Meles meles) have received considerable attention, not least because the species acts as a potential wildlife reservoir for bovine tuberculosis (bTB) in Britain and Ireland. Herein, we detail the most comprehensive population and landscape genetic study of the badger in Ireland to date—comprised of 454 Irish badger samples, genotyped at 14 microsatellite loci. Bayesian and multivariate clustering methods demonstrated continuous clinal variation across the island, with potentially distinct differentiation observed in Northern Ireland. Landscape genetic analyses identified geographic distance and elevation as the primary drivers of genetic differentiation, in keeping with badgers exhibiting high levels of philopatry. Other factors hypothesized to affect gene flow, including earth worm habitat suitability, land cover type, and the River Shannon, had little to no detectable effect. By providing a more accurate picture of badger population structure and the factors effecting it, these data can guide current efforts to manage the species in Ireland and to better understand its role in bTB.The population genetic structure of free‐ranging species is expected to reflect landscape‐level effects. Quantifying the role of these factors and their relative contribution often has important implications for wildlife management. The population genetics of the European badger ( As an emerging discipline, landscape genetics combines population genetics and landscape ecology to assess the influence of landscape or environmental features on genetic variation in wildlife populations, their dispersal, and connectivity of habitats  Figure The European badger , they can still be considered relatively high compared to other European populations  specialists , and biotic interactions (earthworm availability), on badger gene flow.22.1n = 176) from an ongoing road traffic accident (RTA) survey were collected by the Department of Agriculture, Environment and Rural Affairs Northern Ireland (DAERA‐NI) across all six counties of NI during the period from September 2011 to March 2013. GPS locations of all carcasses were logged and a tissue sample was stored for DNA extraction. In addition, badger carcasses (n = 278) from ongoing culling efforts in the RoI were collected by the Department of Agriculture, Food and the Marine (DAFM) during 2014. These badger carcasses were collected from sites distributed across 23 of the 26 counties of RoI. The three counties excluded were Donegal, Dublin, and Louth on account of there being no badger carcases available during the time window described. GPS coordinates of locations of culled animals were collected and a tissue sample stored for DNA extraction. For 21 samples across both territories, there were no geo‐location data available. Samples were frozen at −20°C before DNA extraction.A total of 454 badger samples were collected from the Republic of Ireland (RoI) and Northern Ireland . Extracted DNA was stored at −20°C until PCR amplification for either microsatellite or mitochondrial DNA sequence analysis.2.3We genotyped all 454 samples at the following 14 microsatellite loci—Mel102, Mel103, Mel104, Mel105, Mel106, Mel109, Mel110, Mel111, Mel112, Mel113, Mel114, Mel115, Mel117, and Mel129  was used as the template. PCR conditions for all three multiplex assays were as follows—96°C for 15 min, followed by 35 cycles of 96°C/30 s, 60°C/90 s, 72°C/90 s, followed by a final cycle of 72°C for 10 min.Final PCR products were diluted 1:10 with double distilled water before electrophoresis on a Life Technologies ABI3130xl genetic analyzer  using a GS500 Rox size standard. Allele calls were scored and manually checked using Life Technologies Gene‐mapper 4.0 software.2.4We regenotyped a random selection of 5% of all extracted DNA samples tested. All microsatellite data were subjected to analysis by Microchecker v2.2.3 , observed (HO), and expected (HE) heterozygosity and the inbreeding/fixation coefficient (FIS). Deviations of allele frequencies from Hardy–Weinberg equilibrium (HWE) were also assessed by GENEPOP v4.2.We determined standard population genetic indices of diversity from the microsatellite data using GENEPOP v4.2 Rousset, , namely To elucidate the genetic population structure of badgers across Ireland, we analyzed microsatellite data of all 454 badger samples in an admixture model in STRUCTURE v2.3.4 , we used the ΔK method of Evanno, Regnaut, and Goudet  were processed by the program CLUMPP .In order to examine the influence of geographic distance, landscape variables, and biotic interactions on badger gene flow, we combined Mantel tests, multiple regression on distance matrices (MRM), and redundancy analysis (RDA). Analyses were conducted with the subset of individuals for which precise coordinates of sampling were available  within said categories (CORINE Level 2), we made further distinctions: within artificial surfaces, we lowered resistance for artificial, nonagricultural vegetated areas; within agricultural areas, we assigned low resistance to pastures and high resistance to arable lands and within forest and seminatural areas, we assigned highest resistance to open spaces with no vegetation.To generate land cover surfaces, we used the CORINE land cover data set EEA,  and genehttp://srtm.csi.cgiar.org/), and two surfaces were generated, one maintaining raw elevation (masl) and one using a threshold of 200 m above sea level to assign low vs. high resistance (1:100) because badgers tend to avoid elevations beyond this threshold  from the Global Biodiversity Information Facility, GBIF (http://www.gbif.org/), and reviewed relevant literature on the ecology of Lumbricus terrestris and related taxa , and two soil variables: pH and silt % .As a result, we selected five environmental variables, including three bioclimatic variables: temperature seasonality (BIO4), maximum temperature of the warmest month (BIO5), annual precipitation , they were used together as input in MaxEnt software, where analyses were run with default settings. Results of analyses showed the area under the curve (AUC) = 0.791 for the MaxEnt output model, which is considered an acceptable predictive accuracy , we generated a “flat” resistance surface in which all cells had the same value (=1). This is an alternative to using Euclidean distance that accounts for the finite size of the landscape  with genetic distance matrices. These were checked for correlation using the “cor” function in R, and only those without strong correlation (r < 0.8) were retained for MRM analyses. In MRM models, matrices were used as predictors of genetic distance. Model selection was done using a backward elimination approach, with a “threshold” p < 0.05. Significance of regression coefficients and R‐square values was assessed from 10,000 permutations.From the generated resistance surfaces, we obtained resistance–distance matrices using the software Circuitscape 4.0 , are increasingly used in landscape genetics studies given their power to detect effects of landscape resistance and barriers on gene flow  and tests for Hardy–Weinberg equilibrium (HWE) across all of Ireland, NI, RoI, and the five Irish populations identified by STRUCTURE (see below) are shown in Table Indices of diversity, inbreeding fixation . 3.3K (L(K)) around K = 4 or 5 ) around K = 5  were observed  accounted for 30.4% of the observed genetic variance, whilst linear discriminant axis 2 (LD2) accounted for 17.4% of observed genetic variance  accounted for 16.0% of observed genetic variance , confirming IBD. When controlling for flat resistance–distance in partial Mantel tests, genetic distance showed significant positive correlation with raw elevation  suggesting that elevation inhibits gene flow.The simple Mantel test showed a significant correlation between genetic distance and the flat resistance surface , or EHS. The results of the partial Mantel tests and correlations for all landscape factors are presented in Supporting Information Table F = 72.235, p = 0.001). When controlling for geographic location with partial RDA, EHS and elevation only explained 2% of variance . In contrast, when controlling for EHS and elevation, geographic location explained 27% of variance .After model selection in RDA, the minimal model included the variables: elevation, EHS, geographic location (latitude/longitude) and explained 40% of genetic variance  K = 10 scenario. Such clinal variation would be in keeping with a species experiencing IBD across its range and is likely a more appropriate model of badger genetic variance than the one inferred by the K = 5 hierarchical STRUCTURE model. Indeed, the large numbers of animals that were found to exhibit admixed genetic heritage and no defined subpopulation membership in both the STRUCTURE K = 2 and K = 5 analyses lend credence to the DAPC supported clinal nature of badger genetic variation in Ireland.At ns Table . Given tLandscape genetics analyses showed an effect of geographic location/distance on genetic differentiation . This could indicate that Irish badgers are philopatric. Nevertheless, whilst IBD is consistent with, on average, limited dispersal, mark–recapture studies of Irish badgers have documented rare long‐distance dispersal of up to 22.1 km . This pattern indicates that earthworm availability has a low influence on badger dispersal/gene flow, consistent with previous suggestions that Irish badgers have less‐specialized diets than populations elsewhere (Cleary et al., For a species with strict habitat requirements and limited dispersal ability, strong associations among habitats and genetic differentiation are expected (Kierepka & Latch, Our overall findings thus highlight the importance of spatial replication in landscape genetics studies (Castillo et al., 5Our data demonstrate that geographic distance and elevation are pre‐eminent drivers of continuous, clinal, genetic variation in contemporary Irish badgers. These data can guide management of the species in Ireland, particularly in the context of controlling bovine tuberculosis in cattle. Knowledge of genetic population structure allows us to formulate testable hypotheses about how this level of partitioning might affect spatial disease patterns in the pathogen (Biek & Real, M. bovis clusters, a small proportion of dispersers have been observed to move larger distances (Byrne, Quinn, et al., Additionally, whilst philopatry appears to be the norm in the Irish badger and may contribute to maintenance of local The authors declare no competing interest.JG participated in the design of the study, carried out statistical analysis, and drafted the manuscript. AB participated in the design of the study and drafted the manuscript. JL, EP, and GK carried out the molecular laboratory work. EC, JO'K, UF, and DO'M collected field samples. DE helped with statistical analysis. CM carried out field sampling and mapping. RB and RS participated in the design of the study and drafted the manuscript. AA conceived the study, participated in its design, drafted the manuscript, and performed statistical analysis and molecular laboratory work. All authors gave final approval for publication.https://doi.org/10.5061/dryad.3r85690 as Supporting Information All badger genotypic data are available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file. Click here for additional data file.


