Single Nucleotide Polymorphism Datasets Research Articles

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson’s disease

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson’s disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Genomic inbreeding coefficients using imputation genotypes: assessing the effect of ancestral genotyping in Holstein-Friesian dairy cows

The objective of this study was to assess the effect of using or not the genotypes of the parents of a cow for imputing single nucleotide polymorphisms (SNP), on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown and Jersey Breeders (ANAFIBJ) were analyzed. Cows were genotyped with the HD Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the MD GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD and the Labogena MD. To assess differences among estimators genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1, 2, 3), 2 genomic relationship matrix (grm) based estimators (Fgrm and Fgrm2; with the latter including also pedigree information) and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNP, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNP or the SNP after imputation was made. Information on the presence or absence of genotypic information from sire, dam and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNP or SNP after imputation were consistent for F, Fhat3, Fgrm2 and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNP were observed in cows that were genotyped with MD SNP panels whose SNP were poorly represented in the selected imputation SNP data set and also did not have their parents genotyped compared with what expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2 and Fgrm provided higher genomic inbreeding coefficients of imputation SNP even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNP selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNP. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH based estimator.

Influence of molecular marker type on estimating effective population size and other genetic parameters in a critically endangered parrot

AbstractGenetics is a fast‐moving field, and for conservation practitioners or ecologists, it can be bewildering. The choice of marker used in studies is fundamental; in the literature, preference has recently shifted from microsatellites to single nucleotide polymorphism (SNP) loci. Understanding how marker type affects estimates of population genetic parameters is important in the context of conservation, especially because the accuracy of estimates has a bearing on the actions taken to protect threatened species. We compare parameter estimates between seven microsatellites, 3761 SNP loci, and a random subset of 100 SNPs for the exact same 324 individual swift parrots, Lathamus discolor, and also use 457 additional samples from subsequent years to compare SNP estimates. Both marker types estimated a lower HO than HE. We show that microsatellites and SNPs mainly indicate a lack of spatial genetic structure, except when a priori collection locations were used on the SNP data in a discriminant analysis of principal components (DAPC). The 100‐SNP subset gave comparable results to when the full dataset was used. Estimates of effective population size (Ne) were comparable between markers when the same individuals were considered, but SNPs had narrower confidence intervals. This is reassuring because conservation assessments that rely on population genetic estimates based on a few microsatellites are unlikely to be nullified by the general shift toward SNPs in the literature. However, estimates between markers and datasets varied considerably when only adult samples were considered; hence, including samples of all age groups is recommended to be used when available. The estimated Ne was higher for the full SNP dataset (2010–2019) than the smaller comparison data (2010–2015), which might be a better reflection of the species status. The lower precision of microsatellites may not necessarily be a barrier for most conservation applications; however, SNPs will improve confidence limits, which may be useful for practitioners.

Drought-induced growth phenotypes are associated with genetic variation across a white pine hybrid zone.

Understanding relationships among warming climate, increased drought severity, and the genetic architecture of hybrid drought resilience is necessary for forest conservation and management. We calculated three drought-related tree-ring-growth indices (dendrophenotypes) using tree-ring data from hybrid P. strobiformis – P. flexilis study trees at nine sites across Colorado, New Mexico, Arizona, and Texas. Along with hybrid index (percentage of P. strobiformis ancestry inherited by a single tree), and climate variables, we used dendrophenotypes to (Q1) examine relationships among climate and hybrid index, (Q2) examine relationships between dendrophenotypes and hybrid index, and (Q3) conduct a genotype-phenotype analysis. We observed significant correlation between hybrid index and dendrophenotypes resulting from high-temperature drought in addition to significant correlation between those dendrophenotypes and our dataset of single-nucleotide polymorphisms (SNPs). We conclude that P. strobiformis – P. flexilis trees exhibiting higher hybrid indices are more resilient to high-temperature drought events and encourage future research that identifies genetic linkage between relevant loci and their conferred physiological benefits. 

A new wild emmer wheat panel allows to map new loci associated with resistance to stem rust at seedling stage.

