Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

Assessment of genetic diversity, inbreeding and collection completeness of Jersey bulls in the US National Animal Germplasm Program

Genomic selection and extensive use of a few elite bulls through artificial insemination are leading to reduced genetic diversity in Jersey cattle. Conservation of genetic diversity through gene banks can protect a breed's genetic diversity and genetic gain, ensuring continued genetic advancement in the future. The availability of genomic information in the US National Animal Germplasm Program (NAGP) facilitates characterization of Jersey bulls in the germplasm collection. Therefore, in this study, we compared the genetic diversity and inbreeding between Jersey bulls in the NAGP and the national cooperator database (NCD). The NCD is maintained and curated by the Council on Dairy Cattle Breeding (CDCB). We found the genetic diversity to be marginally higher in NAGP (Ho = 0.34 ± 0.17) relative to the NCD population (Ho = 0.33 ± 0.16). The average pedigree and genomic inbreeding (FPED, FGRM, FROH > 2Mb) were similar between the groups, with estimates of 7.6% with FPED, 11.07% with FGRM and 20.13% with FROH > 2Mb. An increasing trend in inbreeding was detected, and a significantly higher level of inbreeding was estimated among the older bulls in the NAGP collection, suggesting an overrepresentation of the genetics from elite bulls. Results from principal component analyses (PCA) provided evidence that the NAGP collection is representative of the genetic variation found in the NCD population and a broad majority of the loci segregating (98.2%) in the NCD population were also segregating in the NAGP. Ward's clustering was used to assess collection completeness of Jerseys in the NAGP by comparison with top 1000 sires of bulls, top 1000 sires of cow, and bulls with high Lifetime Net Merit (NM$). All the clusters were represented in the NAGP suggesting that most of the genetic diversity in the US Jersey population is represented in the NAGP and confirmed the PCA results. The decade of birth was the major driver grouping bulls into clusters, suggesting the importance of selection over time. Selection signature analysis between the historic bulls in the NAGP with the newer bulls, born in the decade after implementation of genomic selection, identified selection for milk production, fat and protein yield, fertility, health, and reproductive traits. Cluster analysis revealed that the NAGP has captured allele frequency changes over time associated with selection, validating the strategy of repeated sampling and suggests that the continuation of a repeated sampling policy is essential for the germplasm collection to maintain its future utility. While NAGP should continue to collect bulls that have large influence on the population due to selection, care should be taken to include the entire breadth of bulls, including low merit bulls.

Exploring the secrets of super-aging: a UK Biobank study on brain health and cognitive function

Communities across the globe are faced with a rapidly aging society, where age is the main risk factor for cognitive decline and development of Alzheimer's and related diseases. Despite extensive research, there have been no successful treatments yet. A rare group of individuals called "super-agers" have been noted to thrive with their exceptional ability to maintain a healthy brain and normal cognitive function even in old age. Studying their traits, lifestyles, and environments may provide valuable insight. This study used a data-driven approach to identify potential super-agers among 7121 UK Biobank participants and found that these individuals have the highest total brain volume, best cognitive performance, and lowest functional connectivity. The researchers suggest a novel hypothesis that these super-agers possess enhanced neural processing efficiency that increases with age and introduce a definition of the "neural efficiency index." Furthermore, several other types of aging were identified and significant structural-functional differences were observed between them, highlighting the benefit of research efforts in personalized medicine and precision nutrition.

Genome-Wide Association Study for Agro-Morphological Traits in Eggplant Core Collection

Eggplant is one of the most economically and nutritionally important vegetables worldwide. The study of the association of phenotypic traits with genetic factors is vital for the rapid and efficient identification and selection of eggplant genetic resources for breeding purposes with desired traits. The eggplant resources (587) collected from different countries, including Korea, were used for establishing the core collection. A total of 288 accessions were selected from 587 Solanum accessions based on 52 single nucleotide polymorphisms (SNPs) markers together with 17 morphological traits. This core collection was further used to analyze the genetic associations of eggplant morphological variations. A large variation was found among the evaluated eggplant accessions for some agro-morphological traits. Stem prickles and leaf prickles showed a significant positive correlation (r = 0.83***), followed by days to flowering and days to maturity (r = 0.64***). A total of 114,981 SNPs were filtered and used for phylogenetic tree analysis, population structure analysis, and genome-wide association study (GWAS). Among the agro-morphological traits, significantly associated SNPs were found for six traits. A total of 377 significantly associated SNPs with six agro-morphological traits were identified. These six traits and the number of SNPs were: days to maturity (51), flower size (121), fruit width (20), harvest fruit color (42), leaf prickles (38), and stem prickles (105). The largest fraction of significant SNPs (11.94%) was obtained on chromosome Ch01, followed by Ch07 and Ch06 with 11.67% and 10.08%, respectively. This study will help to develop markers linked to the most important agro-morphological traits of eggplant genetic resources and support the selection of desirable traits for eggplant breeding programs.

Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Genetic diversity and growth responses of Indonesian tomato (

Tomato (Solanum lycopersicum L.) is cultivated and consumed worldwide, including in Indonesia. It is used in the food, cosmetic, and pharmaceutical industries, due to its high content of carotenoid (lycopene) compounds that have antioxidant and anticancer activities. In Indonesia, although several cultivars of tomato are cultivated, including Opal, Permata, Mutiara, and Rewako, studies on their genetic information are limited. Unpredicted climate change as well as heavy metal contamination, especially Pb pollution, has threatened Indonesian food security. Therefore, our study aimed to analyze the genetic diversity of the four local tomatoes using random amplified polymorphic DNA (RAPD) markers and to determine the growth responses of several local tomato genotypes under Pb stress. In this study, morphological responses to Pb, including plant height and root length were observed. The RAPD analysis showed that Rewako and Permata were distinct, whereas Opal and Mutiara were closely related, possessing 81.8% similarity. Pb stress influenced plant height and root length in the four tomato genotypes, with each genotype exhibiting different morphological responses than others. However, the closely related Mutiara and Opal genotypes demonstrated similar responses to Pb stress to Permata and Rewako. Our study demonstrates that RAPD are sensitive and efficient for elucidating the genomic profile of the tomato genotypes. In addition, our results suggest that genetic variation among tomato genotypes might influence the morphological responses against Pb stress.