This study aimed to measure genetic diversity and to determine the relationships among fourteen goose breeds.Microsatellite markers were isolated from the genomic DNA of geese based on previous literature. The DNA segments, including short tandem repeats, were tested for their diversity among fourteen populations of geese. The diversity was tested on both breeds and loci level and by mean of unweighted pair group method with arithmetic mean and structure program, phylogenetic tree and population structure were tested.IS) ranged from −0.033 to 0.358, and the pair wise genetic differentiation (FST) ranged from 0.01 to 0.36 across the fourteen breeds; for the 9 loci observed and expected heterozygosity, and Ne were same as the breeds parameters, PIC of the screened loci reported 6 loci highly polymorphic and 3 loci to be medium polymorphic, and FIS ranged from −0.113 to 0.368. In addition, genetic distance estimate revealed a close genetic distance between Canada goose and Hortobagy goose breeds by 0.04, and the highest distance was between Taihu goose and Graylag goose (anser anser) breed by 0.54.A total of 108 distinct alleles (1%) were observed across the fourteen breeds, with 36 out of the 108 alleles (33.2%) being unique to only one breed. Genetic parameters were measured per the 14 breeds and the 9 loci. Medium to high heterozygosity was reported with high effective numbers of alleles (Ne). Polymorphic information contents (PIC) of the screened loci was found to be highly polymorphic for eleven breeds; while 3 breeds were reported moderately polymorphic. Breeding coefficient (FCluster analyses were made, and they revealed that goose breeds had hybridized frequently, resulting in a loss of genetic distinctiveness for some breeds. Geese play a minor role in meat and egg production compared to chicken worldwide. On the other hand, the nutrition values of protein, vitamin A, vitamin B, niacin and sugar content are higher in goose meat, than that in pork or mutton. The energy content is 30% to 63% greater than that of other poultry with the advantage of low-fat and cholesterol content ,2. GeeseIn the past few decades, extensive production systems have been instituted by many countries aiming for high quality and quantity of meat as their primary breeding objective. As a direct result of these systems, some goose breeds with specific features either have declined or their breed characteristics diluted to improve their genetic admixture ,5.The evaluation of genetic variation can be done using several techniques. One of the most important techniques is the usage of microsatellites. Microsatellites have many advantages including their large number of polymorphisms, abundance, co-dominant inheritance, analytical simplicity, and transferability –8. In reIn recent studies the genetic diversity among geese populations in Taiwan were evaluated along with industrial white Rom farms revealed unified genetic structures in their breeders, ensuring more stable and better performing populations. However, Chinese breeds raised at private farms revealed an uneven structure, indicating that breeding management requires urgent care to ensure stable production, maintain genetic resources, and develop hybrid geese for better meat quality .The mitochondrial DNA control region sequence variation of domestic geese was analyzed to evaluate the main matrilineal components and their phylogenetic relationships. Our results supported that Chinese domestic goose breeds (except the Yi breed) originated from the Swan goose (Anser cygnoides) (Sw); while European goose breeds originated from Graylag goose (Anser anser) (Gray) ,14.In the present study, we used 14 goose breeds. They were classified according to their geographical distribution into: four Chinese breed ; five Europeans ; 2 African Egyptian breeds; 2 wild (graylag and white swan and one American breed [Canada]). We evaluated both the genetic diversity and genetic relationship between these populations. In order to give recent information about genetic diversity among, and within these breeds to optimize the utilization of goose genetic resources, and permit efficient genetic improvement for both production and conservation needs.DNA samples  were obt+2) buffer, 2 μL of 10 mmol/L dNTPs, 1 μL of 10 μmol/μL forward primers, 1 μL of 10 μmol/μL reverse primers, 0.2 μL of 5.0 U/μL Taq DNA polymerase, and 1 μL of 100 ng/μL DNA template, and completed by adding 12.8 μL of double distilled water. After a denaturing step for 5 min at 95°C, samples were processed through 35 cycles of 45 s at 94°C, 45 s at an optimal annealing temperature (55°C to 64°C), and 45 s at 72°C. The last elongation step was extended to 10 min at 72°C, and preserved at 4°C as shown in  program [The allelic data obtained through individual genotyping were analyzed by using different analytical software. MS toolkit ( program  was implIS) and genetic differentiation (FST) were analyzed by FSTAT Weir [6 iterations after a burn-in period of 1×105 iterations for 14 populations. The most likely number of populations (K) was determined according to the procedure explained by [The indices of inbreeding  over the fourteen breeds and the nine microsatellite loci.The present study assessed the level of genetic diversity and the population structure of different geese breeds; genetic diversity was measured in terms of allelic frequency, Ho and He, PIC, Ne and inbreeding coefficient  were observed across the fourteen breeds, with 36 out of the 108 alleles (33.2%) unique to only one breed as shown in MATLAB software  was usedGoose populations: Genetic parameters were calculated for the fourteen geese breeds as shown in IS) revealed statistically significant differentiation (p<0.01) among the studied populations. The mean values of FIS ranged from −0.033 (Ca) to 0.358 (Land).Microsatellite loci: Same parameters were calculated per the nine loci as shown in IS revealed statistically significant differentiation (p<0.01) among the nine loci and ranged from −0.113 (CKW14) to 0.368 (GO7).Genetic differentiation (FST): The values of genetic differentiation (FST) for each tested population pair are sum-marized in Genetic distance and phylogenetic tree: The Nei’s [he Nei’s  DA was cThe STRUCTURE software  program When K = 10 there was a clear clustering for Hort, Can, Egy G, Gray, Sw, Yang, Shi, Leim, Ca, and Land. K = 11 was the same as K = 10 except for more interference between the breeds. K = 12 the clusters with clear identification were Gray, Sw, Yang, Shi, Leim, Ca, and Land. K = 13 can see clusters for only Gray, Sw, Yang, Leim, Ca, and Land. K = 14 the clearest clusters were for Gray, Sw, Yang, Shi, Ca, and Land.It is difficult and time-consuming to distinguish indigenous goose breeds on morphological characteristics alone, for this reason it is important to develop molecular markers to aid in goose breeds identification. This will also help in designing an effective breeding program to improve productivity of these breeds, and protect them from becoming extinct.Allele frequency is defined as the relative frequency of an allele at a particular locus in a population, expressed as a fraction or percentage . The chaHeterozygosity, reflects genetic variation in a tested locus among a population. High heterozygosity indicates low genetic uniformity thus high genetic diversity. The mean heterozygosity across all 14 populations and 9 loci in the present work ranged from medium at Hort 0.48 to high at the two wild breeds Sw and Gray 0.69; same as per loci medium at CKW13 0.28±0.12 to high at TTUCG5 0.84±0.03. Medium heterozygosity might be due to inbreeding in population because of the relatively small group in a breeding farm.On the other hand, high heterozygosity was attributed to direct result of a breeding program based on selection to improve the genetic admixture for some breeds ,5,25. ObThe PIC is a good index for gene fragment polymorphism. The PIC index can be used to evaluate the level of gene variation: when PIC<0.25, the locus has low polymorphism; when PIC>0.5, the locus has high polymorphism; and when PIC ranged between 0.25 and 0.5, the locus has intermediate polymorphism . The PICThe effective population size (Ne) is the number of individuals in the idealized Wright Fisher  populatiIS average, might indicate non-random mating, also these examined loci might be under morphological or productive traits of selective interest. Moreover, FIS is used to obtain a deeper insight to appraise the degree of in-breeding and endangerment potentiality and is considered as an important tool to judge the conservation priority [IS is less than 0.05, the breeds are not in danger as in case of Ca breed; between 0.05 to 0.15, they are potentially endangered; between 0.15 to 0.25, they are minimally endangered as in case of Sw, Gray, Leim, Egy G, Egy B, Can, and Tai, breeds; between 0.25 to 0.40, they are endangered as in case of Hort, Rom, Yang, Shi, Yi, and Land breeds; and more than 0.40, they are critically endangered [IS results could be related to different factors such as population sub structuring or recent population growth [Population analysis F-statistics are a way of partitioning variances in gene frequencies among subpopulations by using ratios of different variances . The relpriority . Accordidangered . The FISn growth . These fn growth ,12–14.Genetic differentiation (FST): The pairwise genetic differentiation (FST) is a measure of population differentiation due to genetic structure it ranges from 0 to 1. When FST = 0, there is no differentiation between the subpopulation. When FST = 1, all the alleles in the subpopulation are different [ST value across the 14 studied population showed low (0.01) to moderate mean (0.36) indicating that there is genetic differentiation among the 14 breeds for these values as FST was higher than 0.25 [ifferent ,31. The han 0.25 , this wahan 0.25 ,24,30,31Genetic distance and phylogenetic tree: The project of breed protection and a plan of breeding can be made by analyzing genetic distance. Genetic distance should be an index for group structure and breeds diversity when breed conservation decisions are being made. Microsatellite allelic gene frequency analysis was one of the best methods at present, as it can reflect the time of diversity as well as genetics and variation among breeds [g breeds .A variety of genetic distances are currently available, but Nei DA is the most commonly used . The DA The clustering based on UPGMA and Nei’s distance is the best method for analyzing genetic diversity in different breed population. Nei  discoverThe Structure program is based on the Bayesian probability theory and uses the Markov-Monte Carlo simulation algorithm. When the program runs, a mixed model is used to set the number of classification K of the detected population. All individuals can be divided to reflect the genetic structure of the population, especially for the population genetic structure and individual differentiation, migration and other aspects of the study. The structure program can also infer individuals with complex genetic background or migrating individuals in a population based on the number of individual alleles . The attMicrosatellite markers are the more credible tool for the research of breed origins. This is due to the evolution of breeds in nature, and artificial selection had a little effect on the structure of Microsatellite locus. The clustering results support the hypothesis that geographical distance is an important factor influencing the genetic relatedness of populations’ and provided some useful data for evaluation of breeding programs results.


