed to insert modern LC and ancient samples into a robust reference phylogeny built only with HC sequences. This approach, based on the imputation of missing data in LC and ancient samples, allowed us to minimise the bias caused by the different methods used in the original studies ,84.The phylogeny that we obtained supports and details the structure of Native American haplogroups. We significantly increase the resolution of Q-M1107 and confirm haplogroups C-MPB373, Q-Z780, Q-M848, and Q-Y4276, together with C-P39 (not represented in our phylogeny dataset), as the main Native American founding lineages. The paragroup C-L1373*, observed only in one ancient sample, is a novel enrichment of the haplogroup C phylogeny. The phylogeography of the major branches of the above-noted haplogroups indicates that C-L1373*, C-MPB373, Q-Z780, and Q-M848 were the first to colonize the Americas, rapidly reaching the Southern Cone . HoweverFirst of all, haplogroup C, beside the North American C-P39, which entered the continent with subsequent migrations , is onlyQ-Y4276, the minor clade of haplogroup Q-M3, is mainly observed in North America and only sporadically in Siberia, Panama, and Brazil. Its age (13.9 kya) confirms an early entrance, whereas its presence in Siberia has been attributed to a recent back migration . No infoUnlike Q-Y4276, Q-Z780 and Q-M848 are observed throughout the Americas. Q-Z780 is the most ancient clade (15.1 kya) and has been found in ancient samples across the whole double continent—from Montana  to Peru, Brazil, and Argentina (9.2–8.6 kya). Yet, in modern individuals, Q-Z780 is mainly observed in Mexico as Q-Z781. In contrast, Q-M848, which is slightly younger than Q-Z780 (14.8 kya), is much more frequent and structured, including many parallel sub-clades, each with a specific geographic localisation, suggesting several early differentiation events and a rapid southward expansion.Q-M925, occupying the northernmost region, is the Q-M848 sub-clade with the most marked growth. Its distribution supports a Mexican origin as previously suggested , and a dIn addition, we describe for the first time two geographically well-structured clades, Q-BY104773 and Q-BY15730, which provide new insights into the peopling of South America. They include samples from both the Andean mountain range and the Caribbean, thus adding a missing tile to the genetic history of the subcontinent. Indeed, Q-BY104773 (14.3 kya), which is represented by modern individuals from North-West Amazonia, is associated to a population growth slowly starting after 12.5 kya and then accelerating before 5 kya, around the time when signs of intensive agriculture appeared along the Neotropics . A singlOne Q-BY104773 sub-lineage, Q-BY139813, was also observed in ancient samples (~1.3–0.5 kya) from the lower Caribbean. Interestingly, these specimens were all excavated from Ceramic-associated sites, whereas ancient samples from archaeological contexts associated to the Archaic period (prior to the Ceramic) did not belong to the Q-BY104773 clade. This distribution provides further evidence for the migration of Ceramic Age people into the Lesser Antilles and northwards, starting from ~2 kya, and points at the north-western region of South America as their place of origin, in accordance with genomic data ,43. MoreQ-BY15730, the second major sub-clade reported here for the first time, tells a story similar to that of Q-BY104773. Q-BY15730, observed in modern individuals from North-West Amazonia, was dated at 13.2 kya, slightly before the first human traces in the area, and its phylogeography draws a displacement from Ecuador/Colombia into the Amazon region following river streams. The importance of rivers in the settlement and human activities in this part of South America was previously discussed by Arias et al. , and theMoving along the Pacific Coast, three main lineages  are predominant. All three sub-clades stem directly from Q-M848 and are rather ancient, with estimated ages of 12.4 kya, 10.2 kya, and 9.4 kya, respectively. Their near complete absence outside the Andes attests the effectiveness of the mountain range in limiting the movement of people and is in agreement with genomic data reporting a clear separation between Andean and Amazonian populations ,51,54,90The remaining Q-M848 chromosomes either belong to small clusters or are ancestral for the markers defining the sub-lineages noted above. Although it is difficult to draw conclusions on the origin or dispersal of the minor sub-lineages, at the same time the observation of still undefined Q-M848 individuals hints at the existence of different, yet-to-be-identified Y-chromosome clades.These results can be explained by the arrival of Q-Z780 and Q-M848 in Mexico prior to 15 kya, where they began differentiating while moving further South. This is in accordance with the scenario originally proposed by Sandoval et al.  and lateAs for the migration routes, the one along the Pacific Coast is evident in the distribution of the major Andean Q-Z5906 and Q-Z5908 lineages, whereas the newly reported Q-BY104773 and Q-BY15730 mark the entrance into the continental interior. The limited number of individuals sampled from the remaining regions of South America prevents us from fully comprehending times and modes of colonisation at the continental level. To interpret the high level of variability already visible in the few individuals collected from those areas would require further sampling along the Atlantic Coast as well as across the central part of South America.Our analysis confirms haplogroups C-MPB373, C-P39, Q-Z780, Q-M848, and Q-Y4276 as the main Native American founding lineages. Moreover, the novel integration of modern and ancient samples allowed us to trace unsuccessful or extinct Y-chromosome lineages, revealing a broader haplogroup diversity among the human groups that initially entered the Americas that cannot be retrieved using present-day DNA data alone. Namely, C-L1373* was represented by the Brazilian 9.85 ky-old CP19, Q-YP4010* was found in the 1.8 ky-old Lovelock4 from Nevada, and pre-Q-F1096 was observed in the Tsimshian sample from South Alaska.Q-Z780 and Q-M848, which include the majority of the American Indigenous Y chromosomes, were most likely carried by the first settlers of the continent, together with C-L1373. The distribution and age estimates of Q-Z780 and Q-M848 sub-lineages suggest that they reached Mexico before 15 kya. From there, whereas some Pleistocene populations started settling in the area giving rise to large local clusters , others proceeded southward, settling as small groups along the way, differentiating into local clades, like the Isthmian Q-SA02 and Q-Y12421, and the Andean Q-Z5906 and Q-Z5908. The last two, as well as Q-Z780, best delineate the Pacific coastal route, where consistently favourable conditions allowed a rapid spread.Thanks to the identification of the two novel Q-M848 sub-branches, Q-BY104773 and Q-BY15730, we provide the first Y-chromosome evidence of South American inland peopling. In particular, Q-BY15730 (13.2 kya) marks an early migration from Ecuador/Colombia into the Amazonian area, in accordance with traces of human settlement in the region dating back to 12 kya. Similarly, Q-BY104773 (14.3 kya) probably reached the interior of Colombia from the Pacific Coast and differentiated in North-West Amazonia. Moreover, the Q-BY104773 sub-lineage Q-BY139813 provides further evidence for the migration of Ceramic Age groups from the northern region of South America into the Lesser Antilles.All the Q-M848 sub-clades noted above underwent local population expansions once adapted to specific environmental niches, concomitantly with the improvement of climatic and subsistence conditions.In conclusion, this work assembles the most comprehensive Native American Y-chromosome dataset to date and greatly improves the phylogenetic resolution of haplogroup Q. Furthermore, it helps to shed light on some aspects of the peopling of the double continent, especially in South America, and emphasises that additional sampling in areas that are still underrepresented, both for modern and ancient individuals, is necessary to fully reconstruct the genetic history and demography of Indigenous Americans.