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is a major wheat disease worldwide. A collection of 283 wild emmer wheat [Triticum turgidum L. subsp. dicoccoides (Körn. ex Asch. & Graebn.) Thell] accessions, representative of the entire Fertile Crescent region where wild emmer naturally occurs, was assembled, genotyped, and characterized for population structure, genetic diversity, and rate of linkage disequilibrium (LD) decay. Then, the collection was employed for mapping Pgt resistance genes, as a proof of concept of the effectiveness of genome-wide association studies in wild emmer. The collection was evaluated in controlled conditions for reaction to six common Pgt pathotypes (TPMKC, TTTTF, JRCQC, TRTTF, TTKSK/Ug99, and TKTTF). Most resistant accessions originated from the Southern Levant wild emmer lineage, with some showing a resistance reaction toward three to six tested races. Association analysis was conducted considering a 12K polymorphic single-nucleotide polymorphisms dataset, kinship relatedness between accessions, and population structure. Eleven significant marker-trait associations (MTA) were identified across the genome, which explained from 17% to up to 49% of phenotypic variance with an average 1.5 additive effect (based on the 1-9 scoring scale). The identified loci were either effective against single or multiple races. Some MTAs colocalized with known Pgt resistance genes, while others represent novel resistance loci useful for durum and bread wheat prebreeding. Candidate genes with an annotated function related to plant response to pathogens were identified at the regions linked to the resistance and defined according to the estimated small LD (about 126kb), as typical of wild species.

Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection.

The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen.

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax.

Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola. In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance. We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs. Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.

The genome-wide meiotic recombination landscape in ciliates and its implications for crossover regulation and genome evolution

Meiotic recombination is essential for sexual reproduction and its regulation has been extensively studied in many taxa. However, genome-wide recombination landscape has not been reported in ciliates and it remains unknown how it is affected by the unique features of ciliates: the synaptonemal complex (SC)-independent meiosis and the nuclear dimorphism. Here, we show the recombination landscape in the model ciliate Tetrahymena thermophila by analyzing single-nucleotide polymorphism datasets from 38 hybrid progeny. We detect 1021 crossover (CO) events (35.8 per meiosis), corresponding to an overall CO rate of 9.9 cM/Mb. However, gene conversion by non-crossover is rare (1.03 per meiosis) and not biased towards G or C alleles. Consistent with the reported roles of SC in CO interference, we find no obvious sign of CO interference. CO tends to occur within germ-soma common genomic regions and many of the 44 identified CO hotspots localize at the centromeric or subtelomeric regions. Gene ontology analyses show that CO hotspots are strongly associated with genes responding to environmental changes. We discuss these results with respect to how nuclear dimorphism has potentially driven the formation of the observed recombination landscape to facilitate environmental adaptation and the sharing of machinery among meiotic and somatic recombination.

Lifting of the 1,000 wheat exome project SNPs from Triticum aestivum cv. Chinese Spring assembly RefSeq v1.0 to RefSeq v2.1

ObjectiveThe 1,000 wheat exome project captured the single nucleotide variants in the coding regions of a diverse set of 890 wheat accessions to analyse the contribution of introgression to adaptation of wheat. However, this highly useful single nucleotide polymorphism (SNP) dataset is based on RefSeq v1.0 of the International Wheat Genome Sequencing Consortium (IWGSC) assembly of the bread wheat genome of Chinese Spring. This reference sequence has recently been updated using optical maps and long-read sequencing to produce the improved RefSeq v2.1. Our objective was to develop a reliable high-density SNP dataset positioned onto RefSeq v2.1 because it is the current standard reference sequence used by wheat researchers.ResultsThe 3,039,822 SNPs originally positioned on RefSeq v1.0 were projected to v2.1 using Liftoff with four different flanking regions, and 2,946,536 SNPs were consistently lifted to the same location irrespective of the flanking region lengths. Of these, 2,799,166 were located on the ‘+’ ve strand. The distribution of the SNPs across the 21 chromosomes on RefSeq v2.1 was similar to that of RefSeq v1.0. Among the SNPs that were based on unanchored scaffolds in RefSeq v1.0, 11,938 were projected to one of the 21 pseudomolecules in the upgraded assembly. This SNP dataset constitutes a much-needed standardized resource for the wheat research community.

Genetic diversity and signature of divergence in the genome of grapevine clones of Southern Italy varieties.