Population Structure and Relatedness for Genome-Wide Association Studies

The estimation of the population structure and genetic relatedness between individuals within a collection of accessions is important in the formation of core collections for the conservation of genetic resources, uncovering the demographic history of the population under study, as well as for association studies. With the recent development of high-throughput genotyping technologies, several algorithms and methods have been developed and implemented in software to estimate the extent of genetic diversity between individuals. In this chapter, our objective is to describe methods to capture population structure and relatedness in a step-by-step fashion. To exemplify the process, two pruned datasets (14K and 243K SNP markers) were used to investigate population structure and relatedness among a soybean GWAS panel using different approaches and methods.

Construction of a core collection of native Perilla germplasm collected from South Korea based on SSR markers and morphological characteristics

The leaves and seed oil of Perilla crop (Perilla frutescens L.) have attracted interest as health foods in East Asia. This crop has been traditionally cultivated and used for a long time as a folk plant, especially in Korea. In our study, the 22 SSR markers and eight morphological traits were used to assess the genetic diversity and population structure, to select a core collection of 400 Perilla accessions conserved in the RDA-Genebank of South Korea. A total of 173 alleles were detected and the number of alleles per locus ranged from 4 to 15 (average = 7.9). Gene diversity and polymorphic information content ranged from 0.138 to 0.868 (average = 0.567) and 0.134 to 0.853 (average = 0.522), respectively. The 400 accessions were not clearly distinguished geographically by STRUCTURE and UPGMA analyses. A core collection (44 accessions) was selected from the entire collection by using PowerCore. The core collection accounted for 11.0% of the entire Perilla collection, including 100% of the number of alleles maintained in the whole collection and with similar or greater Shannon-Weaver and Nei diversity indices than the whole collection. The core collection selected by SSR markers was evenly distributed in three clusters on a scatter plot by eight morphological traits. The first core collection of Perilla accessions was constructed, and it maintained allelic richness. Further modification of the core collection is expected with the continuous addition of new accessions of the two cultivated types of Perilla crop and their weedy types.

Population structure and genetic diversity of the Pee Dee cotton breeding program

Accelerated marker-assisted selection and genomic selection breeding systems require genotyping data to select the best parents for combining beneficial traits. Since 1935, the Pee Dee (PD) cotton germplasm enhancement program has developed an important genetic resource for upland cotton (Gossypium hirsutum L.), contributing alleles for improved fiber quality, agronomic performance, and genetic diversity. To date, a detailed genetic survey of the program's eight historical breeding cycles has yet to be undertaken. The objectives of this study were to evaluate genetic diversity across and within-breeding groups, examine population structure, and contextualize these findings relative to the global upland cotton gene pool. The CottonSNP63K array was used to identify 17,441 polymorphic markers in a panel of 114 diverse PD genotypes. A subset of 4597 markers was selected to decrease marker density bias. Identity-by-state pairwise distance varied substantially, ranging from 0.55 to 0.97. Pedigree-based estimates of relatedness were not very predictive of observed genetic similarities. Few rare alleles were present, with 99.1% of SNP alleles appearing within the first four breeding cycles. Population structure analysis with principal component analysis, discriminant analysis of principal components, fastSTRUCTURE, and a phylogenetic approach revealed an admixed population with moderate substructure. A small core collection (n < 20) captured 99% of the program's allelic diversity. Allele frequency analysis indicated potential selection signatures associated with stress resistance and fiber cell growth. The results of this study will steer future utilization of the program's germplasm resources and aid in combining program-specific beneficial alleles and maintaining genetic diversity.

Influence of Environmental Factors on the Genetic and Chemical Diversity of Brickellia veronicifolia Populations Growing in Fragmented Shrublands from Mexico

Brickellia veronicifolia is a native Asteraceae from Mexico that persists in fragmented habitats. This investigation reports the genetic and chemical diversity of B. veronicifolia. The diversity analysis based on iPBS markers showed an averaged Shannon index (S) of 0.3493, a Nei genetic diversity (h) of 0.2256, and a percentage of polymorphic loci average (P) of 80.7867%. The population structure obtained by AMOVA revealed that the highest variation found within populations was 94.58%. GC-MS profiling of six populations indicated that major volatiles were β–caryophyllene (11.63%), spathulenol (12.85%), caryophyllene oxide (13.98%), α–cadinol (7.04%), cubedol (6.72%) and tau-muurolol (4.81%). Mantel tests suggested a statistically significant relationship between minor volatiles and geographical distance (r = 0.6163; p = 0.0470; p ˂ 0.05). Likewise, major volatiles showed a significant correlation with the soil pH (r = 0.6862; p = 0.0230) and maximum temperature (r = 0.4999; p = 0.0280). Our study suggests that the variation and genetic divergence of B. veronicifolia has no relationship with climatic parameters, whereas the volatiles are probably influenced by environmental factors and not by the genotype per se. Based on the characteristics of B. veronicifolia, this plant could be considered as a candidate for restoring fragmented shrublands in Mexico.

Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms

The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.

Deciphering genetic diversity analysis of saffron (Crocus sativus L.) using RAPD and ISSR markers

The existence of genetic diversity in Crocus sativus has globally remained a mystery till date. The study investigated PCR based DNA amplification profile of saffron using ISSR and RAPD based primers. A total of 38 amplicons were generated by ISSR primers in the range from 7 to 12 with an average of 9.50 bands per primer. 20 bands were found to be polymorphic and 18 were monomorphic with an average percentage of polymorphism as 52.48%. RAPD based amplification revealed a total 161 amplicons, 107 as polymorphic and 54 as monomorphic with an average percentage of polymorphism as 66.44%. Cumulative results of RAPD and ISSR demonstrated that Nei-Li’s similarity index ranged between 0.70 and 0.97. The results of AMOVA has revealed 9% of variance among populations and 91% of variance within populations, Φ PT was found as 0.089, which indicates existence of genetic differences though limited. In conclusion, the results indicate that saffron accessions are minimally genetically differentiated, which could be capitalized in future breeding programmes to ameliorate this precious crop.