Ursus maritimus) sampled across two decades (1995–2016) from the Svalbard Archipelago, Norway, an area that is affected by rapid sea ice loss in the Arctic Barents Sea. We analysed genetic variation at 22 microsatellite loci for 626 polar bears from four sampling areas within the archipelago. Our results revealed a 3–10% loss of genetic diversity across the study period, accompanied by a near 200% increase in genetic differentiation across regions. These effects may best be explained by a decrease in gene flow caused by habitat fragmentation owing to the loss of sea ice coverage, resulting in increased inbreeding of local polar bears within the focal sampling areas in the Svalbard Archipelago. This study illustrates the importance of genetic monitoring for developing adaptive management strategies for polar bears and other ice-dependent species.Loss of Arctic sea ice owing to climate change is predicted to reduce both genetic diversity and gene flow in ice-dependent species, with potentially negative consequences for their long-term viability. Here, we tested for the population-genetic impacts of reduced sea ice cover on the polar bear ( LossOur analysis focused on the polar bears inhabiting the Svalbard Archipelago in the northwest Barents Sea, because there is long-term genetic monitoring data available for this subpopulation going back until 1995. Moreover, the reduction of sea ice cover for the area inhabited by the Barents Sea polar bear subpopulation is an ongoing process. This includes both an estimated increase of ice-free days by 41 per decade between 1979 and 2014 . CoincidMost polar bears in the Barents Sea subpopulation, which was estimated to be approximately 2650  1900–3600) individuals in August 2004 , hunt onThe Svalbard Archipelago has undergone over two decades of rapid sea ice loss . To asse. 2 (a)n = 123), northeastern Svalbard , southwestern Svalbard  and southeastern Svalbard  . For each of the four areas, we divided the 22-year sampling period into five temporal groups: T1: 1995–1999; T2: 2000–2004; T3: 2005–2009; T4: 2010–2014; T5: 2015–2016, but excluded periods with fewer than 10 individuals . Thus, our final dataset comprised 16 spatio-temporal groups and 622 individuals, although to explore our results we also evaluated other plausible temporal divisions .Polar bears from the Barents Sea were captured throughout the Svalbard Archipelago from 1995 to 2016 by the Norwegian Polar Institute, Tromsø, Norway, following standard immobilization, sampling and handling procedures  figure . We extr (b)NA; observed heterozygosity, HO; unbiased expected heterozygosity, HE; allelic richness, RA; and private allelic richness, PA), inbreeding  and relatedness . Moreover, to allow tests of a potential negative association between habitat fragmentation owing to loss of sea ice cover and genetic exchange among Svalbard polar bears, we estimated pairwise genetic differentiation indices  per sampling site and period .To facilitate tests of whether population-genetic changes in polar bears coincide with declining sea ice coverage in the Svalbard Archipelago, we estimated several standard parameters of genetic diversity HO, HE, RA and wr) and for pairwise genetic differentiation STG′′ using the R package glmmTMB v. 1.0.2.1 [R2 = 0.916, p = 0.0106), and their interaction as explanatory variables. Additionally, we used the four sampling areas , we selected the optimal model based on Akaike's information criterion corrected for small sample size (AICC) and Akaike weights (iω) [To test the prediction that genetic variation and exchange in polar bears decreased along with declining sea ice coverage in the Svalbard Archipelago, we built linear mixed models for each genetic diversity parameter ( 1.0.2.1 . This wahts (ωi)  (electro (d)K) of polar bears along with declining sea ice cover in the Svalbard Archipelago, we ran Structure v. 2.3.4 [ocpriorL model, which uses additional sample-specific data, such as sampling location or time, to increase the power to detect subtle genetic changes and genetic structure. We also included runs accounting for kin structure following the recommendations by Waples & Anderson [et al. [K .To track potential changes in the composition and number of genetic subpopulations or clusters (v. 2.3.4  for (i) Anderson . This wa [et al.  to infer (e)NA, OH and EH) and global genetic differentiation . Under fixed parameters, we compared six alternative scenarios: (1) no effect of sea ice loss on population connectivity, (2) a moderate effect of loss in sea ice on population connectivity, (3) pronounced effect of loss in sea ice on population connectivity, (4) pronounced effect of sea ice loss on population connectivity  and size, (5) pronounced effect of sea ice loss on population connectivity  and size, and (6) empirical genotypic data from the four capture areas at the last sampling period (T4 or T5)  in the . 0.29.0  C invariably identified time (i.e. sampling period) as the main predictor variable needed to explain the observed variation in genetic diversity parameters. Over time, RA decreased by −0.145 (97.5% CI: −0.211 to −0.079) and EH by −0.041  per 5-year period, corresponding to approximately 3% and 9% loss through the whole study period, respectively. Furthermore, wr increased by 0.030 (97.5% CI: 0.014–0.044) per 5-year period . Still, they provided evidence of the above-described temporal changes, while simultaneously pinpointing some potential deviations from the straight-line relationship of the model preferred by AICC.Although less supported by AICC but provided additional predictive power by identifying significantly elevated genetic differentiation among the polar bears in the northern areas of Svalbard (p = 0.022 (increase in differentiation in 11 of 13 time steps).Our results are also consistent with the predicted decline in genetic exchange locally following loss of sea ice coverage. Notably, pairwise and NES) . A similSvalbard . A sign  (b)tructure results consistently identified K = 3 genetic clusters as the most likely model of spatial population structure , suggesting a split among polar bear bears from NWS, SWS and eastern Svalbard (NES and SES) , Structure runs within each sampling period showed a pattern of increasing genetic heterogeneity over time .Our results provided evidence of increasing genetic structure coinciding with the loss of sea ice habitat. On average, across the whole dataset, the Sand SES) . HoweverK = 2 showed a strong split between NWS and the rest of the sampling areas. At K = 4, a fourth cluster appeared in southern Svalbard (SWS and SES), which was dominated by polar bears in SES and varied across periods. Likewise, a fifth cluster emerged at K = 5 in southern Svalbard, that was dominated by polar bears in SWS and was also temporally variable. When we accounted for kin structure by randomly purging closely related individuals, we obtained a similar clustering pattern (results not shown). As expected, Structure runs without ocpriorL gave fairly flat distributions of membership probabilities of individuals, which were resolved when using the ocpriorL model (rlocprior = 2.99).In alternative models of spatial genetic structure , K = 2 s (c)Our forward-in-time simulations indicated that the observed genetic erosion is a trend that will probably continue for future generations of the Barents Sea subpopulation. Scenarios 1 and 2 with no loss in sea ice and functional connectivity between populations was, as expected, the best for preserving genetic diversity for the next 100 generations . By contM-ratio tests, regardless of the parameter combination used (p > 0.05). The M-ratio values ranged from 0.726 to 0.891 across sampling areas per period , while M-ratio values of less than 0.7 are generally considered as indicative of a bottleneck. The coalescent analysis indicated a rather constant historical effective population size, corroborating the findings of the heterozygosity-excess and M-ratio tests .There was no support for recent genetic bottlenecks based on heterozygosity-excess or . 