Sexual reproduction has contributed to a significant degree of variability in cultivated grapevine populations. However, the additional influence of spontaneous somatic mutations has played a pivotal role in shaping the diverse landscape of grapevine agrobiodiversity. These naturally occurring selections, termed 'clones,' represent a vast reservoir of potentially valuable traits and alleles that hold promise for enhancing grape quality and bolstering plant resilience against environmental and biotic challenges. Despite their potential, many of these clones remain largely untapped.In light of this context, this study aims to delve into the population structure, genetic diversity, and distinctive genetic loci within a collection of 138 clones derived from six Campanian and Apulian grapevine varieties, known for their desirable attributes in viticulture and winemaking. Employing two reduced representation sequencing methods, we extracted Single-Nucleotide Polymorphism (SNP) markers. Population structure analysis and fixation index (FST) calculations were conducted both between populations and at individual loci. Notably, varieties originating from the same geographical region exhibited pronounced genetic similarity.The resulting SNP dataset facilitated the identification of approximately two hundred loci featuring divergent markers (FST ≥ 0.80) within annotated exons. Several of these loci exhibited associations with essential traits like phenotypic adaptability and environmental responsiveness, offering compelling opportunities for grapevine breeding initiatives. By shedding light on the genetic variability inherent in these treasured traditional grapevines, our study contributes to the broader understanding of their potential. Importantly, it underscores the urgency of preserving and characterizing these valuable genetic resources to safeguard their intra-varietal diversity and foster future advancements in grapevine cultivation.

A review of machine learning models applied to genomic prediction in animal breeding.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

Speciation patterns of related species under the hybrid zone: A case study of three sclerophyllous oaks in the east Himalaya-Hengduan Mountains.

Speciation is often accompanied by frequent gene exchanges, which have been gradually recognized as a common phenomenon in nature. Although gene flow may influence different reproductive isolations, the specific mechanism of the process still requires more experimental evidence, especially in hybrid populations that have no significant differentiation and isolation. To address this challenge, this study aims to elucidate the underlying mechanisms of sympatry and parapatry in related species. Three sclerophyllous oaks (Quercus spinosa, Quercus aquifolioides and Quercus rehderiana), which are mainly distributed in the sympatry/parapatry of the East Himalaya-Hengduan Mountains and adjacent regions, were used to explore the population dynamics and evolutionary history. Based on 12,420 genome-wide single nucleotide polymorphism datasets, gene flow detection showed that the three species did not present any obvious genetic barriers. Evolutionary analysis indicated that the three species diverged during the Tertiary Period, and no migration events occurred in the early stages of species divergence. Combined with the data of 19 ecological factors, geological movements and climatic turbulence caused the rapid radiated differentiation of the three species during the Neocene, and similar selective pressures resulted in the same evolutionary pattern based on demographic history analysis. In addition, the predicted niche occupancy profiles and Generalized Dissimilarity Modelling revealed that the three species occupied distinct niches and had significant differences in ecological adaptation, which may explain the specific morphological characteristics of the different species. Therefore, we believe that the populations of the three related species underwent adaptive evolution in different habitats during the early stages of divergence. This study provides new experimental evidence of the formation patterns of parallel speciation.

Tufted Puffins exhibit low levels of genetic differentiation among breeding colonies in North America

Abstract Tufted Puffins (Fratercula cirrhata) are experiencing population declines in some parts of their distribution, making this a species of increased conservation interest. Genetic data will help to identify Tufted Puffin populations of conservation importance and provide an important tool for developing conservation management plans. This species is broadly distributed across the North Pacific Ocean but little is known about the extent of genetic variation and differentiation across their range. In this study, we examine mitochondrial DNA (mtDNA), 8 microsatellite loci and 1,260 single nucleotide polymorphisms (SNPs) to determine the extent of gene flow among 7 breeding colonies (Oregon to the western Aleutians) in the North American breeding range of the species and identify potential barriers to dispersal. Our results show that most breeding colonies form a single genetic cluster, and mtDNA data show substantial historical gene flow among populations. For the microsatellite dataset, all FST comparisons that include St. Lazaria, in southeast Alaska, except Oregon, which had a small sample size, were significant as were comparisons between Triangle Island and the two westernmost sampling sites of Buldir and Aiktak. For the SNP dataset, FST comparisons were low and nonsignificant, further suggesting that breeding colonies form a single panmictic population. Individuals were more closely related to individuals from the same colony, and we found a weak relationship between genetic and geographic distance. This suggests that dispersal among colonies is high, likely facilitated by an overlap in wintering ranges among colonies. The high connectivity among breeding colonies indicates that Tufted Puffins form a single conservation unit, although future genetic studies should incorporate a whole genome sequencing approach to assessing how functional genetic diversity varies across their distribution.