Assessment of genetic diversity and population structure among a collection of Korean Perilla germplasms based on SSR markers

BACKGROUND

Information on the genetic variation of genetic resource collections is very important for both the conservation and utilization of crop germplasms in genebanks. Var. frutescens of Perilla crop is extensively cultivated in South Korea as both an oil crop and a vegetable crop.

OBJECTIVES

We used SSR markers to evaluate the genetic diversity, genetic relationships, and population structure of 155 accessions of var. frutescens that have been selected as genetic resources for the development of leaf vegetable cultivars and preserved in the RDA-Genebank collection from South Korea.

METHODS

A total of 155 accessions of var. frutescens of Perilla crop collected in South Korea were obtained from the RDA-Genebank of the Republic of Korea. We selected 20 SSR markers representing the polymorphism of and adequately amplifying all the Perilla accessions.

RESULTS

The average GD and PIC values were 0.642 and 0.592, respectively, with ranges of 0.244-0.935 and 0.232- 0.931. The genetic variability in the southern region of South Korea was higher than that in the central region. The clustering patterns were not clearly distinguished between the accessions of var. frutescens from the central and southern regions of South Korea.

CONCLUSION

These results regarding the genetic diversity and population structure of the 155 accessions of var. frutescens of South Korea provide useful information for understanding the genetic variability of this crop and selecting and managing core germplasm sets in the RDA-Genebank of the Republic of Korea.

DArTSeq SNP-based markers revealed high genetic diversity and structured population in Ethiopian cowpea [Vigna unguiculata (L.) Walp] germplasms

Cowpea [Vigna unguiculata (L.) Walp] is one of the important climate-resilient legume crops for food and nutrition security in sub-Saharan Africa. Ethiopia is believed to harbor high cowpea genetic diversity, but this has not yet been efficiently characterized and exploited in breeding. The objective of this study was to evaluate the extent and pattern of genetic diversity in 357 cowpea accestions comprising landraces (87%), breeding lines (11%) and released varieties (2%), using single nucleotide polymorphism markers. The overall gene diversity and heterozygosity were 0.28 and 0.12, respectively. The genetic diversity indices indicated substantial diversity in Ethiopian cowpea landraces. Analysis of molecular variance showed that most of the variation was within in the population (46%) and 44% between individuals, with only 10% of the variation being among populations. Model-based ancestry analysis, the phylogenetic tree, discriminant analysis of principal components and principal coordinate analysis classified the 357 genotypes into three well-differentiated genetic populations. Genotypes from the same region grouped into different clusters, while others from different regions fell into the same cluster. This indicates that differences in regions of origin may not be the main driver determining the genetic diversity in cowpea in Ethiopia. Therefore, differences in sources of origin, as currently distributed in Ethiopia, should not necessarily be used as indices of genetic diversity. Choice of parental lines should rather be based on a systematic assessment of genetic diversity in a specific population. The study also suggested 94 accesstions as core collection which retained 100% of the genetic diversity from the entire collection. This core set represents 26% of the entire collection pinpointing a wide distribution of the diversity within the ethiopian landraces. The outcome of this study provided new insights into the genetic diversity and population structure in Ethiopian cowpea genetic resources for designing effective collection and conservation strategies for efficient utilization in breeding.

Optimization of the genotyping-by-sequencing SNP calling for diversity analysis in cape gooseberry (Physalis peruviana L.) and related taxa

A robust Genotyping-By-Sequencing (GBS) pipeline platform was examined to provide accurate discovery of Single Nucleotide Polymorphisms (SNPs) in a cape gooseberry (Physalis peruviana L.) and related taxa germplasm collection. A total of 176 accessions representing, wild, weedy, and commercial cultivars as well as related taxa from the Colombian germplasm bank and other world repositories were screened using GBS. The pipeline parameters mnLCov of 0.5 and a mnScov of 0.7, tomato and potato genomes, and cape gooseberry transcriptome for read alignments, were selected to better assess diversity and population structure in cape gooseberry and related taxa. A total of 7,425 SNPs, derived from P. peruviana common tags (unique 64 bp sequences shared between selected species), were used. Within P. peruviana, five subpopulations with a high genetic diversity and allele fixation (H_E: 0.35 to 0.36 and F_IS: -0.11 to -0.01, respectively) were detected. Conversely, low genetic differentiation (F_ST: 0.01 to 0.05) was also observed, indicating a high gene flow among subpopulations. These results contribute to the establishment of adequate conservation and breeding strategies for Cape gooseberry and closely related Physalis species.

Molecular and phenotypic diversity of groundnut (Arachis hypogaea L.) cultivars in Togo

Diversity assessment of 94 groundnut accessions from Togo and Senegal, using agro-morphological and SNP markers, revealed high variability for many quantitative traits such as late leaf spot (LLS) incidence, number of pods per plant and yield per plant. For qualitative traits, the Simpson Index showed high diversity for primary seed colour (0.75), stem pigmentation (0.60), and Growth habit (0.59). Principal component analysis underscored quantitative traits such as hundred seed weight, days to maturity, and LLS incidence, as the main traits contributing to the divergence. Correlation and path coefficient analysis showed that the number of pods per plant was the main yield-related trait positively affecting yield (r = 0.95, PC = 0.84; p = 0.01). Overall, 990 SNP markers revealed moderate genetic variability in the genotypes and the percentage of heterozygous genotypes varied from 0 to 50% for all loci. Analysis of molecular variance revealed that only 1.1% of the total molecular variance accounted for geographical contribution to the diversity. Co-analysis of phenotypic and SNP data delineated three clusters harbouring useful alleles and interesting phenotypic features such as LLS resistance, large number of pods per plant and early maturity indicating that differences observed at the phenotypic level are underlined by genotypic differences. The phenotypic and genotypic diversity observed could be exploited for the identification of parents with preferred traits for use in the breeding program. However, the low population structure highlights the necessity to improve groundnut diversity in Togo through introduction from various sources.

StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis

Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations' structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.

Genetic diversity of avocado (Persea americana Mill.) germplasm using pooled sequencing

Background

Discovering a genome-wide set of avocado (Persea americana Mill.) single nucleotide polymorphisms and characterizing the diversity of germplasm collection is a powerful tool for breeding. However, discovery is a costly process, due to loss of loci that are proven to be non-informative when genotyping the germplasm.

Results

Our study on a collection of 100 accessions comprised the three race types, Guatemalan, Mexican, and West Indian. To increase the chances of discovering polymorphic loci, three pools of genomic DNA, one from each race, were sequenced and the reads were aligned to a reference transcriptome. In total, 507,917 polymorphic loci were identified in the entire collection. Of these, 345,617 were observed in all three pools, 117,692 in two pools, 44,552 in one of the pools, and only 56 (0.0001%) were homozygous in the three pools but for different alleles. The polymorphic loci were validated using 192 randomly selected SNPs by genotyping the accessions within each pool. The sensitivity of polymorphic locus prediction ranged from 0.77 to 0.94. The correlation between the allele frequency estimated from the pooled sequences and actual allele frequency from genotype calling of individual accessions was r = 0.8. A subset of 109 SNPs were then used to evaluate the genetic relationships among avocado accessions and the genetic diversity of the collection. The three races were distinctly clustered by projecting the genetic variation on a PCA plot. As expected, by estimating the kinship coefficient for all the accessions, many of the cultivars from the California breeding program were closely related to each other, especially, the Hass-like ones. The green-skin avocados, e.g., ‘Bacon’, ‘Zutano’, ‘Ettinger’ and ‘Fuerte’ were also closely related to each other.

Conclusions

A framework for SNP discovery and genetically characterizing of a breeder‘s accessions was described. Sequencing pools of gDNA is a cost-effective approach to create a genome-wide stock of polymorphic loci for a breeding program. Reassessing the botanical and the genetic knowledge about the germplasm accessions is valuable for future breeding. Kinship analysis may be used as a first step in finding a parental candidates in a parentage analyses.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5672-7) contains supplementary material, which is available to authorized users.

Genotypic Frequencies at Equilibrium for Polysomic Inheritance Under Double-Reduction

Polyploids are organisms whose genomes consist of more than two complete sets of chromosomes. Both autopolyploids and allopolyploids may display polysomic inheritance. A peculiarity of polysomic inheritance is multivalent formation during meiosis resulting in double-reduction, which occurs when sister chromatid fragments segregate into the same gamete. Double-reduction can result in gametes carrying identical-by-descent alleles and slightly increasing homozygosity. This will cause the genotypic frequencies to deviate from expected values and will thus bias the results of standard population genetic analytical methods used in molecular ecology and selective breeding. In this study, we extend existing double-reduction models to account for any even level of ploidy, and derive the symbolic expressions for genotypic frequencies via two methods. Inbreeding coefficients and heterozygosity under double-reduction and inbreeding are also calculated. Numerical solutions obtained by computer simulations are compared with analytical solutions predicted by the model to validate the model.

Analysis of genetic diversity and structure in a worldwide walnut (Juglans regia L.) germplasm using SSR markers

Persian or English walnut (Juglans regia L.), the walnut species cultivated for nut production, is one of the oldest food sources known and is grown worldwide in temperate areas. France is the 7^th leading producer as of 2016 with 39 kt. Deciphering walnut genetic diversity and structure is important for efficient management and use of genetic resources. In this work, 253 worldwide accessions from the INRA walnut germplasm collection, containing English walnut and several related species, were genotyped using 13 SSR (Single Sequence Repeat) markers selected from the literature to assess diversity and structure. Genetic diversity parameters showed a deficiency of heterozygotes and, for several SSRs, allele-specificities among the accessions tested. Principal Coordinate Analysis (PCoA) showed the 253 accessions clustered in largely in agreement with the existing botanical classification of the genus. Among the 217 J. regia accessions, two main clusters, accessions from Eastern Europe and Asia, and accessions from Western Europe and America, were identified using STRUCTURE software. This was confirmed by Principal Coordinate Analysis and supported by Neighbor-Joining tree construction using DARwin software. Moreover, a substructure was found within the two clusters, mainly according to geographical origin. A core collection containing 50 accessions was selected using the maximum length sub-tree method and prior knowledge about their phenotype. The present study constitutes a preliminary population genetics overview of INRA walnut genetic resources collection using SSR markers. The resulting estimations of genetic diversity and structure are useful for germplasm management and for future walnut breeding programs.

Identification of Genomic Regions Contributing to Protein Accumulation in Wheat under Well-Watered and Water Deficit Growth Conditions

Sustaining wheat production under low-input conditions through development and identifying genotypes with enhanced nutritional quality are two current concerns of wheat breeders. Wheat grain total protein content, to no small extent, determines the economic and nutritive value of wheat. Therefore, the objectives of this study are to identify accessions with high and low grain protein content (GPC) under well-watered and water-deficit growth conditions and to locate genomic regions that contribute to GPC accumulation. Spring wheat grains obtained from 2111 accessions that were grown under well-watered and water-deficit conditions were assessed for GPC using near-infrared spectroscopy (NIR). Results indicated significant influences of moisture, genotype, and genotype × environment interaction on the GPC accumulation. Furthermore, genotypes exhibited a wide range of variation for GPC, indicating the presence of high levels of genetic variability among the studied accessions. Around 366 (166 with high GPC and 200 with low GPC) wheat genotypes performed relatively the same across environments, which implies that GPC accumulation in these genotypes was less responsive to water deficit. Genome-wide association mapping results indicated that seven single nucleotide polymorphism (SNPs) were linked with GPC under well-watered growth conditions, while another six SNPs were linked with GPC under water-deficit conditions only. Moreover, 10 SNPs were linked with GPC under both well-watered and water-deficit conditions. These results emphasize the importance of using diverse, worldwide germplasm to dissect the genetic architecture of GPC in wheat and identify accessions that might be potential parents for high GPC in wheat breeding programs.