4 (a)The disappearance of Arctic sea ice is accelerating and rapidly changing the habitat configuration and dispersal patterns of ice-dependent species, which, in turn, escalates conservation challenges in populations of threatened species across the Arctic ,45. The  (b)For polar bears, sea ice is the intervening habitat matrix that either promotes or restricts functional connectivity among habitats and subpopulations ,50. In tOur null hypothesis of panmixia of polar bears across the Svalbard Archipelago was rejected owing to major temporal changes in genetic composition during the course of the focal time period. Initially, the four assigned sampling areas showed a high degree of admixture, which decreased over time and covaried with documented reductions in sea ice coverage . At the With our extensive spatio-temporal sampling regime, we were able to disentangle the effects of sea ice loss and behavioural traits. For instance, we determined that genetic drift was more intense in NWS, which is the area with the highest loss of sea ice and degree of philopatric bears. By contrast, the southern areas with relatively lower levels of sea ice loss showed relatively weaker signals of genetic differentiation, even though there was evidence of localized genetic groups indicative of site fidelity and/or philopatry. In turn, the local loss of genetic diversity may be best explained by a decrease in gene flow resulting in increased inbreeding of local polar bears within the focal sampling areas. In addition, restricted movements across relatively small geographical areas may also reflect sea ice affecting prey distribution ,65.Indeed, we found that the levels of admixture between individuals from the genetically differentiated sampling areas decreased over time. The observed temporal decline in admixture was especially pronounced between the northern and southern range extremes characterized by different distinct climatic regimes in the Barents Sea . Accordi (c)et al. [Our study shows that tracking of temporal population-genetic changes will become important for our understanding of how polar bears and other ice-dependent species may cope with rapid habitat fragmentation and loss in the Arctic and Antarctic. For the Svalbard Archipelago, the high degree of philopatry of ‘local’ female polar bears over generations ,63 in coet al. , our stuClick here for additional data file.


Solanum melongena L.), two other underutilized eggplant species, the African eggplant (S. macrocarpon L.) and the scarlet eggplant (S. aethiopicum L.), were also cultivated with local importance where the leaves and fruits are used for food and medicinal purposes. The major objectives of the eggplant breeding program are to improve fruit quality, increase yield performance through heterosis breeding, and introduce pest and disease resistances from wild relatives. Europe and Asia hold a wide collection of germplasm resources with significant potential for genetic improvement. While cultivated eggplant is susceptible to several fungi and bacteria, many wild relatives offer potential resistance to these pathogens. In this paper, we review the genetic resources and diversity of cultivated eggplant and its wild relatives. As a point of departure, we examine the economic importance, domestication, taxonomy characterization, and relationships of the crop and its wild relatives. The importance of evaluating and safeguarding wild relatives is highlighted, as crop wild relatives are highly underrepresented. A key section in this study is an overview dedicated to genetic resources, resistance to biotic and abiotic stresses, pre-breeding, and breeding for sustainable eggplant production.Eggplant is the fifth economically most important vegetable in the Solanaceae family after tomato, potato, chili, and tobacco. Apart from the well-cultivated brinjal or aubergine eggplant ( Solanum melongena L.), also known as brinjal in Southern Asia and aubergine in France and Britain, is the fifth economically most important vegetable in the Solanaceae family after tomato (Solanum lycopersicum L.), potato (Solanum tuberosum L.), chili (Capsicum annuum L.), and tobacco (Nicotiana tabacum L.). The fruit is prevalent in many essential diets across several local communities, especially in Africa, the subtropics , Middle East, and Southeast Asia [S. dulcamara L. (the nightshades). Eggplant is considered an Old World crop that was domesticated in Africa, Asia, and Europe. In contrast, its relatives, such as tomato and potato, are New World crops with evidence of origination in South America [S. melongena); However, two other Solanum species relative to the Asian eggplant viz; the Gboma/African eggplant (S. macrocarpon L.) and the Ethiopian/scarlet eggplant (S. aethiopicum L.) are partly discussed. The S. macrocarpon and S. aethiopicum are minor crops with local importance where the leaves and fruits are used for food and medicinal purposes. The similarities between these three species of eggplant have previously confused the taxonomic classification. However, they are comparatively far related within the genus [Eggplant  has the highest concentration of flavonoids such as quercetin and rutin [Eggplant is the best source of phenolic content, with the highest total among the Solanaceae family . Severalandraces ,16. Hencandraces  reportedandraces . Generalandraces  reportednd rutin . This, hnd rutin . Knowlednd rutin . Anthocynd rutin . Raigón nd rutin  reportednd rutin  observedAnother active compound found in eggplant is carotenoids, although the amount in eggplant is less compared to other vegetables such as tomato and carrot. Carotenoids are ‘lipid-loving’ molecules that serve as accessory pigments in photosynthesis and protect the photosynthetic mechanism. The health benefits of these pigments have made them prevalent in dietary supplements and they are used as colorants in the food industry. Zeaxanthin and lutein have shown positive effects on age-related issues such as cataracts and macular degeneration . The carTrypanosoma cruzi, Leishmania amazonensis, and Leishmania mexicana [Glycoalkaloids are nitrogen-containing steroidal glycosides found in eggplants and other Solanum members, including tomato and potato. Researchers showed glycoalkaloids play active roles in plants resistance against pathogens and pests ). α-solamexicana . Though mexicana . TherefoReactive oxygen species (ROS) are dangerous entities produced by multiple cellular processes, which can be overproduced in reaction to different stimuli. The main source of ROS is incompletely processed electrons or oxygen produced by the electron transport chain (ETC) in the mitochondria . Normal S. melongena and the two other cultivated species and serve as sources of variations in breeding for adaptation to climate change and pest and disease resistance [S. melongena, together with nine wild species, form the “eggplant complex”, which includes the cultivated brinjal eggplant and its closest wild relatives [S. sisymbriifolium Lam, S. elaeagnifolium Cav., and S. torvum Sw.,) used in breeding programs for their resistance characters which require specific cross-breeding techniques to succeed [S. melongena. The success of the crosses, fertility, and viability of wild eggplants with cultivated types may be reduced. For