As CaCO3 structures such as shell are deposited incrementally, sequential analysis of these structures allows reconstructions of animal movements. However, variation driven by genetics or ontogeny may interact with the environment to influence CaCO3 composition. This study examined how genetics, ontogeny, and the environment influence shell composition of the bivalve Perna canaliculus. We cultured genetically distinct families at two sites in situ and in the laboratory. Analyses were performed on shell formed immediately prior to harvest on all animals as well as on shell formed early in life only on animals grown in the laboratory. Discriminant analysis using 8 elements  classified 80% of individuals grown in situ to their family and 92% to growth site. Generalised linear models showed genetics influenced all elements, and ontogeny affected seven of eight elements. This demonstrates that although genetics and ontogeny influence shell composition, environmental factors dominate. The location at which shell material formed can be identified if environmental differences exist. Where no environmental differences exist, genetically isolated populations can still be identified.The trace elemental composition of biogenic calcium carbonate (CaCO The composition of these structures has been shown to reflect a number of environmental parameters. Analysis of the trace elemental composition of carbonate structures formed early in an individual’s life can therefore be used to infer the water mass in which they formed and provide information on natal locations and population connectivity7. This technique of reconstructing animal movements from calcium carbonate structures is known as trace elemental fingerprinting9.The trace elemental composition of biogenic calcium carbonate  ratios in in bivalve shells15. This suggests that other factors intrinsic to individuals influence the trace elemental composition of CaCO3 structures.Implicit in the use of the trace elemental fingerprinting is the assumption that these calcified structures reflect the properties of the water mass in which they were formed. Thus, there is a significant body of literature investigating the relationship between environmental parameters and the trace elemental composition of biogenic CaCO18. Understanding whether genetic variation can affect the elemental composition of bivalve shell is essential before inferences on the conditions under which they formed can be made and ecologically relevant information obtained. If the incorporation of trace elements to shell material is dominated by genetics, the use of trace elemental fingerprinting to reconstruct animal movements may be limited. Similar elemental fingerprints may be misinterpreted as being caused by shell forming at the same location rather than due to genetic similarities. This would be of particular concern within metapopulations where a lack of genetic heterogeneity and diversity results in homogeneous trace elemental fingerprints in individuals from a number of spatially separated sub-populations.Although the influence of genetic variation on the incorporation of trace elements into bivalve shell has not been previously investigated, laboratory studies have shown that the trace elemental fingerprint in fish otoliths is a result of interactions between the environment and the genetic origin of individuals3 structures may vary under tightly controlled conditions, understanding how a broad suite of elements varies under natural conditions is essential for reconstructing animal movements. If both genetic and environmental variation drive the incorporation of elements into shell material, these could enhance differences between populations and improve the utility of this method for determining population connectivity17. Regardless of genetic effects if environmental differences between sites exert dominant control over the incorporation of trace elements into shell, the use of trace elemental fingerprinting will remain useful regardless of the genetic relationships between populations.Although laboratory studies provide insight as to how the incorporation of elements into CaCO3. In fish otoliths and bivalve shells, age can affect the response to environmental variables19. It is important that any ontogenetic influences are known before inferences on changes in the environment as an animal grows can be determined. If this is the case, it is important that differences in trace elemental fingerprints between locations are greater than any ontogenetic differences to avoid complications in the development of reference fingerprints20.In addition to biological variation driven by genetic differences, it is also likely that ontogenetic changes in animals will modulate how trace elements are incorporated into biogenic CaCOPerna canaliculus Gmelin 1791 (GreenshellTM mussel). We set out to answer the following three questions: (1) Are environmental differences between sites or genetic differences between families responsible for the trace elemental composition of P. canaliculus grown at two separate sites? (2) Does the trace elemental composition of genetically distinct, P. canaliculus families vary when grown under identical laboratory conditions? (3) Finally, as an animal grows, what impact does this have on the trace elemental composition of its shell? Using animals from a commercial selective breeding programme, individuals from known genetic lines were studied in both in situ and laboratory trials. Variation in the trace elemental composition of shell material was analysed using laser ablation inductively coupled plasma mass spectrometry.The aim of this study was to determine the influence of the genetic lineage of individuals, the location at which they grew, and ontogeny on the trace elemental composition of the shell of the bivalve Perna canaliculus grown in the field and were therefore considered in further statistical analyses. The most abundant of the elements was strontium followed by magnesium. The least abundant elements were cobalt and lead  in addition to calcium were consistently detectable in shells of ad Table .TE:Ca) ratios into the classification model  resulted in correct classification of individuals to their growth site:family combination with 78% accuracy  stepwise variable selection procedure revealed that the inclusion of eight trace element:calcium (y Tables  and 2. Iy Tables .The incly Tables  supportey Tables  which inTE:Ca ratio (Table TE:Ca ratio was Ti:Ca which increased growth site classification to 73% (a 22% increase), family classification to 47% (a 17.6% increase) and site:family classification to 43% (an 18% increase) , Mn:Ca , B:Ca , Co:Ca , and Ti:Ca  ratios , Li:Ca  and Mg:Ca    Fig. . Pairwis01) Fig. . A signi01) Fig. .Figure 29,168 = 2.1, p < 0.03), Mn:Ca , and B:Ca  ratios , Mg:Ca , Ti:Ca , Co:Ca , Ni:Ca , Cu:Ca , Ba:Ca , and Mg:Ca  ratios. Multiple pairwise differences were shown to be responsible for these differences , Mg:Ca , and Ba:Ca   particularly in the prodissoconch I shell which begins to form whilst Perna canaliculus larvae are still in a lecithotrophic stage38.It is also possible that maternal condition and investment into the production of gametes beyond simple genetics may have affected the composition of shell material. Maternal investment can vary between individualsa priori statistical technique which requires all potential source populations to be categorised was used. Although in this study all potential source populations were categorised, in population connectivity studies this is often impossible to achieve due to the multitude of possible sources. However, as this study shows that we may be able to estimate genetic variation from the trace elemental fingerprint of individuals, future studies should focus on the development of alternative statistical techniques, such as those proposed by White et al.39 or Neubauer et al.40.Despite the findings of this study, it is important to acknowledge possible limitations. Only two sites were investigated, which results in a much simpler QDFA model as significant variation in only one element between sites may be sufficient to correctly classify all individuals. Additionally, an 12. Explicitly relating environmental differences to shell chemistry in this case would have been difficult due to complicated interactions between different environmental variables and trace elemental fingerprints. Finally, future studies investigating the effect of ontogeny in the shell of Perna canaliculus should examine multiple time points to determine if the differences observed in shell material deposited at different ages represents the beginning of a trend or if elemental incorporation stabilises as an individual ages.Although we assume that differences in environmental parameters between sites are likely due to their geographic location, this study would have benefited from closer monitoring of these conditions. A number of studies however, have attempted to relate environmental conditions to the levels of trace elements in shells with varying levels of successTE:Ca ratios which have been reported in the literature. These results have the potential to allow the further refinement of the technique of trace elemental fingerprinting to allow patterns of genetic connectivity to be unravelled through the analysis of bivalve shell. Finally, there is growing consensus that the integration of both genetic and trace elemental markers in carbonate structures is essential for accurate estimates of population connectivity to be obtained42. As these studies become more widespread there is an opportunity for further investigation into links between genetic differences between populations and their trace elemental fingerprint.Overall, this research has provided additional insight into the factors which influence the incorporation of trace elements into bivalve shell. They provide more uses for fingerprinting over small scales. The findings that genetic variation and ontogeny influence the uptake of trace elements provides a possible explanation for the conflicting relationships between the environment and P. canaliculus grown at two separate sites?Are environmental differences between sites or genetic differences between families responsible for the trace elemental composition of 2 families from the Cawthron commercial Perna canaliculus selective breeding programme were investigated (Families A-F). Animals were spawned in 2014 and transported to two in situ sites where they were grown on long-line aquaculture facilities in the Marlborough Sounds, New Zealand. Each family was ongrown at two sites located approximately 10 km apart in Pelorus Sound. Site One was located in outer Pelorus Sound and therefore subject to more oceanic influences. Site Two was located in mid Pelorus sound and is therefore more affected by freshwater input and terrestrial influences. Twenty animals from each family at each site were harvested on the 8th of April 2018, after a period of moderate rainfall43.The flesh was removed, shells were frozen at −20 °C and transported to the University of Auckland where they were stored at −20 °C until elemental analysis.Six FIn preparation for elemental analysis each valve was scrubbed thoroughly with a household brush to remove any large adhering particles and biofouling organisms. A fragment of the growing shell edge measuring approximately 15 mm × 15 mm was broken off the left valve of each individual along the axis of maximum growth. This fragment was then rewashed in deionised water and mounted on a glass microscope slide using double sided adhesive tape. As the periostracum is not a calcified structure and is comprised primarily of organic matter it was removed through desiccation which caused it to peel off the shell.P. canaliculus families vary when grown under identical laboratory conditions? Does ontogeny affect the trace elemental composition of shell material?Does trace elemental composition of genetically distinct, Perna canaliculus were examined in this study (named hereafter Families G-P). Individual male and female mussels were randomly selected from unrelated F2 families raised on long-line aquaculture facilities in the Marlborough Sounds  as part of the Cawthron Perna canaliculus selective breeding programme. Thermal shock was used to induce spawning; eggs were diluted to 1000 mL−1 and fertilized with sperm at 200 egg−1 44, and transferred to 160 L incubation tanks containing 5 µm-filtered seawater and 12 µM EDTA at 16 °C, with gentle aeration. After 48 h incubation the embryos had formed the prodissoconch I shell, entering the feeding veliger stage. Larvae were transferred to the continuous-flow culture system described by Ragg et al.44. Three 2.5 L tanks were used for each family. Density was approximately 200 larvae mL−1. They received filtered seawater enriched with dietary microalgae . After 3 weeks, pediveligers were offered coir string as a settlement substrate and allowed to metamorphose and grow. Water properties including temperature, salinity, alkalinity and pH were monitored throughout the culturing