SSR and morphological trait based population structure analysis of 130 diverse flax (Linum usitatissimum L.) accessions

A total of 130 flax accessions of diverse morphotypes and worldwide origin were assessed for genetic diversity and population structure using 11 morphological traits and microsatellite markers (15 gSSRs and 7 EST-SSRs). Analysis performed after classifying these accessions on the basis of plant height, branching pattern, seed size, Indian/foreign origin into six categories called sub-populations viz. fibre type exotic, fibre type indigenous, intermediate type exotic, intermediate type indigenous, linseed type exotic and linseed type indigenous. The study assessed different diversity indices, AMOVA, population structure and included a principal coordinate analysis based on different marker systems. The highest diversity was exhibited by gSSR markers (SI=0.46; He=0.31; P=85.11). AMOVA based on all markers explained significant difference among fibre type, intermediate type and linseed type populations of flax. In terms of variation explained by different markers, EST-SSR markers (12%) better differentiated flax populations compared to morphological (9%) and gSSR (6%) markers at P=0.01. The maximum Nei's unbiased genetic distance (D=0.11) was observed between fibre type and linseed type exotic sub-populations based on EST-SSR markers. The combined structure analysis by using all markers grouped Indian fibre type accessions (63.4%) in a separate cluster along with the Indian intermediate type (48.7%), whereas Indian accessions (82.16%) of linseed type constituted an independent cluster. These findings were supported by the results of the principal coordinate analysis. Morphological markers employed in the study found complementary with microsatellite based markers in deciphering genetic diversity and population structure of the flax germplasm.

Assessment and Accessibility of Phenotypic and Genotypic Diversity of Carrot (Daucus carota L. var. sativus) Cultivars Commercially Available in the United States

Increased use of intellectual property rights over plant germplasm has led to a complicated landscape for exchange among plant breeders. Our goal was to examine phenotypic and genotypic diversity present in commercially available carrot (Daucus carota L. var. sativus) germplasm in relation to the freedom to operate—the ability for plant breeders to access and use crop genetic diversity. A collection of 140 commercially available carrot cultivars were grown in replicated field trials in the Madison, WI area in 2013 and 2014. Phenotypic measurements were recorded for leaf and root characteristics. Illumina sequencing was used to conduct genotyping by sequencing analysis on all cultivars to understand the range of genetic diversity present. Additionally, the intellectual property rights associated with each cultivar was noted to determine the freedom to operate. We found that although one-third of the commercially available US carrot cultivars in our study are restricted through some form of intellectual property rights, the genetic and phenotypic variability of the protected cultivars does not represent a completely separate group from the available material. Phenotypic analyses including ANOVA and principal components analysis, suggest that many of the traits differed significantly based on market class, but not by whether the cultivar had freedom to operate. The principal components and F_st analyses on the genotyping by sequencing data revealed that carrot market classes (F_st = 0.065) and freedom to operate classes (F_st = 0.023) were not genetically distinct, and that principle components 1 and 2 account for only 10.1% of the total genotypic variation, implying that cultivated carrot germplasm in the US forms an unstructured population. Our findings suggest that the genetic diversity present in carrot cultivars that have freedom to operate is potentially large enough to support carrot breeding efforts in most market classes given present levels of intellectual property protection.

Characterization of Brassica napus L. genotypes utilizing sequence-related amplified polymorphism and genotyping by sequencing in association with cluster analysis

Identifying parental combinations that exhibit high heterosis is a constant target for commercial Brassica napus L. hybrid development programs. Finding high heterotic parental combinations can require hundreds of test crosses and years of yield evaluation. Heterotic pool development could be used to divide breeding material into specific breeding pools and focus the number of parental combinations created. Here, we report the genotypic characterization of 79 B. napus genotypes by calculating genetic distance based on sequence-related amplified polymorphism (SRAP) and genotyping by sequencing (GBS) in association with a neighbour-joining clustering algorithm. Despite the different genotypic analyses, neighbour-joining cluster analysis based on genetic distance of SRAP and GBS produced similar clusters. Homology between SRAP and GBS clusters was approximately 77 % when manually comparing clusters and 68 % when comparing clusters using Compare2Trees. This research demonstrates that SRAP can have similar efficacy when compared to next-generation sequencing technology for heterotic pool classification. This information may provide an important breeding scaffold for the development of hybrid cultivars based upon genetic distance and cluster analysis.

Mango (Mangifera indica L.) germplasm diversity based on single nucleotide polymorphisms derived from the transcriptome

BACKGROUND

Germplasm collections are an important source for plant breeding, especially in fruit trees which have a long duration of juvenile period. Thus, efforts have been made to study the diversity of fruit tree collections. Even though mango is an economically important crop, most of the studies on diversity in mango collections have been conducted with a small number of genetic markers.

RESULTS

We describe a de novo transcriptome assembly from mango cultivar 'Keitt'. Variation discovery was performed using Illumina resequencing of 'Keitt' and 'Tommy Atkins' cultivars identified 332,016 single-nucleotide polymorphisms (SNPs) and 1903 simple-sequence repeats (SSRs). Most of the SSRs (70.1%) were of trinucleotide with the preponderance of motif (GGA/AAG)n and only 23.5% were di-nucleotide SSRs with the mostly of (AT/AT)n motif. Further investigation of the diversity in the Israeli mango collection was performed based on a subset of 293 SNPs. Those markers have divided the Israeli mango collection into two major groups: one group included mostly mango accessions from Southeast Asia (Malaysia, Thailand, Indonesia) and India and the other with mainly of Floridian and Israeli mango cultivars. The latter group was more polymorphic (FS=-0.1 on the average) and was more of an admixture than the former group. A slight population differentiation was detected (FST=0.03), suggesting that if the mango accessions of the western world apparently was originated from Southeast Asia, as has been previously suggested, the duration of cultivation was not long enough to develop a distinct genetic background.