instance, the interspecific hybridization derived is partly sterile due to reproductive obstacles such as S. tomentosum L., S. linnaeanum Hepper, and S. dasyphyllum, [Many wild species of eggplant are related to sistance . Vorontssistance  reportedsistance . Most ofsistance  are preselatives . The genelatives . The ter succeed ,33. The phyllum, .S. insanum, the wild progenitor for S. melongena and their closest African wild relative S. incanum, were recently clarified [S. dasyphyllum and S. anguivi were confirmed as the wild progenitors of S. macrocarpon and S. aethiopicum, respectively. The three cultivated eggplants had a common and complex domestication event, as well as morphological changes associated with their domestication [S. melongena than the other two cultivated eggplants [S. melongena has been shown to affect both gene expression and genetic architecture [S. macrocarpon, S. aethiopicum, and S. melongena genomes should bring further insights into the similarities and differences between the three cultivated eggplants.Phylogenic relationships between larified . S. dasytication . Similarggplants . At the itecture . Hence, S. macrocarpon and S. aethiopicum are the most common and popular eggplants native to Africa, especially in Central and West Africa. However, the production of these crops remains relatively low, with limited information on the cultivated area and yield performance. West Africa is the center of diversity for these eggplants. Generally, eggplants are grown in Africa, mainly in small fields near villages and backyard gardens [S. macrocarpon is widely cultivated in tropical America and Asia, while S. aethiopicum is popularly grown in South America.The cultivation of small-fruited eggplant in China dates back to the 4th century, while evidence of cultivation in Africa indicated it began in the 9th century . Though  gardens . S. macrS. aethiopicum is a leafy and fruity vegetable that can be cooked or consumed raw. Its leaves can be consumed in the same way as spinach [The  spinach . It is aS. macrocarpon is widely cultivated for its glabrous and large leaves as a green vegetable. The fruits are large, with clasping calyx ranging from 2 to 6 cm in length and 3 to 10 cm in width. They are green or green-white, cream, white, and sub-spherical. The fruits are sweeter compared to S. aethiopicum and are more preferred. At the full maturity stage, the fruits turn brown, orange, or yellow with a ruptured surface [The  surface .S. melongena is characterized as a tall plant with spiny, large leaves. The flower is andromonoecy and in clusters. Furthermore, the fruit is bitter, green, and small in size with hard flesh and thick skin. The fruit color varies from dark to light purple, with some sub-species being white, green, or almost black. Its size ranges between 4 to 45 cm in length and 2 to 35 cm in width at different weights and shapes ranging between 15 g to 1.5 kg. The fruits are in clusters or single fruit set with up to 5 fruits per cluster. At full physiological maturity, the fruits become yellow, red, or brown [or brown . ExtensiS. melongena was recorded in India, with over 5000 of the total record of 21,000 globally  database. Gangopadhyay et al. [S. aethiopicum and 561 of S. melongena; The Vavilov Research Institute of Plant Industry, St. Petersburg, Russia recorded 238 accessions of S. melongena [S. melongena. There are some reports of collected germplasm resources in the Middle East [S. melongena, S. aethiopicum, and S. macrocarpon was compiled by the Eggplant Genetic Resources Network (EGGNET project) in Europe project, a network of private and public sector researchers from the UK, Germany, Greece, Spain, Italy, France, and the Netherlands. This database is currently managed by the European Cooperative Programme for Plant Genetic Resources (ECPGR), Nijmegen, Netherlands. Castañeda-Álvarez et al. [Eggplant’s genetic resources have been collected systematically in some Asian and European countries. The Global Biodiversity Information Facility (GBIF) has recorded over 1.5 million occurrences of Solanum which could be biodiversity records, herbarium samples, or natural populations . The larglobally . Other pectively . The Glo GENESYS . The Asiy et al.  reportedelongena ; and thedle East , Africa dle East , and Inddle East . A more z et al.  reportedS. melongena germplasm collections existed in public institutions. Private breeders worked mostly with local material for their national market. The International Board for Plant Genetic Resources’ inputs was created in 1971  and worldwide national initiatives. Many public collections have been progressively assembled for saving local material endangered by the intensification of horticulture and research purposes. According to online databases, S. melongena germplasm is relatively well-represented in genebanks worldwide. For the two indigenous African eggplants, S. aethiopicum and S. macrocarpon, efforts have been developed to protect their diversity and complete ex situ collections. Wild Asian species have been partly collected in the last decade via national and collaborative Asian projects, but apart from the World Vegetable Center (formerly AVRDC), little is known about these collections [In the 1970s, very few lections .Bi-national projects (France and the UK in the 1990s) and the EU ESIN project (1993–1994) were set up as the first collaborations among experts of complementary disciplines ranging from botany and taxonomy to germplasm collections and genetics. A few years later, and within the framework of French and Dutch national agreements, vegetable breeding companies were connected to eggplant management and related species germplasm held by public institutions . These cFirst, the large number of species related to cultivated eggplants is both an outstanding reserve of genes for breeders and a burden for germplasm holders, the supervision of which requires close collaboration with taxonomists . Living Furthermore, access to wide germplasm resources is necessary to optimize the use of the powerful tools created by fast-evolving genomics and bioinformatics. Quantification and structuration of genetic and phenotypic diversity, limited for decades to a handful of species and accessions, is now accessible at whole collection and genome scales, as ambitioned by the EU G2PSol project (2016–2021). Hence, joint efforts among genebanks within and outside Europe are more imperative than ever to identify the strengths and weaknesses of the different collections and increase accessions for research and breeding.S. melongena and related species has been limited so far to a narrow range of accessions and traits and is clearly a bottleneck on future research efforts. Increased knowledge of germplasm-wide diversity is indispensable, particularly for resistance or resilience to biotic and abiotic stresses that are expected to increase in our changing climate. Phenotyping methods must also gain precision by intimate dissection of complex traits to identify their key regulatory genes and QTL networks.Lastly, the exploration of phenotypic diversity for traits of interest within S. macrocarpon, S. aethiopicum, and S. melongena have been developed by the International Board for Plant Genetic Resources [The morpho-physiological evaluation and characterization of available germplasm for targeted traits are major factors in the eggplant breeding program. These evaluations are essential for the sustainability and management of genetic resources. The major characterization involves measuring the plant traits that can be observed through simple visual observation at different growth stages such as the germination and seedling