Using conservation genetics to prioritise management options for an endangered songbird

Genetic data can be highly informative for answering questions relevant to practical conservation efforts, but remain one of the most neglected aspects of species recovery plans. Framing genetic questions with reference to practical and tractable conservation objectives can help bypass this limitation of the application of genetics in conservation. Using a single-nucleotide polymorphism dataset from reduced-representation sequencing (DArTSeq), we conducted a genetic assessment of remnant populations of the endangered forty-spotted pardalote (Pardalotus quadragintus), a songbird endemic to Tasmania, Australia. Our objectives were to inform strategies for the conservation of genetic diversity in the species and estimate effective population sizes and patterns of inter-population movement to identify management units relevant to population conservation and habitat restoration. We show population genetic structure and identify two small populations on mainland Tasmania as ‘satellites’ of larger Bruny Island populations connected by migration. Our data identify management units for conservation objectives relating to genetic diversity and habitat restoration. Although our results do not indicate the immediate need to genetically manage populations, the small effective population sizes we estimated for some populations indicate that they are vulnerable to genetic drift, highlighting the urgent need to implement habitat restoration to increase population size and to conduct genetic monitoring. We discuss how our genetic assessment can be used to inform management interventions for the forty-spotted pardalote and show that by assessing contemporary genetic aspects, valuable information for conservation planning and decision-making can be produced to guide actions that account for genetic diversity and increase chances of recovery in species of conservation concern.

Genetic Adaptation of Siberian Larch (Larix sibirica Ledeb.) to High Altitudes

Forest trees growing in high altitude conditions offer a convenient model for studying adaptation processes. They are subject to a whole range of adverse factors that are likely to cause local adaptation and related genetic changes. Siberian larch (Larix sibirica Ledeb.), whose distribution covers different altitudes, makes it possible to directly compare lowland with highland populations. This paper presents for the first time the results of studying the genetic differentiation of Siberian larch populations, presumably associated with adaptation to the altitudinal gradient of climatic conditions, based on a joint analysis of altitude and six other bioclimatic variables, together with a large number of genetic markers, single nucleotide polymorphisms (SNPs), obtained from double digest restriction-site-associated DNA sequencing (ddRADseq). In total, 25,143 SNPs were genotyped in 231 trees. In addition, a dataset of 761 supposedly selectively neutral SNPs was assembled by selecting SNPs located outside coding regions in the Siberian larch genome and mapped to different contigs. The analysis using four different methods (PCAdapt, LFMM, BayeScEnv and RDA) revealed 550 outlier SNPs, including 207 SNPs whose variation was significantly correlated with the variation of some of environmental factors and presumably associated with local adaptation, including 67 SNPs that correlated with altitude based on either LFMM or BayeScEnv and 23 SNPs based on both of them. Twenty SNPs were found in the coding regions of genes, and 16 of them represented non-synonymous nucleotide substitutions. They are located in genes involved in the processes of macromolecular cell metabolism and organic biosynthesis associated with reproduction and development, as well as organismal response to stress. Among these 20 SNPs, nine were possibly associated with altitude, but only one of them was identified as associated with altitude by all four methods used in the study, a nonsynonymous SNP in scaffold_31130 in position 28092, a gene encoding a cell membrane protein with uncertain function. Among the studied populations, at least two main groups (clusters), the Altai populations and all others, were significantly genetically different according to the admixture analysis based on any of the three SNP datasets as follows: 761 supposedly selectively neutral SNPs, all 25,143 SNPs and 550 adaptive SNPs. In general, according to the AMOVA results, genetic differentiation between transects or regions or between population samples was relatively low, although statistically significant, based on 761 neutral SNPs (FST = 0.036) and all 25,143 SNPs (FST = 0.017). Meanwhile, the differentiation based on 550 adaptive SNPs was much higher (FST = 0.218). The data showed a relatively weak but highly significant linear correlation between genetic and geographic distances (r = 0.206, p = 0.001).