phase . LA-ICP-MS analysis was performed 200 µm from the most recently formed edge  laser ablation system (Electro Scientific Industries) coupled to an Agilent 7700 ICP-MS (Agilent Technologies). One LA-ICP-MS spot analysis was performed on each of the shells grown dge Fig. . This ren = 30 per family); where less than 10 individuals were available as many individuals as possible from each family were analysed  610 and 612 glass standards were analysed every 20 spots for standardisation and calibration purposes. For full laser operating parameters see Tables Prior to each laser spot analysis, background element counts were monitored for 30 seconds. The laser operated with a spot size of 50 µm, a repetition rate of 5 Hz, and a dwell time of 40 seconds. Laser power was 45% and fluence was between 7 and 7.5 Jcm45. We used only the next 10 seconds of data to determine the elemental composition of the shell material in order to reduce the risk of laser burn through to lower layers within shell material2. We background-corrected data by subtracting background average counts from the ablation counts. Data was then standardised using the most recent published NIST610 values46 and NIST612 values were used to calculate internal precisions. All data was then standardised to a trace element:cacium ratio (TE:Ca) in µmol of trace element to mol of calcium.To remove possible surface contamination we used a pre-ablation technique where the first five seconds of dwell time were not included in the data reduction process41. This was done stepwise by removing the least significant TE:Ca ratio whilst still maintaining site classification success rates of >90%. Receiver operating curves were then generated for each site:family combination to provide a measure of sensitivity of the DFA model. This QDFA was undertaken in JMP v13.0 .To determine how overall trace elemental fingerprints varied between sites and between families, as well as to determine if genetic variation prevented classification of individuals to their growth site, a quadratic discriminant function analysis (QDFA) was performedTE:Ca ratios investigated. The GLM examined the effect of growth site, family, and any interactions between these two terms. Post hoc t-tests were performed, with a false discovery rate (FDR) p-value adjustment applied due to multiple comparisons.In order to determine how each of the elements responsible for classification varied, and to examine how they contributed to classification in the QDFA, univariate GLMs were performed in R v3.3.3 (R core team). Prior to analysis we inspected the data which did not meet the assumptions of normality, and we therefore log transformed the data which improved normality for all P. canaliculus shell in the absence of environmental differences we performed univariate statistical analyses on the same TE:Ca ratios as above. Univariate analyses were performed in R v3.3. The data did not fit the assumptions of normality; thus, we performed a log transformation on all elemental ratios, which improved normality. We used additional GLMs to examine the effects of the burn position (shell edge vs umbo), family and any interaction effects. In the GLMs the three replicate tanks for each treatment were included as random factors. Post hoc t-tests were performed, with a false discovery rate (FDR) p-value adjustment applied due to multiple comparisons.To examine the effect of ontogeny and genetics on the incorporation of trace elements into Supplementary material


ST value estimates. Analysis of molecular variance (AMOVA) and pairwise FST values showed significant differentiation among all the pairs of sub-population ranging from 0.152 to 0.222 suggesting that all the three subpopulations were significantly different from each other. AMOVA revealed that most of the variation in rice accession mainly occurred among individuals. The present study suggests that diverse germplasm of NE India could be used for the improvement of root and drought tolerance in rice breeding programmes.The studies on genetic variation, diversity and population structure of rice germplasm of North East India could be an important step for improvements of abiotic and biotic stress tolerance in rice. Genetic diversity and genetic relatedness among 114 rice genotypes of North East India were assessed using genotypic data of 65 SSR markers and phenotypic data. The phenotypic diversity analysis showed the considerable variation across genotypes for root, shoot and drought tolerance traits. The principal component analysis (PCA) revealed the fresh shoot weight, root volume, dry shoot weight, fresh root weight and drought score as a major contributor to diversity. Genotyping of 114 rice genotypes using 65 SSR markers detected 147 alleles with the average polymorphic information content (PIC) value of 0.51. Population structure analysis using the Bayesian clustering model approach, distance-based neighbor-joining cluster and principal coordinate analysis using genotypic data grouped the accession into three sub-populations. Population structure analysis revealed that rice accession was moderately structured based on F Oryza sativa L.) is a primary staple food crop for more than 3.5 billion population of the world and cultivated across at least 114 countries1. Rice is grown on an area of 163 million hectares with the production of 758.9 million tonnes (499.2 million tonnes milled basis) of rice2. In India rice is cultivated on 43.79 million hectares with a production of 112.91 million tonnes of milled rice in 2017–183. Rice production must be enhanced by about 60% to meet dietary needs by the year 2025 to match the explosive increase in world population4.Rice (5. North-Eastern (NE) India being the secondary source centre of origin of rice, it is a hotspot of rice genetic resources in the world and rich in rice crop diversity. These landraces are grown in diverse ecosystems ranging from the high altitude of Arunachal Pradesh, the flood-prone areas of Assam, and rainfed, irrigated, upland, steep terraces and deep-water, Jhum and tilla land ecologies. Due to its wide spectra of cultivation systems and erratic rainfall due to climate change, rice cultivation in the NE region particularly Assam has experienced water stress condition in recent year’s drastically reducing yield. Now there has been tremendous pressure mounting on the breeders to identify the sources of resistance/tolerance to drought stress in rice to combat water stress condition.Rice is the main principal household cereal crop and nutritional security food crop of North East India. It occupies 75% of the total cultivated area of the region (4.58 million hectares)aus/ahu” rice6. These are photoperiod insensitive landraces maintained by farmers endowed with tremendous genetic variability and valuable genes for various abiotic stress tolerances as they are not subjected to selective breeding over a long period. Recently, with the use of genome sequence information, aus subpopulation is identified as distinct subpopulation from both indica and japonica subpopulation of O. sativa species9. Recent sequencing of wild rice relatives Oryza rufipogon species complex suggested that aus cultivars evolved from a distinct population of the annual Oryza nivara found in NE India, Bangladesh and Northern Myanmar10. Kim et al.9 stated that “the cultivated aus cultivars and its wild ancestor represent an underappreciated genetic resource”. These aus rice cultivars are early maturing, photoperiod insensitive and drought tolerant11. The genetic diversity among aus genotypes is abundant and they are enriched with various abiotic and biotic stress resistance genes13. For example, a traditional aus cultivar FR 13A harbour Sub1 gene which confers submergence tolerance15; likewise Kasalath an aus type cultivar was the donor of Pstol1 gene which confers phosphorus starvation tolerance13. Rayada, N-22 and Dular aus type cultivars have a large root length and high root density and drought resistance17. Nagina22 deep-rooted an aus type rice cultivar is the donor for heat and drought tolerant traits23. BG1222 an aus type cultivars harbour xa34 bacterial blight resistance genes24. Most of the drought-tolerant rice are originated from aus rice germplasm which has been cultivated in Northeast India and Bangladesh25. Knowledge of the genetic diversity of rice genotypes is useful for core collection development and effective conservation strategy. In the present study, an attempt was made to study genetic diversity in a few rice genotypes of North East India, the majority of which were agronomically identified as aus/ahu cultivars along with few indica rice.Among the various groups of rice cultivated in Assam, upland rice cultivar of North East India which is directly sown in fields during March-April and harvested in July-August is known agronomically as “26. Root is the first organ to experience water stress and thus root system plays a vital role under drought stress conditions29. Root traits such as small fine root diameter, long specific root length and root length density play a major role in water uptake and maintaining plant productivity under drought. The nature and type of root characteristics are the main factors deciding their survival and adaptation to drought. The distribution of the root system and its density indicate water uptake potential30. In rice, deep rooting is governed by DEEPER ROOTING1, which confers improved drought resistance in drought stress environment31. In upland rice, the long root system is highly associated with drought tolerance32.The root system is the main part of plants for the absorption of water and nutrients from soil35. Thus, DNA based markers such as RAPD (Random Amplified Polymorphic DNA), SSR (Simple Sequence repeat), AFLP (Amplified Fragment Length Polymorphism) and SNP (Single Nucleotide Polymorphism) have been routinely used to assess the genetic divergence among the genotypes as they are not influenced by environmental factors.Study on root traits particularly in the field condition is very challenging and thus is very limited. Similarly, no attempt has been made to study the genetic architecture of the root traits in relation to drought tolerance using rice from North East India which necessitates the study of the diversity for such traits for supplement rice improvement works. It is envisaged that the selection of diverse parents based on root traits and drought tolerance would facilitate the development of transgressive segregates, as well as, heterotic groups for hybrid crop breeding in the population. However, genetic diversity analysis solely based on phenotypic traits may not be a reliable measure of genetic differences as they are influenced by environmental factors36. SSR markers are preferred as they are abundance in the genome, well-distributed throughout the genome, hyper-variable, multi-allelic and co-dominant nature, ease of assaying, highly reproducible and highly informative38. SSR markers are immensely valuable in studies of variation detection, diversity analysis, phylogeny, population structure, gene mapping and association studies40.Multi-allelic nature and high polymorphism of SSR markers help to establish the relationship among the individuals even with less number of markersThe knowledge of the extent of genetic variation, diversity and genetic relationships between genotypes of the crop is vital and foundation for developing an improved cultivar possessing high yield, good grain quality and adapted to various abiotic and biotic stresses situations. Knowledge of the genetic diversity of rice genotypes is useful for core collection development and effective conservation strategy. Thus in this present study attempt has been made to (a) estimate the extent of genetic diversity in indigenous landraces using both morphological traits  as well as SSR markers genotypic data. (b) To study the genetic structure in a few rice germplasm from NE India.41. The root length, shoot length, fresh root weight and shoot weight data of 45-day-old plants were recorded using PVC pipes following standard method42. Plants were sampled and dried at 80 °C for seven days and root dry weight and shoot dry weight was recorded. The seedling stage drought tolerance score was recorded separately by growing the genotypes in the seedbed of three-row of 2-meter length per genotype in three replication under rain protected condition following Swain et al.43. The observation for drought tolerance was recorded at 32 and 34 days after withdrawal of life-saving irrigation (DAWW) when the soil moisture content was around 7–8% (W/V) and susceptible check (IR64 and Ranjit) showed complete drought stress symptoms using “Standard Evaluation System for Rice (SES)”44. The Soil moisture status was determined by the gravimetric method45.The experimental material for the present investigation comprised of 114 genotypes of rice  for cyclic amplification using the amplification programme Step 1  94 °C for 5 min. Step 2 (Denaturation) 94 °C for 1 min. Step 3  32 °C for 1 min. Step 4 (Extension) 72 °C for 1 min. Step 5  