phase, vegetative stage, inflorescence descriptors, and maturity stage. Secondary morphological characterization deals with further complicated agronomic important traits such as biochemical properties, yield potential, fruit set, and pest and disease resistance . These mesources  and the esources , which iesources  revealinesources . The mosesources .S. melongena, and two other Solanum species viz; African eggplants S. macrocarpon L. and the Ethiopian eggplant S. aethiopicum L. using 16 morphological traits. The results show that considerable diversity exists both between and within species. Based on cluster analysis, the accessions were clustered into three unrelated groups to the taxonomy classification of an accession . This, however, revealed that morphological characterization is not a good predictor in genetic diversity assessment. Hence, the author suggests the use of molecular data as a better alternative for categorizing germplasm collections. Random amplified polymorphic DNA (RAPD) and allozyme data suggest that despite the fact that S. incanum (cultivated eggplant wild progenitor), S. insanum (weedy types), and S. melongena are morphologically different, these species are related at the genetic level [S. melongena and related species using restriction fragment length polymorphism (RFLP) revealed that taxonomic relationships based on morphological characters are, to a certain extent, unreliable [The molecular diversity of wild and cultivated eggplant has been evaluated by several researchers to determine the genetic relationship for germplasm conservation and serve as a guide in the breeding program towards the development of superior lines. Different molecular markers have been used, including sequence-related amplified polymorphism (SRAP), inter-simple sequence repeat (ISSR), random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR), allozymes, amplified fragment length polymorphism (AFLP), and chloroplast DNA markers. Several research studies have emphasized the superiority of molecular markers over morphological characterization in assessing the relatedness and diversity among eggplant species. Polignano et al.  evaluateic level . While treliable . Howeverreliable ,55.S. melongena using microsatellite markers [S. melongena and among related species using genic SSRs [S. melongena using morphological traits and polymorphic SSR markers. It was uncovered that higher genotypic and phenotypic diversity was observed among landraces as compared to commercial cultivars. Additionally, the hybrid’s accessions were observed to share a very limited gene pool. Hence, through breeding and efforts, domestication served as a diversity constraint within eggplants, while the non-hybrid and landrace varieties are still potentially valuable sources of heterozygosity for modern improved cultivars.Furthermore, both molecular and morphological data were significantly useful in accurately classifying earlier mis-named and unnamed lines . Hence,  markers , signifinic SSRs , RAPD [5nic SSRs , SRAP [5nic SSRs , and SSRnic SSRs . Tiwari nic SSRs . Muñoz-Fnic SSRs  assessedS. lycopersicum (tomato) as an essential model in early classical mapping, whereas S. melongena was ignored in this regard. Aside from anthocyanin accumulation, there is a dearth of phenotypic trait mapping in eggplant. Like other crop species, the quantitative nature of important agronomic traits made it difficult to conduct studies on inheritance in eggplant. Upon introducing molecular linkage maps and the accompanying breakthrough in comparative genomics, hitherto concentrated efforts on tomatoes have broadened to include eggplant, pepper, and potato. The genome-wide characterization of eggplant via molecular mapping is vital to breeding efforts on several levels. With this, it became easier to analyze the inheritance of complex traits and cull undesirable genotypes from breeding populations via marker-assisted selection. In contrast, the screening of germplasm for important traits was achieved with relative efficiency [Research efforts were concentrated on ficiency .1 hybrids. Despite this, eggplant breeding is limited by the laborious process of producing hybrid seeds. The process of manual emasculation and pollination of the inbred parents is time consuming and uneconomical. As a result, attempts are ongoing to incorporate cytoplasmic male sterility (CMS) into breeding lines of eggplants. Apart from heterosis breeding, the grafting of S. melongena onto tomato (S. lycopersicum and S. hirsutum) and other related species such as S. macrocarpon, S. torvum, S. incanum, and S. aethiopicum led to a massive improvement in eggplant production [The primary aim of eggplant breeders was to increase yield and improve harvest quality by incorporating disease and pest resistance into the crop. Another important objective of the eggplant breeding program was to increase its tolerance to abiotic stress. With the advent of heterosis in brinjal, there have been concerted efforts to develop hybrids with improved productivity from inbred lines. Therefore, the majority of the commercial varieties are Foduction . Furtheroduction . Variousoduction . Similaroduction . Differeviz., genetic transformation, protoplast fusion, somatic hybridization, and haploidization, have been successfully exploited in wild and cultivated species with numerous success stories  raised concern in relation to the possible toxicity that may result from the use of this species in S. melongena breeding activities [Recently, more than 14 phenolic compounds, an essential group of antioxidants, have been found in eggplant accessions . Studiesarieties . Howevertivities ,88. It iIn the past 50 years, eggplant breeding has turned from an exclusive field activity on the improvement of a few traits to a collective and highly technical process. From hundreds of progenies screened mostly for quantitative and qualitative yield, eggplant breeders now work on thousands of plants, with many varietal types grown year-round in several countries. Breeders have taken advantage of the synteny between eggplant, pepper, and tomato genomes to efficiently and effectively improve their breeding programs. However, there is more scope for future work in eggplants. Eggplant’s transcriptome sequencing is still in its initial phase, this will facilitate comparisons with other relative’s genome sequences, thus intensifying genetic information. With over a thousand markers developed in eggplant, it would be easier to explore genome regions and gene features that control quantitative and qualitative traits of interest. Focus on the improvement of nutritional composition, such as phenolic and anthocyanin, should be included as breeding objectives. In addition, the characterization of antioxidant activities among landraces, cultivars, and wild species will aid in the selection of the best germplasm for this important trait. As the nutritional benefits of eggplant have become widely recognized and higher yield cultivars are developed through genetic improvement, this crop will become a globally important vegetable crop. The food security of several nations depends on crops produced from genetic resources from other regions of the world; thus, plant genetic resources call for global attention due to their mutually beneficial role. Information on the characteristics and extent of genetic diversity within the crop species is important for a successful and efficient breeding program. It plays a major role in characterizing individual accessions and also as a guide in selecting parents for hybridizations. There is a vast range of genetic diversity in wild relatives compared to their cultivated