CONCLUSIONS

Whole-transcriptome reconstruction was used to significantly broaden the mango's genetic variation resources, i.e., SNPs and SSRs. The set of SNP markers described in this study is novel. A subset of SNPs was sampled to explore the Israeli mango collection and most of them were polymorphic in many mango accessions. Therefore, we believe that these SNPs will be valuable as they recapitulate and strengthen the history of mango diversity.

Evaluating the Influence of the Microsatellite Marker Set on the Genetic Structure Inferred in Pyrus communis L

Fingerprinting information can be used to elucidate in a robust manner the genetic structure of germplasm collections, allowing a more rational and fine assessment of genetic resources. Bayesian model-based approaches are nowadays majorly preferred to infer genetic structure, but it is still largely unresolved how marker sets should be built in order to obtain a robust inference. The objective was to evaluate, in Pyrus germplasm collections, the influence of the SSR marker set size on the genetic structure inferred, also evaluating the influence of the criterion used to select those markers. Inferences were performed considering an increasing number of SSR markers that ranged from just two up to 25, incorporated one at a time into the analysis. The influence of the number of SSR markers used was evaluated comparing the number of populations and the strength of the signal detected, and also the similarity of the genotype assignments to populations between analyses. In order to test if those results were influenced by the criterion used to select the SSRs, several choosing scenarios based on the discrimination power or the fixation index values of the SSRs were tested. Our results indicate that population structure could be inferred accurately once a certain SSR number threshold was reached, which depended on the underlying structure within the genotypes, but the method used to select the markers included on each set appeared not to be very relevant. The minimum number of SSRs required to provide robust structure inferences and adequate measurements of the differentiation, even when low differentiation levels exist within populations, was proved similar to that of the complete list of recommended markers for fingerprinting. When a SSR set size similar to the minimum marker sets recommended for fingerprinting it is used, only major divisions or moderate (FST>0.05) differentiation of the germplasm are detected.

Applicability of ISSR and DAMD markers for phyto-molecular characterization and association with some important biochemical traits of Dendrobium nobile, an endangered medicinal orchid

Dendrobium nobile is an important medicinal orchid having profound importance in traditional herbal drug preparations and pharmacopeias worldwide. Due to various anthropogenic pressures the natural populations of this important orchid species are presently facing threats of extinction. In the present study, genetic and chemical diversity existing amongst 6 natural populations of D. nobile were assessed using molecular markers, and the influence of genetic factors on its phytochemical activity especially antioxidant potential was determined. Molecular fingerprinting of the orchid taxa was performed using ISSR and DAMD markers along with the estimation of total phenolics, flavonoids and alkaloid contents. Antioxidant activity was also measured using DPPH and FRAP assays which cumulatively revealed a significant level of variability across the sampled populations. The representatives from Sikkim in Northeast India revealed higher phytochemical activity whereas those from Mizoram showed lesser activity. Analysis of molecular variance (AMOVA) revealed that variation amongst the populations was significantly higher than within the populations. The data generated by UPGMA and Bayesian analytical models were compared in order to estimate the genetic relationships amongst the D. nobile germplasm sampled from different geographical areas of Northeast India. Interestingly, identical grouping patterns were exhibited by both the approaches. The results of the present study detected a high degree of existing genetic and phytochemical variation amongst the populations in relation to bioclimatic and geographic locations of populations. Our results strongly establish that the cumulative marker approach could be the best suited for assessing the genetic relationships with high accuracy amongst distinct D. nobile accessions.

Molecular characterization and population structure of the macaw palm, Acrocomia aculeata (Arecaceae), ex situ germplasm collection using microsatellites markers

The Acrocomia aculeata is one of the most promising plants for sustainable production of renewable energy. In order to understand patterns of the distribution of the allelic diversity of A. aculeata ex situ germplasm collection, the present study investigated the hypothesis that the genetic variability of the accessions may match their geographical origin. A genotypic analysis of 77 A. aculeata accessions was conducted with 6 simple sequence repeat markers. A high degree of molecular diversity among the accessions was found, with an average of 9 alleles per locus and a polymorphic information content with a mean of 0.76. A total of 4 clusters was identified by the Bayesian analysis of population structure. The highest subpopulation diversity was identified in Pop1, mainly formed by accessions from State of Mato Grosso do Sul. The populations Pop2A, Pop2B, and Pop2C, all from the State of Minas Gerais, showed high genetic variability as determined by a higher F st, and a wide genetic variance, which were identified within and among the population by analysis of molecular variance. Based on our results and on Vavilov's theory on crop origins, one possible diversity center for A. aculeata is proposed to be in a region in southeast Brazil.

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.

Single-nucleotide polymorphism markers from de-novo assembly of the pomegranate transcriptome reveal germplasm genetic diversity

Pomegranate is a valuable crop that is grown commercially in many parts of the world. Wild species have been reported from India, Turkmenistan and Socotra. Pomegranate fruit has a variety of health-beneficial qualities. However, despite this crop's importance, only moderate effort has been invested in studying its biochemical or physiological properties or in establishing genomic and genetic infrastructures. In this study, we reconstructed a transcriptome from two phenotypically different accessions using 454-GS-FLX Titanium technology. These data were used to explore the functional annotation of 45,187 fully annotated contigs. We further compiled a genetic-variation resource of 7,155 simple-sequence repeats (SSRs) and 6,500 single-nucleotide polymorphisms (SNPs). A subset of 480 SNPs was sampled to investigate the genetic structure of the broad pomegranate germplasm collection at the Agricultural Research Organization (ARO), which includes accessions from different geographical areas worldwide. This subset of SNPs was found to be polymorphic, with 10.7% loci with minor allele frequencies of (MAF<0.05). These SNPs were successfully used to classify the ARO pomegranate collection into two major groups of accessions: one from India, China and Iran, composed of mainly unknown country origin and which was more of an admixture than the other major group, composed of accessions mainly from the Mediterranean basin, Central Asia and California. This study establishes a high-throughput transcriptome and genetic-marker infrastructure. Moreover, it sheds new light on the genetic interrelations between pomegranate species worldwide and more accurately defines their genetic nature.