72 °C for 5 min. Step 6 (Storage) 4 °C for infinity. Steps 2, 3 and 4 were repeated 35 times.A total of 140 SSR markers and 30 genes specific (root related traits and aquaporin) SSR markers covering all 12 chromosomes were used to assess the level of genetic diversity among 114 rice accessions. Finally, 65 markers including four gene-specific markers out of 170 SSR markers were chosen for genetic diversity analysis because they were found polymorphic and showed prominent distinguishable banding patterns among genotypes. The genetic sequence of genes related to root traits and drought tolerance factors were downloaded from 48.Amplified products were separated based on their size using 3% agarose gel electrophoresis and 1x TBE buffer in the horizontal electrophoresis tank. The photograph of the gel was digitally documented in Gel Documentation System . The molecular weight of distinct bands or amplified fragments was measured in base pair by comparing with the band size of 100 bp ladder (GeNeI Company) with IR-36 as molecular weight reference49.The root traits and drought tolerance were subjected to analysis of variance (ANOVA), clustering based on the algorithm of unweighted pair group method with arithmetic mean (UPGMA) and Principal Component Analysis (PCA) using R packages50. To derive the optimal number of groups (K), STRUCTURE was run with K varying from 1 to 10, with five runs for each K value. To determine the true value of K, ad hoc statistic ΔK was followed. Parameters were set to 1,00,000 burn-in periods and 5,00,000 Markov Chain Monte Carlo (MCMC) replications after burn-in with an admixture and allele frequencies correlated model. The method described by Evano et al.51 was used to estimate the most probable K value for the analyzed data, using the web tool Structure Harvester ver. 0.6. application52.To identify the genetic structure of the given population and assign individuals to populations, the software STRUCTURE version 2.3.4 was used53. A PIC value of each marker was determined as suggested by Botstein et al.54. Further, the allelic data were subjected to estimation of genetic distances among genotypes using simple matching coefficients by bootstrapping 1000 times and they were clustered using a neighbor-joining method using Darwin software version 6.055. Further, analysis of molecular variance (AMOVA) was performed to describe variance components among individuals and the population differentiation among the seven assumed subpopulations using GeneAlEx 6.502 program53 with 1000 permutations. Principal coordinate analysis (PCoA) was performed to highlight the resolving power of the ordination and the first two components were used to represent the genotypes in the graphical form. PCoA and dissimilarity matrix was performed by using DARwin software version 6.055. Genetic differentiation among the assumed subpopulation was analysed using Nei’s gene diversity statistics using GenAlEx program version 6.502. Venn diagram analysis was performed to identify common varieties between the model-based cluster and neighbor-joining based grouping using online interactive tool Venny 2.156. A Mantel test was done to test similarities of distance and kinship matrices, with 30,000 permutations for a two-tailed Mantel test using R packages57.The number of alleles (N), Ne (Allelic richness) Shannon information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (I)) were determined by using GenAlEx 6.502 programmeAnalysis of variance revealed significant differences among the genotypes for all traits measured in the study Table . UPGMA bPCA analysis revealed that the first principal component with eigenvalue of 5.21 accounted for 40.13% of contribution to total variation and a second component with eigenvalue of 2.0 accounted for 15.38% of contribution to the total variation in the population. These two principal components include fresh shoot weight, root volume, dry shoot weight, fresh root weight, drought score after 32 and 34 DAWW  was observed for RM219 and lowest (0.32) for RM480 with an average of 1.37. The most major frequent allele frequency of 0.90 was observed for the marker RM480 and the lowest (0.35) was observed for RM24 with a mean of 0.61.The polymorphism information content (PIC) of markers along with allele information such as number, size, highest frequency detected among 114 accessions is presented in Table o) ranged from 0 to 0.725 (RM219) with an average of 0.036 across all 65 loci. The majority of the SSR markers exhibited observed heterozygosity as zero, indicating that the majority of rice germplasm used in the present study were pure and completely homozygous for SSR markers used in the present study, which may be the result of the self-pollinated mode of reproduction of rice. Observed heterozygosity (0.041) was far lower than total expected heterozygosity (0.467) which is further supported by low gene flow (Nm) value for the majority of loci, except RM87, RM431, RM333, RM 336 and RM495. The average value of Nm was recorded at 3.264. Expected heterozygosity or gene diversity (He) estimated based on Nei distance varied from 0.118 (RM480) to 0658 (RM219) with an average of 0.334.The population-level genetic diversity of the rice accessions under study is presented in Table In the present study grouping in population was determined using STRUCTURE analysis. The population structure of the 114 genotypes was analyzed by Bayesian clustering model-based approach with admixture and k value ranging from 1 to 10 with 5 iterations using 65 polymorphic markers. The ΔK was found highest for the model parameter K = 3 then for other value of K  based on classification given by Wright58. Among the subpopulations, the P3 was highly differentiated followed by the P2 and P1. This genetic differentiation might be a result of natural selection favouring a different set of alleles in different ecologies and physical barrier which leads to low interchange or migration of alleles between different subpopulations. This is further supported by low gene flow (Nm) value for the majority of loci, except RM566 and RM495 SSR marker gene flow value. The average distance between individuals in P1, P2 and P3 was observed 0.358, 0.323 and 0.240 respectively, which indicated that P1 was the most diverse and less differentiated as compared to P2 and P3 subpopulation.FST values of sub-population range from 0.152 to 0.222 and showed significant differentiation among all the pairs which suggested that all the three groups were significantly different from each other. Based on pairwise FST estimate, P2 and P3 showed the highest level of differentiation from each other and population P1 and population P2 exhibited less differentiation from each other , No of effective alleles (Ne), observed heterozygosity (Ho), gene diversity (He), unbiased expected heterozygosity (uHe) and Wright’s fixation index (F), which is presented in Table The Na, F and Ne were comparable among the three subpopulations Table . The genST) was 0.174, while FIS and FIT were 0.93 and 0.94, respectively. Higher FIS, which is measured at the subgroup level in the whole population, has indicated a lack of heterozygosity and high distinctness of populations, due to the autogamous nature of the crop. Determination of FST has shown high genetic variation among the population.AMOVA was done on population provided by model-based analysis, because of its reliability and consistency to provide detail information about the genetic constitution of the population. AMOVA revealed the presence of 17% of the variation was among populations, whereas, 77% of the variation among individuals and 6% of the variation among individuals within a population (Table Nei genetic distance ranged from 0.190 to 0.333. The maximum distance was observed between Pop 3 and pop 1 (0.333) and minimum distance was observed between pop1 and pop 2 (0.190), indicating that genomic differences between pop 3 and pop1 were more and it was less between pop1 and pop2 Table .Table 8PAn unweighted neighbor-joining tree, based on the alleles detected by 65 SSR markers, showed the genetic relationships among the 114 accessions. Cluster analysis based on the unweighted neighbor-joining clustering method separated the accessions into three main groups along with admixture genotypes spreading over three different clusters  Fig. . Mantel ST analysis with that obtained from model based analysis, PCoA and unweighted neighbor-joining clustering. These revealed that the population under study has high genetic diversity and moderate population structure.A close correspondence was recorded in the results of AMOVA and F60. Therefore the inclusion of diverse valuable genetic base in breeding programmes can play a key role in the improvement of the degree of tolerance against various abiotic and biotic stress damages. The present investigation was aimed to throw some light on genetic variation for root traits and genetic diversity in North East Indian rice genotypes for their effective utilization in the breeding programme.Analysis of genetic relationship among individual is an important component and play a major role in their effective utilization in the crop improvement programme. Genetic diversity study provides knowledge about the level of genetic diversity and genetic structure of the population and serve as a platform for the selection of superior genotypes to be used as parents in crop improvement breeding programmes. The popularization of few improved varieties in a crop like rice among the farming community leads to the narrow genetic base of crop species which results in high susceptibility of crops to various abiotic and biotic stress damagesDendrogram analysis based on root, shoot and drought tolerance traits revealed that the clustering pattern obtained is determined by mainly fresh shoot weight, root volume, dry shoot weight, fresh root weight and drought score traits. Cluster I genotype was characterized by the highest root volume, fresh root weight and dry root weight. Cluster II genotype is characterized by the highest fresh shoot weight and drought tolerance. Cluster IV genotype was characterized by drought susceptibility, root length and bottom root number. Cluster V is characterized by high drought tolerance and cluster 6 is characterized by low value for fresh shoot weight, dry shoot weight, root volume and fresh root weight. Cluster VII is characterized by drought susceptibility and intermediate value for root volume, fresh root weight, fresh shoot weight and dry shoot weight. The PCA analysis revealed that maximum diversity in a population of 114 rice germplasm was governed by fresh shoot weight, root volume, dry shoot weight, fresh root weight, drought score traits. This study revealed sufficient diversity and genotypes identified to be superior for one or more traits from different clusters might be useful in the hybridization programme to identify desirable segregants for the traits under study.62. Diversity analysis using molecular markers is advantageous over the conventional approach based on phenotypic data, as molecular markers provide true information at a genetic level without the influence of environmental effects and provide information about the genetic constitution of genotypes such as which genomic regions or alleles have come from which population63. Genetic diversity study provides not only the phylogenetic relationship but also provide a chance of a finding a new and useful novel alleles present in a diverse set of accessions64.Genetic diversity refers to the presence of contrasting alleles of a gene in different individuals of the same species65. SSR polymorphism analysis revealed an average PIC of 0.507 for 65 markers, which reflected the better discriminatory power of these markers to reveal the higher level of genetic diversity among genotypes and indicated the diverse nature of accessions under study. Similar results for average PIC were reported by Das et al.66 in the landraces of northeast India. Behera et al.67 reported a higher average PIC of 0.811 per locus, this might be due to the use of a more diverse set of rice accession in their study or due to the use of highly polymorphic markers. Shah et al.68 and Pachauri et al.69 have reported mean PIC values 0.37 and 0.38, respectively in sets of 14 improved varieties and 27 landraces of rice collected from different regions/zones of seven Indian states, which were lower to our result. Choudhury et al.70 reported a PIC value of 0.25 in 2630 Assam rice collections and PIC value of 0.23 in the whole northeast rice collection. These all findings indicated that the rice accession used in the present study material has larger genetic diversity. The marker RM 474 and RM 320 have higher discriminatory power to distinguish genotypes due to its high PIC value (0.747) and (0.749). The primer RM 87 and RM 480 showed lower PIC value (0.005) suggesting less discriminatory power of this primer under study.Polymorphism information content (PIC) indicates the informativeness of a marker and allelic diversity of the population. PIC value of 1 indicates