counterparts, and this could be a useful source for resistance to biotic and abiotic stresses. At present, only a few wild relatives have been utilized in eggplant breeding, and the introgression of wild relatives to improve commercial varieties is yet to become popular. To achieve remarkable success in eggplant cultivation, efforts should be intensified on screening for abiotic and biotic stresses in wild relatives and the collection of important germplasm accessions for the production of superior eggplant varieties. This information, alongside genomics studies on the identification of genes and QTLs of agronomic importance and their associated markers, will go a long way to improve eggplant production. Apart from using modern technologies, the conventional breeding technique remains an important method of developing a new variety where wild relatives/ species are utilized. Meanwhile, somatic hybridization has been widely studied in eggplants as a means of overcoming limitations due to cross incompatibility where important agronomic traits from wild species are introgressed to the cultivated ones. Additionally, new opportunities are available to enrich the existing genetic pools by increasing cytoplasmic and nuclear variability due to the production of somatic hybrids. The assessment of genetic resources in brinjal, mainly based on phenotype, indicate several useful features in its wild relatives, however, there is a dearth of molecular markers for their characterization. These studies will contribute to the available genetic linkage map by accelerating the isolation and identification of genes and markers involved in resistance to pests and diseases that are useful for marker-assisted breeding. Further studies on genetic engineering in eggplant are required for biotic and abiotic resistance encoding genes. So far, only parthenocarpy and Bt endotoxin genes have been introduced successfully in eggplants. The cost of production may be reduced with the development of Bt eggplants along with minimal environmental and health effects. In the future, it is important to focus on the improvement of nutritional quality and the productivity of specific secondary metabolites. Although significant developments have been achieved through biotechnology, the improvement has not been exploited to its full potential. Hence, the information provided in this review will be of great importance for the utilization of eggplant wild relatives and the management of genetic resources in germplasm collections.


Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium. This study analyses temporal and genomic trends of additive genetic variance in different stages of a breeding programme. Genetic variance is one of the critical parameters in a breeding programme because it determines the potential for selection Lush . EstimatThe estimation of genetic variance in breeding programmes has a long history and a recent revival with the advent of genomic information. Historically, the genetic variance was estimated with an analysis of variance (ANOVA) method in optimised experimental designs, ranging from simple parent-offspring or sib groups to North Carolina and diallel designs  build and validate a flexible framework based on the work of Sorensen et al. , LehermeIn this section, we present study material and methods in seven parts: (1) simulation of a breeding programme where we generate true genetic values and corresponding variances, and simulated phenotype and marker genotype data, (2) theory for the temporal and genomic analysis of genetic variance assuming we know QTL genotypes and their effects, (3) statistical analysis where we describe marker-based model fitted to the simulated phenotype and marker genotype data, (4) statistical and computational approaches to estimate marker effects, genetic values and variances, (5) validation of the framework with different genetic architectures of a simulated trait, (6) summarising the results and (7) software implementation.1 that had their genome doubled and planted in headrows . In the headrows, we visually evaluated the lines (trait heritability of 0.1) and advanced the best 500 into a preliminary yield trial. In the preliminary yield trial, we evaluated the lines in an unreplicated trial (trait heritability of 0.2) and advanced the best 50 into an advanced yield trial. In the advanced yield trial, we evaluated the lines in a small multi-location replicated trial (trait heritability of 0.5) and advanced the best 10 into an elite yield trial. In the elite yield trial, we evaluated the lines for two consecutive years in a large multi-location replicated trial (trait heritability of 0.67) and released one variety. We used the best lines from the advanced and elite yield trials as parents to start a new breeding cycle. During the breeding programme simulation process, we generated fully inbred individuals (except in the F1) and, as a consequence, we assumed only additive genetic variation because dominance variance is not visible in inbred individuals, while epistasis variance is generally small  and corresponding genotypes at 10,500 markers.Here we describe a theoretical approach to temporal and genomic analysis of genetic variation, assuming we know the QTL genotypes and their effects. In the following sub-sections, we present a framework for temporal and genomic analysis of genetic variation that closely matches this theoretical approach; however, it uses observed phenotypes and marker genotypes to analyse the genetic variation. The theoretical approach consists of four steps. First, we define whole-genome genetic values from QTL genotypes and their effects, and then, we partition individuals and their genetic values by time to calculate genetic variances over these time partitions for temporal analysis. Finally, we partition whole-genome genetic values into chromosome and locus genetic values to calculate genetic variances and covariances over these genomic partitions for genomic analysis. This calculation involves three ‘layers’ of variances: (a) total (whole-genome) genetic variance, (b) chromosome variances alongside linkage-disequilibrium covariances between chromosomes and (c) locus genic variances alongside locus linkage-disequilibrium covariances within chromosomes and locus linkage-disequilibrium covariances between chromosomes. Fourth, we combine temporal and genomic analyses.Q be ni × nq matrix of QTL genotypes for ni individuals at nq QTL, with Q denoting QTL genotype of individual i at locus l. Also, let α be nq × 1 vector of QTL additive effects, with α[l] denoting QTL additive effect at locus l. Whole-genome genetic values of ni individuals are a linear combination of QTL genotypes and their effects, a = Qα, with a[i] denoting genetic value of individual i and a[i : j] denoting genetic values of a set of individuals spanning from the ith individual to the jth individual, inclusive, in the a. Variance of these genetic values is genetic variance, ni individuals and might not be an informative measure if these individuals span multiple stages and years of a breeding programme. In fact, directional selection or population structure will likely inflate this variance measure and mislead breeders in overestimating the amount of genetic variance. This is why we need temporal analysis of genetic variance.First, let a[(k + 1): l], a[(l + 1): m], Second, for the temporal analysis of genetic variance we partition the vector of genetic values by time and calculate variance for each time partition  = Var(x) + Var(y) + 2Cov and the variance product rule Var(xa) = Var(x)a2. Partitioning of the genetic variance nc chromosome variances pl being allele frequency. Instead, we advocate to calculate empirical variance in observed locus genotypes, Third, for genomic analysis of genetic variance we initially partition whole-genome genetic values entclass1pt{minimaentclass1pt{minimaces Fig. . Locus gces Fig.  , nc = 21 chromosome within-chromosome linkage-disequilibrium covariances), and nc × nc − nc = 420 chromosome between-chromosome linkage-disequilibrium covariances). In this study we work only with the genome and chromosome level partitioning of genetic variance. For a genome region level partitioning, see Burch et al.  The model (Eq. ) has locVanRaden  is scaleVanRaden . The scamentclass2pt{minimb and marker effects m by solving Henderson’s mixed model equations:We used the empirical and full Bayesian approach to estimate the model’s parameters (Eq. ) with maSpecifically, the solution of Eq.  is the centclass1pt{minimarior Eq.  and Leherior Eq. , we can entclass1pt{minimarior Eq. ), we canons (Eq. )  for each year for the temporal analysis and further partitioned along the genome for the genomic analysis. This procedure gave us posterior distribution for the genetic variance of each group, time and genome partition.Once we obtained samples of marker effects from Eq. , we haveWhen variances are unknown, we can use the empirical Bayesian approach (Sorensen and Gianola We fitted the model (Eq. ) both wiT is an ni × np score matrix obtained from a truncated SVD of genotypes with the np leading principal components such that s is an np × 1 vector of np principal component effects and S matrix is a diagonal matrix with np singular values , the columns of U are left singular vectors, and the columns of V are right singular values. This model is structurally the same as the model . In our simulation these numbers of principal components respectively explained 14%, 38%, 52%, 84%, 94%, 99% and 100% of marker genotype variation in the first replicate.Since genomic analyses can be time-consuming, we have also investigated the use of approximation for marker genotypes with their leading principal components. We changed the model (Eq. ) into:5\entclass1pt{minimaTo test our framework’s sensitivity to different genetic architectures, we have done additional simulations by varying the number of QTL and by adding genotype-by-environment interactions. Namely, the framework will depend on the ability of the fitted statistical model to capture the genetic complexity of the analysed trait. We have simulated an additive trait with either 10, 100 or 1000 QTL per chromosome, respectively with 210, 2100 or 21000 QTL per genome. In addition, we have added variation in QTL effects across years for genotype-by-environment interactions across years, assuming that years represent different environments. The amount of this additional phenotype variance due to genotype-by-year interactions was set to 0.2. In total, this gave us six scenarios (three for the number of QTL and two for absence/presence of genotype-by-year interactions). In each of the scenarios, we used the standard model    temporal analysis, (2) genomic analysis, (3) computational analysis and (4) sensitivity to genetic architecture.1 progeny across multiple crosses was lower than in the parents as this variance indicates variance in parent averages between crosses. When we generated doubled haploids for these full-sib families (HDRW stage), genetic variation was regenerated to the level in parents due to recombination and complete inbreeding. Genetic variation gradually reduced through the breeding cycle due to the repeated selection from headrows to elite yield trial. This change was particularly evident for genetic variance but less for genic variance. Also, the genetic variance was consistently smaller than the genic variance. The estimated genetic and genic variance matched the true values well across all breeding stages. There was more uncertainty in the estimates of genetic variance in the elite yield trial than in other stages.The genetic and genic variance changed through the breeding cycle. We show this in Fig. Genetic variation decreased over the years and genetic variance was consistently smaller and more variable than genic variance across years. We show this in Fig. The genomic analysis enabled accurate partitioning of whole-genome genetic variance into whole-genome genic variance and whole-genome linkage-disequilibrium covariances. We show this in Fig. The genomic analysis also enabled accurate partitioning of whole-genome genetic variance by chromosomes. We show this in the Supplementary Fig. Full and empirical Bayesian approaches had similar posterior mean estimates of variances, but the empirical Bayesian approach had smaller posterior standard deviation. We show this in Fig. Additional evaluation with multiple replicates showed that the full and empirical Bayesian results were consistently estimated for genetic and genic variance. We show this in Table 1 progeny, followed by the elite yield trial, while the largest estimation error was in the parents.When we used a sufficient number of principal components, approximation with leading principal components accurately estimated genetic variance, but this was never the case for genic variance. We show this in Fig. Our framework relies on a statistical model that can capture the genetic complexity of an analysed trait. We have tested the effect of using a sub-optimal statistical model by varying the number of QTL and by adding genotype-by-year interaction in our simulation, without accounting for these complexities in the statistical model. Results in Supplementary Figs. For scenarios without genotype-by-year interaction, the estimates of genetic and genic variance were very much in line with the true values and largely insensitive to the number of QTL  temporal analysis of genetic variance, (2) genomic analysis of genetic variance, (3) computational aspects and (4) assumptions of this study.This study will help breeders assess the amount of genetic variance in their programmes and better manage its utilisation for future genetic gains. Genetic variance  is a key component of the breeder’s equation for predicting response to selection Lush . While bThe framework builds on Sorensen et al. , LehermeThere are also other approaches to the temporal analysis of genetic variance. Tsuruta et al.  used theThe proposed framework can estimate the size and trends for genomic components of genetic variance. We have followed a standard quantitative genetics decomposition of genetic variance α2, which assumes independent binomial sampling of alleles with probability p (Hardy–Weinberg equilibrium). There is an excess of homozygotes over heterozygotes in some breeding programmes, particularly in plant breeding programmes that use inbreeding. In this case, we have a clear deviation from the Hardy–Weinberg equilibrium, and the expression 2p(1 − p)α2 will underestimate genetic variance at a locus. To see this consider p = 0.5 and α = 1, which gives 2p(1 − p)α2 = 0.5, but if we only have reference and alternative homozygotes (50% each) the actual variance is doubled due to complete inbreeding (Wright p(1 − p)(1 + F)α2, where 2p(1 − p)(1 + F) is the variance of genotypes under non-random mating, we suggest a simpler straightforward calculation of the variance of genotypes at a locus and multiplying that variance with α2. Bulmer (p(1 − p)α2 and a deviation due to non-random mating 2p(1 − p)Fα2.A final note on genomic analysis is that the proposed framework does not depend on the assumption of Hardy–Weinberg and linkage equilibrium. It is common to see expressions for genetic variance at a locus of the form 2g Wright . While t. Bulmer  was awarThe framework is