Genetic structure and a selected core set of Brazilian soybean cultivars

Soybean is one of the most valuable and profitable oil crop species and a thorough knowledge of the genetic structure of this crop is necessary for developing the best breeding strategies. In this study, a representative collection of soybean cultivars recommended for farming in all Brazilian regions was genotyped using 27 simple sequence repeat (SSR) loci. A total of 130 alleles were detected, with an average allelic number of 4.81 per locus. These alleles determined the core set that best represented this soybean germplasm. The Bayesian analysis revealed the presence of two clusters or subgroups within the whole collection (435 soybean cultivars) and the core set (31 entries). Cultivars of similar origin (ancestral) were clustered into the same groups in both analyses. The genetic diversity parameters, based on the SSR loci, revealed high similarity between the whole collection and core set. Differences between the two clusters detected in the core set were attributed more to the frequency of their ancestors than to their genetic base. In terms of ancestry, divergent groups were presented and a panel is shown which may foster efficient breeding programs and aid soybean breeders in planning reliable crossings in the development of new varieties.

Genetic diversity and population structure of Musa accessions in ex situ conservation

BACKGROUND

Banana cultivars are mostly derived from hybridization between wild diploid subspecies of Musa acuminata (A genome) and M. balbisiana (B genome), and they exhibit various levels of ploidy and genomic constitution. The Embrapa ex situ Musa collection contains over 220 accessions, of which only a few have been genetically characterized. Knowledge regarding the genetic relationships and diversity between modern cultivars and wild relatives would assist in conservation and breeding strategies. Our objectives were to determine the genomic constitution based on Internal Transcribed Spacer (ITS) regions polymorphism and the ploidy of all accessions by flow cytometry and to investigate the population structure of the collection using Simple Sequence Repeat (SSR) loci as co-dominant markers based on Structure software, not previously performed in Musa.

RESULTS

From the 221 accessions analyzed by flow cytometry, the correct ploidy was confirmed or established for 212 (95.9%), whereas digestion of the ITS region confirmed the genomic constitution of 209 (94.6%). Neighbor-joining clustering analysis derived from SSR binary data allowed the detection of two major groups, essentially distinguished by the presence or absence of the B genome, while subgroups were formed according to the genomic composition and commercial classification. The co-dominant nature of SSR was explored to analyze the structure of the population based on a Bayesian approach, detecting 21 subpopulations. Most of the subpopulations were in agreement with the clustering analysis.

CONCLUSIONS

The data generated by flow cytometry, ITS and SSR supported the hypothesis about the occurrence of homeologue recombination between A and B genomes, leading to discrepancies in the number of sets or portions from each parental genome. These phenomenons have been largely disregarded in the evolution of banana, as the "single-step domestication" hypothesis had long predominated. These findings will have an impact in future breeding approaches. Structure analysis enabled the efficient detection of ancestry of recently developed tetraploid hybrids by breeding programs, and for some triploids. However, for the main commercial subgroups, Structure appeared to be less efficient to detect the ancestry in diploid groups, possibly due to sampling restrictions. The possibility of inferring the membership among accessions to correct the effects of genetic structure opens possibilities for its use in marker-assisted selection by association mapping.

Molecular characterization and genetic structure of Quercus acutissima germplasm in China using microsatellites

Quercus acutissima is native to eastern Asia. It has a wide distribution in China and China is an important component in understanding the ecology and genetic structure of this species. Q. acutissima attained high economic value for hardwood product and can be managed as an energy tree species. To investigate the genetic variation of Q. acutissima provenances, 12 microsatellite primer pairs were used to analyze 672 trees sampled from 28 provenances of Q. acutissima in China. All of the tested microsatellite loci proved to be effective for the studied Q. acutissima provenances. The results revealed that allele numbers varied from 5 to 13 per locus, with an average of 8 alleles per locus. The mean observed heterozygosity and expected heterozygosity were 0.4927 and 0.7023, respectively. The relatedness of the provenances was studied using the arithmetic mean algorithm based on Nei's genetic distance and principal coordinates analysis. Interestingly, both approaches revealed two main groups: one consisted of the eastern Chinese provenances, and the other comprised of the western Chinese provenances. An analysis of molecular variance indicated that most genetic variation was contained within populations (84 %). The two microsatellite markers developed in this study may be employed for genetic characterization of other oak species. Considering the management or breeding programs of Q. acutissima provenances in China, we should treat each main group as a single gene resource.

Maximizing genetic differentiation in core collections by PCA-based clustering of molecular marker data

Developing genetically diverse core sets is key to the effective management and use of crop genetic resources. Core selection increasingly uses molecular marker-based dissimilarity and clustering methods, under the implicit assumption that markers and genes of interest are genetically correlated. In practice, low marker densities mean that genome-wide correlations are mainly caused by genetic differentiation, rather than by physical linkage. Although of central concern, genetic differentiation per se is not specifically targeted by most commonly employed dissimilarity and clustering methods. Principal component analysis (PCA) on genotypic data is known to effectively describe the inter-locus correlations caused by differentiation, but to date there has been no evaluation of its application to core selection. Here, we explore PCA-based clustering of marker data as a basis for core selection, with the aim of demonstrating its use in capturing genetic differentiation in the data. Using simulated datasets, we show that replacing full-rank genotypic data by the subset of genetically significant PCs leads to better description of differentiation and improves assignment of genotypes to their population of origin. We test the effectiveness of differentiation as a criterion for the formation of core sets by applying a simple new PCA-based core selection method to simulated and actual data and comparing its performance to one of the best existing selection algorithms. We find that although gains in genetic diversity are generally modest, PCA-based core selection is equally effective at maximizing diversity at non-marker loci, while providing better representation of genetically differentiated groups.