that marker is highly polymorphic, and would have an infinite number of alleles, and the marker is more informative, suggesting higher discriminatory power of marker71. In the present study, 65 polymorphic markers detected 147 alleles among 144 rice germplasm with an average of 2.26 alleles per locus. Similarly, Singh et al.72 also reported 112 alleles with an average of 3.11 alleles per locus in 729 varieties using 36 HvSSR markers. Anupam et al.33 also detected only 2–3 alleles in 74 rice germplasms comprised of indigenous landraces and improved variety and breeding lines of Tripura. Pauchauri et al.69 reported 2–4 alleles with an average of 2.7 alleles in a collection of landraces and improved variety. Islam et al.73 detected 2–3 alleles in the aromatic rice of Bangladesh. Whereas, Nachimuthu, et al.74 detected 2–7 alleles with an average of 3 alleles per locus in rice collected from India, South East Asia and America. Choudhary et al.75 reported a higher number of alleles (3.69) per locus with a range of 3 to 7 alleles in 100 major rice cultivars using 52 hyper-variable SSR markers. Das et al.66 also reported a higher number of alleles ranging from 2–11, with an average of 4.91 alleles per locus in Northeast rice germplasm. Edzesi et al.76 reported average 11.3 alleles per locus in China and Vietnam rice accession. Roy et al.77 also detected 2–21 alleles with an average of 8.49 alleles in rice accession comprised of Arunachal Pradesh landraces, basmati rice, local aromatic rice of Meghalaya, aus rice, japonica rice, indica rice. The discrepancy in the number of alleles detected might be due to the use of highly diverse genetic material and polymorphic DNA markers in their study.Allelic richness (Ne) is a measure of genetic diversity and determines the flexibility of the population to adapt to various ecosystemset al.78. Ananadan et al.40 also reported 0.30 gene diversity in 426 ARC accessions, 25 tropical japonica, 57indica landraces, 127 breeding lines. Roy et al.79 reported 0.66 gene diversity in 26 rice accession of Arunachal Pradesh. Genotyping of 409 Asian rice accessions collected from 79 countries of the world using SSR markers revealed the genetic diversity of 0.68, which is higher than the present study observation80. Most of the diversity panel with global accessions have the gene diversity of 0.45 to 0.782. These results on global accessions involving indica, tropical japonica, temperate japonica and wild relatives help to infer that, the diversity in a panel of 114 rice accession collected from North East India represents a considerable proportion of the genetic diversity that exists in the major rice-growing Asian continent. In the present study Nm value for some markers recorded more than 1, which is considered to be high as Nm value in self-pollinated crops lies below one83. The inclusion of sister lines or same landraces with different names or little amount of cross-pollination as farmers some time grows crops vary adjacent to each other might attributed to high Nm value.Gene diversity similar to the present study has been reported by Chen 84.Population structure analysis using model-based analysis is better than the frequentist approach of clustering since model-based clustering is based on Bayesian methods, in which certain parameters like correlated allele frequencies no-prior population information were used as defined in STRUCTURE software. Distance-based methods are usually easy to apply and are often visually appealing such as neighbor-joining to cluster multi-locus genotype data, but difficult to assess how confident one should be that the clusters obtained in this way are meaningful85. The assigning of genotypes to the subpopulation is based on the ancestry threshold of the individual. In the present study threshold of 80% was used for grouping which is more stringent and result in 30 genotypes as an admixture. A similar ancestral threshold of 80% to categorize an individual to a particular subpopulation was used by Salgotra et al.59, and Travis et al.6. However, if the ancestral threshold of 60% has been followed to categorize an individual according to Liakat Ali et al.80, then only 15 individuals have been categorized as an admixture.The model-based Bayesian clustering algorithm approach assigns individuals to subpopulations. Among 114 genotypes 84 genotypes were pure and 30 rice accessions were admixture. The admixture was observed because of gene flow among genotypes through a small amount of natural cross-pollination as farmers grow rice crops adjacent to each other fields. The relatively small value of alpha (α = 0.1022) in the present study reveals that only a few individuals were admixedet al.79 also reported three number of a subpopulation in North East rice collection. Singh et al.72 reported three numbers of groups in Indian rice. Islam et al.73 reported three clusters in aromatic rice accessions. Das et al.66 reported four groups in a 91 rice landraces from Eastern and Northeastern India. Edzesi et al.76 reported seven subgroups in 628 rice accessions from China (507) and Vietnam (121) because of the big population and highly diverse population. Rathi et al.86 have reported ten subgroups in a population of 100 indica rice using 98 SSR markers. Anandan et al.40 grouped 96 rice accessions involving 70 ARC into two groups due to less number of genotypes and low diversity among individuals. In the present study grouping, pattern or number was different from other studies might be attributed to the use of different marker system and a different set of genotypes. In the present study, Banglami, ‘Rongadoria’ and ‘Kola Ahu’ was grouped in different subpopulations and ‘Inglongkiri’ and ‘Mazubiron’ was identified as admixture while Travis et al.6 reported ‘Banglami’, ‘BogaAhu’ and ‘Kola Ahu’ in Aus-1 and ‘Inglongkiri’ and ‘Mazubiron’ in Aus-2. Such differences might be attributed to use of fewer markers in the present study.In the present study, population structure analysis grouped 114 genotypes into three subpopulations named as P1, P2 and P3. Roy The P1 comprised of genotypes like ‘Banglami’, ‘Bizor’, ‘Bizor-2’, ‘Horin Kajuli’ and ‘Raja Ahu’ showing drought tolerance and high recovery rate. ‘Horin Kajuli’ was the best genotype of this group in terms of drought tolerance, root volume, fresh root weight and dry shoot weight. The best performing genotypes in P2 were ‘Kosamoni’ for drought tolerance and Hafa Ahu for root volume, fresh root weight and root length. The P3 consisted of 15 genotypes Fig. , among wet al.87, Islam et al.39, Salgotra et al.59 and Singh et al.72 also reported a higher proportion of variation among individuals in the rice population. All the genotypes of the present study were collected from different regions of Assam covering lowland  and varied upland (Ahu rice) situation, which results in higher variation among individuals than among populations. Within individuals 6% variance was observed, it indicated the high purity of germplasm and has been maintained carefully without any mixture. A very high FIT value has indicated a lack of heterozygosity most likely due to the inbreeding nature of rice . The FST inbreeding coefficient within subpopulations relative to the total provides a measure of the genetic differentiation between subpopulations88. The determination of FST using structure analysis for the subpopulation of the present study was 0.407 which indicated high differentiation between subpopulation because genotypes were collected from a wide range of ecology and topography. Wright58 proposed that values of FST 0.25 explain a very great differentiation between subpopulations; the range of 0.15 to 0.25 indicates moderate differentiation; while differentiation is not negligible if FST is 0.05 or less.Results of AMOVA indicated that there was a higher proportion of variation among individuals and a lower proportion of variation among populations. Similar to this Jasim Aljumaili et al.74.The PCoA analysis showed large genetic diversity and distinctness of populations, the first two principal coordinates explained 11.63 and 9.12% of the variance. A similar pattern of molecular variance was reported by Nachimuthu et al.72. Observed minor differences in a grouping of genotypes could be attributed to the difference in their methodology of grouping, as model-based analysis grouping is based on the Bayesian model approach whereas unweighted neighbor-joining clustering is distance-based approach in which genotypes with admixtures were not considered. The result of model-based analysis was more productive as it provides detail information about the genetic constitution of genotypes which help in the separation of admixture from pure genotypes. The result of Model-based analysis is robust for small population size89. Grouping or clustering of genotypes helps in identification of diverse parents to be used in the hybridization programme to create segregating progenies with maximum genetic variability for further selection90. Therefore the selection of genotypes from different population complimenting for different root traits and drought tolerance help in generating transgressive segregates for root traits and drought tolerance.In all genotypic based clustering patterns of the present study, admixtures were distributed over subpopulation. Groupings of genotypes obtained through model-based analysis, unweighted neighbor-joining clustering and PCoA were incongruent to a large extent indicating real genetic differences among the genotypes under study at DNA level and perfectness of clustering of genotypes. Venn diagram analysis also showed more than 62% of co-linearity between rice germplasm grouping in neighbor-joining clustering and model-based population structure. A similar finding was also reported by Singh 92, but majorities of SSR markers are present in introns and might not affect the trait of interest directly93. Only a few of the SSR markers might be linked to genomic regions which influence the trait of interest. Phenotype is the resultant of genotype and environment effect. At molecular level phenotype is the outcome of many gene expression and interaction. Diversity based on phenotypic traits is influenced by environments35. Another reason could be that only a few markers may be linked with root, shoot and drought tolerance, therefore little variation is detected by the SSR markers in the present study. Farmers have selected land-races based on yield attributes and unknowingly they have selected for drought tolerance and better root system in upland rice germplasm of northeast India. Morphological traits were subjected to farmer’s selection pressure while SSR (DNA) markers were not the target of selection94. These reasons might leads to high chances of no correlation between genotypic and phenotypic based clustering. Silva et al.95 also reported a low correlation (0.35) between genotypic and phenotypic diversity in sorghum. Fufa et al.96 also observed a low correlation between genotypic based clustering and yield attributes based clustering in red winter wheat. Nazaphy et al.97 reported a low correlation of 0.049 between SSR marker-based clustering and phenotypic based clustering. According to Martinez et al.98 correspondence between molecular and phenotypic based diversity might be improved by analysing more numbers of morphological and DNA markers. Silva et al.95 stated that the poor correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program. Hence, the combined use of phenotypic and molecular data is regarded as the best way to identify divergence among genotypes due to their complementary nature.No correlation was observed between genotypic and phenotyping based clustering. which is obvious, because 61 SSR markers used in the present study were random SSR markers, not EST markers. SSR markers are present in both non-coding and coding region of genomeThe present study revealed a high level of genetic diversity among the accession at DNA level, root phenotype and drought tolerance as the landraces of NE India are cultivated by farming community historically for many years in a diversified ecological niches such as near river basin, hills, hill slopes and plains, etc.In this present study, SSR based diversity analysis confirmed the existence of genetic diversity in a population of 114 rice genotypes. Based on various statistical methods, we identified three subpopulations along with 30 admixtures. These three subpopulations were highly differentiated from each other. The majority of variation was observed among individuals. The gene and allele based diversity analysis have indicated the existence of a broad genetic base in this population. The result of the model-based analysis is in close correspondence with the results of the neighbor-joining clustering and PCoA analysis. Thus, the results of this study indicate the scope for utilizing the genetic diversity results in association mapping analysis and selection of diversified genotypes for the development of variety from diversified groups complementing each other for various economical traits.