based on Sorensen et al. , LehermeThe observation that leading principal components underestimate genic variance Fig.  requiresWe made two assumptions. First, we assumed a sufficiently dense panel of markers that collectively capture variation at QTL. An insufficient number of markers will deteriorate the ability of the framework to capture genetic variance at and between QTL. Our simulation shows that with a medium marker density, we can estimate genetic variance in a breeding programme accurately. Still, we highlight that we have simulated a simple genetic architecture with randomly located QTL along the genome. Second, we assumed that allele effects are constant over time and across groups of individuals. This assumption is reasonable for estimation in the sense that we used all the available data to estimate average marker effects as accurately as possible and use them across all individuals. However, allele effects can be time-, environment- or population-specific. We tested how our framework fares in such settings by extending the simulation with genotype-by-year interactions. The results have shown that we overestimate genetic and genic variance in those settings, sometimes by as much as 200%. This miss-match is expected because a too simple statistical model can capture too little genetic variation or capture non-genetic effects as genetic and lead to under- or overestimated genetic variances. While model choice is clearly an issue, we note that the proposed framework can be extended to models with effects such as genotype-by-year interactions, for example, by using a model described in Tolhurst et al. . While eSupplementary Material


The delta-lognormal distribution is a combination of binomial and lognormal distributions, and so rainfall series that include zero and positive values conform to this distribution. The coefficient of variation is a good tool for measuring the dispersion of rainfall. Statistical estimation can be used not only to illustrate the dispersion of rainfall but also to describe the differences between rainfall dispersions from several areas simultaneously. Therefore, the purpose of this study is to construct simultaneous confidence intervals for all pairwise differences between the coefficients of variation of delta-lognormal distributions using three methods: fiducial generalized confidence interval, Bayesian, and the method of variance estimates recovery. Their performances were gauged by measuring their coverage probabilities together with their expected lengths via Monte Carlo simulation. The results indicate that the Bayesian credible interval using the Jeffreys’ rule prior outperformed the others in virtually all cases. Rainfall series from five regions in Thailand were used to demonstrate the efficacies of the proposed methods. Thailand is located in Southeast Asia and is classed as a tropical area. It is influenced by both the southwest and northeast monsoons. The southwest monsoon crosses Thailand between mid-May to mid-October (the rainy season) and brings moist air from the Indian Ocean that causes clouds and heavy rain. The northeast monsoon crosses Thailand from mid-October to mid-February (the winter season) causing cold and dry weather. Moreover, the South receives additional heavy rainfall due to moisture coming in from the Gulf of Thailand. The season changes from mid-February to mid-May (the summer season) due to uncertainty in the weather and is influenced by tropical cyclones in the South China Sea, and thus, the weather is generally hot and dry but often with heavy rain and thunderstorms . ThailanThere have been numerous studies on rainfall series that have used the delta-lognormal distribution. For statistical inference, the CV, the ratio of the standard deviation to the mean, is a good tool for investigating rainfall dispersion. The advantage of using the CV is that it is unitless and thus, is useful for measuring dispersion in data series with different units or drastically different means. Focusing on inferential statistics, the confidence intervals and functions of the CV for several distributions have been presented. Since dispersion in the precipitation series for different areas can be the same or different, simultaneous estimation of this for multiple areas has been investigated using various distributions and parameters. As mentioned above, rainfall series data follow a delta-lognormal distribution. Since our focus is on comparing the dispersion of rainfall from five regions in Thailand, the pairwise differences between the CVs of their rainfall data distributions are an interesting topic to study. Although there have been numerous methods published for constructing SCIs for the differences between the parameters of several types of distributions, constructing SCIs for all of the pairwise differences between the CVs of delta-lognormal distributions has not yet been reported. GCI is a general method that is often used to construct confidence intervals, but FGCI is stronger than GCI since it provides asymptotically correct frequentist coverage . Moreovei = 1, 2, …, k be a random sample from k independent delta-lognormal distributions, denoted by Xi are Xi can be expressed as Let i, l =1 , 2, …, k and i ≠ l. The maximum likelihood estimators of μi are Since we are interested in constructing the SCIs for all pairwise differences between the CVs, then νi can be defined as i, l =1 , 2, …, k and i ≠ l.Assume that i, l =1 , 2, …, k and i ≠ l.According to To construct the simultaneous FGCIs, a fiducial generalized pivotal quantity (FGPQ), which is a subclass of the generalized pivotal quantity (GPQ) , is presDefinition 1k independent delta-lognormal distributions with parameters of interest , Northeastern (A2), Central (A3), Eastern (A4), and Southern (A5). The daily rainfall data from these areas in August 2020 were used to assess the performances of the proposed methods for SCI construction. The distributions of these data are presented in The simulation results indicate that the Bayesian credible interval using Jeffreys’ rule prior outperformed the other methods in virtually all cases. Although the coverage probabilities in some cases were close to 1.00, suggesting that overestimation may have occurred, the expected lengths were the shortest. Therefore, the Bayesian credible interval using Jeffreys’ rule prior can be used to construct the SCIs for all of the pairwise differences between the CVs of delta-lognormal distributions. Since constructing SCIs concerns the differences between the parameters of interest for all pairwise comparisons, our findings correspond with Herein, we proposed methods to construct the SCIs for all pairwise differences between the CVs of delta-lognormal distributions, including FGCI, two Bayesian methods constructed under the equal-tailed confidence intervals and credible intervals using the Jeffreys’ rule and uniform priors, and MOVER. The performances of the proposed methods were determined via their coverage probabilities together with their expected lengths under various circumstances. The results indicate that the Bayesian credible interval using the Jeffreys’ rule prior was suitable for constructing the SCIs for all pairwise differences between the CVs of delta-lognormal distributions in terms of the coverage probability together with the expected length. Furthermore, FGCI is appropriate for constructing these SCIs in cases of the variances equal to 0.5 and 1.0 with the proportion of non-zero values equal to 0.5 and 0.8 for the sample sizes of 50 and 100. In addition, the results of using daily rainfall data from five regions in Thailand coincided with those from the simulation study.