Genetic structure in cultivated grapevines is linked to geography and human selection

BACKGROUND

Grapevine (Vitis vinifera subsp. vinifera) is one of the most important and ancient horticultural plants in the world. Domesticated about 8-10,000 years ago in the Eurasian region, grapevine evolved from its wild relative (V. vinifera subsp. sylvestris) into very diverse and heterozygous cultivated forms. In this work we study grapevine genetic structure in a large sample of cultivated varieties, to interpret the wide diversity at morphological and molecular levels and link it to cultivars utilization, putative geographic origin and historical events.

RESULTS

We analyzed the genetic structure of cultivated grapevine using a dataset of 2,096 multi-locus genotypes defined by 20 microsatellite markers. We used the Bayesian approach implemented in the STRUCTURE program and a hierarchical clustering procedure based on Ward's method to assign individuals to sub-groups. The analysis revealed three main genetic groups defined by human use and geographic origin: a) wine cultivars from western regions, b) wine cultivars from the Balkans and East Europe, and c) a group mainly composed of table grape cultivars from Eastern Mediterranean, Caucasus, Middle and Far East countries. A second structure level revealed two additional groups, a geographic group from the Iberian Peninsula and Maghreb, and a group comprising table grapes of recent origins from Italy and Central Europe. A large number of admixed genotypes were also identified. Structure clusters regrouped together a large proportion of family-related genotypes. In addition, Ward's method revealed a third level of structure, corresponding either to limited geographic areas, to particular grape use or to family groups created through artificial selection and breeding.

CONCLUSIONS

This study provides evidence that the cultivated compartment of Vitis vinifera L. is genetically structured. Genetic relatedness of cultivars has been shaped mostly by human uses, in combination with a geographical effect. The finding of a large portion of admixed genotypes may be the trace of both large human-mediated exchanges between grape-growing regions throughout history and recent breeding.

Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation

Definition of clear criteria for evaluation of the quality of core collections is a prerequisite for selecting high-quality cores. However, a critical examination of the different methods used in literature, for evaluating the quality of core collections, shows that there are no clear guidelines on the choices of quality evaluation criteria and as a result, inappropriate analyses are sometimes made leading to false conclusions being drawn regarding the quality of core collections and the methods to select such core collections. The choice of criteria for evaluating core collections appears to be based mainly on the fact that those criteria have been used in earlier publications rather than on the actual objectives of the core collection. In this study, we provide insight into different criteria used for evaluating core collections. We also discussed different types of core collections and related each type of core collection to their respective evaluation criteria. Two new criteria based on genetic distance are introduced. The consequences of the different evaluation criteria are illustrated using simulated and experimental data. We strongly recommend the use of the distance-based criteria since they not only allow the simultaneous evaluation of all variables describing the accessions, but they also provide intuitive and interpretable criteria, as compared with the univariate criteria generally used for the evaluation of core collections. Our findings will provide genebank curators and researchers with possibilities to make informed choices when creating, comparing and using core collections.

Outlier Loci and Selection Signatures of Simple Sequence Repeats (SSRs) in Flax (Linum usitatissimum L.)

Genomic microsatellites (gSSRs) and expressed sequence tag-derived SSRs (EST-SSRs) have gained wide application for elucidating genetic diversity and population structure in plants. Both marker systems are assumed to be selectively neutral when making demographic inferences, but this assumption is rarely tested. In this study, three neutrality tests were assessed for identifying outlier loci among 150 SSRs (85 gSSRs and 65 EST-SSRs) that likely influence estimates of population structure in three differentiated flax sub-populations (F_ST = 0.19). Moreover, the utility of gSSRs, EST-SSRs, and the combined sets of SSRs was also evaluated in assessing genetic diversity and population structure in flax. Six outlier loci were identified by at least two neutrality tests showing footprints of balancing selection. After removing the outlier loci, the STRUCTURE analysis and the dendrogram topology of EST-SSRs improved. Conversely, gSSRs and combined SSRs results did not change significantly, possibly as a consequence of the higher number of neutral loci assessed. Taken together, the genetic structure analyses established the superiority of gSSRs to determine the genetic relationships among flax accessions, although the combined SSRs produced the best results. Genetic diversity parameters did not differ statistically (P > 0.05) between gSSRs and EST-SSRs, an observation partially explained by the similar number of repeat motifs. Our study provides new insights into the ability of gSSRs and EST-SSRs to measure genetic diversity and structure in flax and confirms the importance of testing for the occurrence of outlier loci to properly assess natural and breeding populations, particularly in studies considering only few loci.

Comparison of genetic diversity structure analyses of SSR molecular marker data within apple (Malus×domestica) genetic resources

The aim of this study was to compare traditional hierarchical clustering techniques and principal coordinate analysis (PCoA) with the model-based Bayesian cluster analyses in relation to subpopulation differentiation based on breeding history and geographical origin of apple (Malus×domestica Borkh.) cultivars and landraces. We presented the use of a set of 10 microsatellite (SSR) loci for genetic diversity structure analyses of 273 apple accessions from national genetic resources. These SSR loci yielded a total of 113 polymorphic SSR alleles, with 5-18 alleles per locus. SSR molecular data were successfully used in binary and allelic input format for all genetic diversity analyses, but allelic molecular data did not reveal reliable results with the NTSYS-pc and BAPS softwares. A traditional cluster analysis still provided an easy and effective way for determining genetic diversity structure in the apple germplasm collection. A model-based Bayesian analysis also provided the clustering results in accordance to traditional cluster analysis, but the analyses were distorted by the presence of a dominant group of apple genetic resources owing to the narrow origin of the apple genome. PCoA confirmed that there were no noticeable differences in genetic diversity structure of apple genetic resources during the breeding history. The results of our analyses are useful in the context of enhancing apple collection management, sampling of core collections, and improving breeding processes.