Ammodytes marinus is by far the most prevalent species of sandeel in the fishery. Management of sandeel in the North Sea plus the Kattegat is currently divided into seven geographical areas, based on subtle differences in demography, population dynamics and results from simulations of larval dispersal. However, little is known about the underlying genetic population structure. In this study, we used 2,522 SNPs derived from restriction site‐associated DNA sequencing (RADseq) typed in 429 fish representing four main sandeel management areas. Our main results showed (a) a lack of a clear spatially defined genetic structure across the majority of genetic markers and (b) the existence of a group of at least 13 SNPs under strong linkage disequilibrium which together separate North Sea sandeel into three haplotype clusters, suggestive of one or more structural variants in the genome. Analyses of the spatial distribution of these putative structural variants suggest at least partial reproductive isolation of sandeel in the western management area along the Scottish coast, supporting a separate management. Our results highlight the importance of the application of a large number of markers to be able to detect weak patterns of differentiation. This study contributes to increasing the genetic knowledge of this important exploited species, and results can be used to improve our understanding of population dynamics and stock structure.Sandeels are an ecologically important group of fishes; they are a key part of the food chain serving as food for marine mammals, seabirds and fish. Sandeels are further targeted by a large industrial fishery, which has led to concern about ecosystem effects. In the North Sea, the lesser sandeel  Collectively, five species of sandeel with partly overlapping distributions can be found in the North Sea  sequencing to develop and analyse genetic markers in samples of lesser sandeel, ea ICES, ; they area ICES, . Lesser ea ICES, . A. mariIn this study, we aimed to (a) characterize the population structure of lesser sandeel in the North Sea using population genomic data and (b) assess whether the management areas currently implemented for the species in the North Sea are in line with observed genetic variability.22.1Ammodytes marinus were collected from 11 sandbank spawning locations during the Danish and Scottish dredge surveys , after a careful exploration of the parameters using an ad hoc simulation script (data not shown), following optimization recommendations from Paris, Stevens, and Catchen  and the FST corrected for the number of individuals (FST’). This function makes use of the functionalities of the R package “hierfstat”  lower than the expected false discovery rate (set to 1%). To explore population structure, we performed a principal component analysis (PCA) using the function dudi.pca from the R package “adegenet” , and an intermediate threshold of LD (ϕ = 4) to extract the maximum number of loci that are in higher LD with each other. We then used data solely for SNPs that were identified by LDna analysis as being highly linked in a second PCA to define haplotype clusters. To describe the genetic make‐up of each cluster identified in the PCA, we estimated heterozygosity and FST across individuals contained within the three main clusters observed (see below). Heterozygosity for this subset of SNPs was estimated as the number of heterozygous genotypes over the total number of loci. We also blasted flanking sequences for the SNPS identified by LDna analysis using the blastn function from ncbi‐blast v.2.6.0+ (parameters: ‐db nr ‐query ‐max_target_seqs 5 –remote)  using the 2.6FST among management areas and collections. To assess the proportion of genomic variation distributed within and among management areas, we performed an analysis of molecular variance (AMOVA)  for both individual collections and management areas, using an ad hoc R script.To explore correspondence between genetic structure and designated management areas, we calculated pairwise 2.7ZMAX), temperature at sea bottom (TSBT), depth of zooplankton maximum (DZMAX) and maximum concentration of zooplankton in the water column (CZMAX). The environmental covariates were extracted from the operational coupled physical–biogeochemical model HBM‐ERGOM set‐up for the North Sea/Baltic Sea area  and ambient temperature conditions. These environmental factors are related to the sandeel population dynamics in the North Sea . No outlier loci were detected using BayeScan, while “pcadapt” highlighted 25 (data not shown). The first and second principal components in the PCA accounted respectively for 1.5% and 0.6% of the total inertia and did not reveal any geographically explicit structure . Hence, cluster 2 in the DAPC analysis corresponded to individuals with admixed ancestry, and clusters 1 and 3 represented genotypes that were pure for one or the other lineage . LDna analysis identified a single cluster of SNPs in high LD . Out of the 13 SNPs, 11 SNPs were fixed, or close to fixation, in at least one of the cluster groups . When the 13 SNPs in strong LD were excluded, heterozygosity was similar among the three clusters . The sandeel sequences from eight out of the 13 linked SNPs identified by LDna analysis blasted against genomic regions of fish species , but 27 loci (approximately 1%) displayed relatively high values of LD . When comparing sampling sites within management areas, FST values ranged from 0 to 0.145 , DW15  and OS15 , and the lowest with its neighbouring location TB16  . When substituting the 2016 samples for the 2015 samples for DS and DW in the AMOVA test, the variance associated with management areas was similar to estimates from samples from 2016, but only showed marginal statistical significance . When only looking at the samples for where we had temporal data in 2015 and 2016 (DS and DW), the variation estimated between years was 1.73% for DW but was not significant (p = .06). The location DS showed no variation between years . Individual genotype proportions within both collections and management areas did not deviate from Hardy–Weinberg Equilibrium proportions (Table Pairwise 5) Table . In the ns Table .TSBT) showed a marginally significant correlation with variation in the 13 high‐LD loci .Analyses of association between genotypes and environmental factors Table  returned4Using SNP markers developed de novo for the current analyses, we here present the first population genetic data for the North Sea keystone species lesser sandeel. Our analysis returned two main results (a) lack of geographically explicit structure across the majority of examined loci and (b) the existence of genetic structure separating individuals into three distinct groups, mainly driven by 13 SNPs in relatively strong linkage disequilibrium (LD).4.1FST cluster 1 vs. cluster 3) which was several orders of magnitude higher with the 13 loci under LD (FST = 0.77) than estimates across all other loci (FST = 0.003). Both LD and FST estimates for the 13 SNPs are close to the values of LD and FST value from genomic regions containing SVs in other marine fishes in the same area , the hypothesized SVs would be ~3 Mb. The average distance between the closest and furthest high‐LD SNP that blasted within the same fish species was ~5 Mbp (Table Two types of SVs can result in these patterns of clustering observed in Figures bp Table . Togethebp Table . Hence, The growing number of studies reporting the presence of SVs shows that SVs may be more widespread than it was originally thought (Wellenreuther & Bernatchez, 4.2FST comparisons. Our genetic results are hence in line with conclusions from Wright, Régnier, Gibb, Augley, and Devalla (While panmixia was inferred from the total SNP data set, analyses of haplotype frequencies of the putative SVs revealed more fine‐scale population structure within the North Sea with potential implications for fisheries management. We found that the area SA4 was the most divergent of all. Particularly, the sample WB16 differed strongly from all other collections. When SA4 samples were excluded from analysis, the variation explained by management areas decreased to statistical nonsignificance, although the easternmost sample in the analysis, from SA2, also showed differentiation from most other collections in the pairwise  Devalla  and Wrig Devalla  who usedIn Atlantic cod, coastal and migratory ecotypes are closely associated with the presence of large SVs (Berg et al., Among the remaining management areas, we found that samples aggregated by fisheries management area did not show higher overall differentiation than individual collections within management areas, suggesting that current management units are not generally associated with diverging genetic profiles. However, the determination of dynamics within and among potential subpopulations in these management areas requires additional analysis as our results are based on a relatively limited number of samples with restricted temporal resolution. It should also be noted that demographic diversity of major importance to fisheries management may not have been reflected in our genetic data, as fisheries management typically operates under the ecological population paradigm while our data mainly reflect evolutionary processes over longer timescales (Waples & Gaggiotti, In the light of the prediction that changing climatic conditions may affect North Sea sandeel productivity negatively (Lindegren et al., Finally, we highlight the importance of using large numbers of markers distributed across the genome to fully characterize the genetic diversity of species and populations. In our case, this allowed us to detect subtle differentiation that otherwise could have been overlooked. This is particularly challenging in species with high gene flow, such as many marine fishes, where low levels of genetic differences across most of the genome can mask genetic divergence of strong functional significance. Thus, our study also serves as an example of the increased power offered by population genomics for conservation and management (e.g. Allendorf, Hohenlohe, & Luikart, None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file.


There is empirical evidence that measured postpartum blood loss has a lognormal distribution. This feature can be used to analyze events of the type ‘blood loss greater than a certain cutoff point’ using a lognormal approach, which takes into account all the quantitative observations, as opposed to dichotomizing the variable blood loss volume into two categories. This lognormal approach uses all the information contained in the data and is expected to provide more efficient estimates of proportions and relative risk when comparing treatments to prevent postpartum haemorrhage. As a consequence, sample size can be reduced in clinical trials, while keeping the statistical precision requirements.The authors illustrate how a lognormal approach can be used in this situation, using data from a clinical trial and the event ‘blood loss greater than 1000 mL’.Estimates of the proportions of this event for each treatment, and relative risks obtained with this method are presented and compared with the standard estimates obtained by dichotomizing measured blood loss volume. An example of how the blood loss distributions of two treatments can be compared is also presented. Different scenarios of the sample size needed to compare two treatments or interventions are presented to illustrate how with the lognormal approach the size of a clinical trial can be reduced.A distributional approach for postpartum blood loss using the lognormal distribution fitted to the data results in more precise estimates of risks of events and relative risks, compared to the use of binomial proportions of events. It also results in reduced required sample size for clinical trials.clinicaltrials.gov (NCT00781066).This paper reports a secondary analysis for a trial that was registered at  The development of an adequate statistical analysis technique to analyze a continuous variable depends on the knowledge of its distribution. Many variables in biology and medicine follow the normal distribution and standard statistical techniques can be applied to compare means. However, when the distribution is not normal, the standard statistical techniques are no longer appropriate and a transformation is often used to normalize the distribution. This has been the case with postpartum blood loss, for which a logarithmic transformation has been used to compare medians,  based onIn the case of postpartum blood loss, there is interest to compare events of the type ‘blood loss beyond a certain cutoff point’ because the loss of large amounts of blood postpartum can lead to severe maternal morbidity and mortality , 6. TherMeasured blood loss is thus categorized in two categories, by means of an indicator variable of blood loss greater than a certain cutoff point. The estimation approach used so far has been to compute the sample proportion of women with blood loss equal to or above the cutoff point, or a binomial proportion. However, the categorization of a dependent variable results in a loss of power to detect true effects, which is substantial if the distribution is highly skewed and if the categorization is done in few categories, or both .We have shown empirically, elsewhere , that thDescriptive histograms by treatment are constructed to have a first view of the distributions.The lognormal approach that we propose uses measured blood loss observations without categorizing this variable. It consists of the following steps:The procedure starts by fitting a three-parameter lognormal distribution  to the dGoodness of fit is assessed using probabilistic plots, consisting in plotting the quantiles of the fitted lognormal distribution against the observed blood loss values. If the fit is good, then the points will fall on a straight line. The probabilistic plot is also used to detect presence of outliers and to assess the quality of the data.Once a lognormal distribution is considered to be a good fit to the data, the fit is visualized by plotting the cumulative distribution function for the observed data  together with the fitted lognormal cumulative distribution function, or, alternatively, its complement, denoted here as the survival function. The “survival” function gives the probability of having blood loss MORE than a particular value. It is R(v) = 1-F(v), where F(v) is the cumulative probability function.The probability of an event of the type ‘blood loss greater than a cutoff point’ is just the survival function at the cutoff point. For example, the proportion of sPPH is just the survival distribution at the point 1000.Comparison of proportions between treatments and computation of relative risks with confidence intervals are done using bootstrap techniques. We generated one thousand bootstrap samples for each treatment. The estimates of the proportions of sPPH and PPH are then computed for each bootstrap sample, for each treatment.  The two To illustrate the gain in precision for this data, we estimated the proportions and relative risk in the standard way, with 95% confidence intervals, denoted the binomial approach. For the proportions, we calculated the width of the 95% confidence intervals for both approaches, the lognormal approach and the binomial approach, and calculated their ratio as a quantification of the gain in precision. For the relative risk we applied a similar procedure but on the relative scale.p-value. To test the equality of the distributions for the two treatments using the lognormal fits, we proceed sequentially: first we test the model with equal scale parameters versus the full model . If the null hypothesis with equal scale parameters is not rejected, we test the model with equal location parameters against the model with possible different location parameters and conclude about whether the distributions differ at, say, 5% level of significance.We also illustrate tests of hypothesis using the two approaches. For the test of equality of the sPPH proportions between the two treatments, using the binomial approach, we report the Pearson chi-square statistic and All the computations for fitting distributions and obtaining estimates were done with JMP® 13 software. Computations for sample size calculations were done with SAS® software version 9.4 (PROC POWER procedure) .In Fig. A three-parameter lognormal distribution  was fitted to the data by maximum likelihood. The third parameter was added because it improved the fit compared to the two-parameter lognormal distribution. The estimated parameters are shown in Table The goodness of fit of the THLN distribution to the data can be visualized in the lognormal probability plot of Fig. n = 98), where the data points are represented by black dots and the pointwise 95% confidence intervals by blue lines. Figure In Fig. Estimation of proportions using number of events divided by the total number in each treatment will be denoted the binomial approach. Estimation of proportions using the survival function at the relevant cut-off point will be denoted by the lognormal approach. We can see in Fig. Table As can be appreciated in Fig. For the lognormal distribution, the relative risk, shown in Table From the comparison of the 95% confidence limits for both approaches, we obtained a log-scale width ratio of 70.4% for the lognormal approach in relation to the binomial approach, so that the gain in precision when using the lognormal instead of the binomial approach, is about 30%.p-value = 0.4440.The test of equality of sPPH proportions by the binomial method can be based on a 2 × 2 table, giving the Pearson chi-square = 0.591 with The lognormal tests of hypothesis that the two distributions are the same is shown in Table The significance level for the final test, comparing the location parameters, is about 0.15 for the lognormal approach. This can be compared to the significance level of the binomial test, 0.44. The lognormal approach seems more sensitive.Figure We present as an example different scenarios of sample size calculation using the two approaches, the binomial and the lognormal one.For the binomial approach, we assume that the proportion of sPPH is 0.015, 0.02 or 0.025 in the current treatment. We also assume that a new preventive therapy is considered worthwhile if the relative risk of sPPH of the new therapy with respect to the current one is no larger than 0.70 or 0.80. We calculated the sample size for the binomial response based on the likelihood ratio chi-square one-sided test for the relative risk statistic.2/p1, we computed the corresponding risk of the competing treatment, p2. For the standard deviation on the log scale, we used the scale parameter obtained from the analysis of three clinical trials, that was in all cases close to s = 0.7. [1-p is the (1-p) quantile (or (1-p)× 100% percentile) of the standard normal distribution, taking values p1 and p2 for a particular scenario. With the two computed means, derived from p1 and p2 using the equation above, and the (fixed) standard deviation, together with the power requirement, the computation of the sample size is straightforward, done as a comparison of means of the log-transformed variable blood loss. SAS PROC POWER with TWOSAMPLEMEANS was used for the computations.For the lognormal approach, we used the well known result that if a variable has a lognormal distribution, its logarithm has a normal distribution. Therefore, our sample size computations were based on the transformation of the volumes to their logarithms. For a scenario consisting of a given proportion of sPPH, say p, and a relative risk RR = ps = 0.7.  The valuThe total sample sizes for the two approaches, for a power of 80%, in a one-sided 5% significance test, are shown in Table We have illustrated how to apply an analysis technique for blood loss volume data based on the lognormal distribution, without categorizing this response variable, for a small trial , verifyiFurthermore, the estimated location and scale parameters from all four of the studies analyzed were very similar . The stuThe available analysis technique to compare proportions of an event of the type ‘blood loss above a certain cut-off point’ between treatments or interventions to prevent this event, has been to estimate the two binomial proportions of sPPH. The categorization of blood loss volume in two categories entails loss of information contained in objectively measured weight or volume data, with a resulting loss of power in tests of hypothesis and a decrease in the precision of the estimates of proportions and relative risks . This faWe proposed a lognormal approach of analysis of postpartum haemorrhage trials aiming to compare events of the type ‘blood loss greater than a certain cutoff point’ between treatments.  We illusAn application of using the lognormal model for the distribution of the blood loss volume is a substantial reduction of sample size of clinical trials, while keeping the statistical power and precision requirements. Using the lognormal approach that we propose, based on fitting a lognormal distribution to the postpartum blood loss data, the objectives of a trial can be attained with smaller sample sizes and reduced cost, through an improvement in the efficiency of the estimation methods. With the lognormal approach, there is a trade-off between the simplicity offered by the binomial approach and the possibility of reducing the size of a trial.Similar methods described in this paper can be used with other variables having the lognormal distribution, like blood pressure  and estiAs an additional bonus to the use of the lognormal approach based on fitting a lognormal distribution to blood loss data, it is possible to test other hypotheses of interest, such as the equality of medians or any other percentile, or even compare the entire distributions between two treatments or interventions.We illustrated how a lognormal approach based on fitting a lognormal distribution to the data can be applied to measured blood loss volume data of a trial. We found that the precision of the estimates of proportions of the event ‘blood loss greater than 1000 mL’ and its comparison between treatments improved compared to the standard methods based on dichotomizing the blood loss variable. We also illustrate how the lognormal approach can be used to compare the distribution parameters for two treatments. When analyzing data using this lognormal approach, sample size of trials can be reduced.