Reproductive isolation between diploid and tetraploid individuals in mixed‐cytotype populations of Lycium australe

Whole-genome duplication is considered a major mechanism of sympatric speciation due to the creation of strong and instantaneous reproductive barriers. Although postzygotic reproductive isolation between diploids and polyploids is often expected, the extent of reproductive incompatibility must be empirically determined and compared to patterns of genetic isolation to fully characterize the reproductive dynamics between cytotypes. We investigated reproductive compatibility between diploid and tetraploid Lycium australe in two mixed-cytotype populations using (1) controlled crossing experiments to evaluate fruit and seed production and (2) germination trials to test seed viability following homoploid and heteroploid crosses. We contrast these experiments with a single-nucleotide polymorphism (SNP) data set to measure genetic isolation between cytotypes and explore whether cytotype or population origin better explains patterns of genetic variation. Finally, we explore mating patterns using the observed germination rates of naturally produced seeds in each population. Although homoploid and heteroploid crosses resulted in similar fruit and seed production, reproductive isolation between co-occurring diploids and tetraploids was nearly complete, due to low seed viability following heteroploid crosses. Of 191,182 total SNPs, 21,679 were present in ≥90% of individuals and replicate runs using unlinked SNPs revealed strong clustering by cytotype and differentiation of tetraploids based on population origin. As often reported, diploid and tetraploid L. australe experience strong postzygotic isolation via hybrid seed inviability. Consistent with this result, cytotype explained a greater amount of variation in the SNP data set than population origin, despite some evidence of historical introgression.

Diversity and enrichment of breeding material for resilience in European forests

Delivering material selected for breeding purposes into the wild in the context of sustainable forest management might reduce the levels of genetic diversity of future forests in comparison to that of natural populations. Another consequence might be a reduction of their resilience under uncertain future climatic and socio-economic conditions if these new populations lack adaptability. Despite the long tradition of breeding activities in Europe, there is still a need to assess the impact of genetically enriched material on forests’ resilience. In this study, we address (1) the genetic diversity of selected material compared to its wild ancestors, and (2) how to enrich breeding material to support forests’ resilience under changing socio-environmental conditions. We analysed 16 study cases of selected material delivered from breeding activities in four European forest tree species (Pinus halepensis Mill., Pinus nigra J.F. Arnold, Pinus pinaster Ait. and Populus nigra L.) with different levels of breeding. To answer these two questions, we first assessed and compared the genetic diversity of selected material versus natural populations using both putatively neutral and adaptive (based on diverging selection) Single Nucleotide Polymorphisms (SNPs). We then suggest how to enrich these populations for resilience under future climatic conditions by defining a core collection for each species including material from populations that will likely disappear under future conditions. Thanks to the large SNP datasets available for our focal species, we were able to detect some trends in our data. Expected and observed heterozygosity values for selected populations were almost always identical. The selected material showed small but significant genetic differentiation from their original population and their inbreeding coefficient was generally lower. However, the level of genetic improvement (i.e. low vs high) was not correlated with the observed genetic differences between selected material and natural populations.The genetic characterization of natural populations distributed across the species range, and the future projection of their range stability, made it possible to identify core-collections that would significantly enrich breeding populations under uncertain future environmental conditions.

Elucidation of genetic variation and population structure of melon genetic resources in the NARO Genebank, and construction of the World Melon Core Collection.

Numerous genetic resources of major crops have been introduced from around the world and deposited in Japanese National Agriculture and Food Research Organization (NARO) Genebank. Understanding their genetic variation and selecting a representative subset ("core collection") are essential for optimal management and efficient use of genetic resources. In this study, we conducted genotyping-by-sequencing (GBS) to characterize the genetic relationships and population structure in 755 accessions of melon genetic resources. The GBS identified 39,324 single-nucleotide polymorphisms (SNPs) that are distributed throughout the melon genome with high density (one SNP/10.6 kb). The phylogenetic relationships and population structure inferred using this SNP dataset are highly associated with the cytoplasm type and geographical origin. Our results strongly support the recent hypothesis that cultivated melon was established in Africa and India through multiple independent domestication events. Finally, we constructed a World Melon Core Collection that covers at least 82% of the genetic diversity and has a wide range of geographical origins and fruit morphology. The genome-wide SNP dataset, phylogenetic relationships, population structure, and the core collection provided in this study should largely contribute to genetic research, breeding, and genetic resource preservation in melon.

Genomes of the cosmopolitan fruit pest Bactrocera dorsalis (Diptera: Tephritidae) reveal its global invasion history and thermal adaptation

IntroductionThe oriental fruit fly Bactrocera dorsalis is one of the most destructive agricultural pests worldwide, with highly debated species delimitation, origin, and global spread routes. ObjectivesOur study intended to (i) resolve the taxonomic uncertainties between B. dorsalis and B. carambolae, (ii) reveal the population structure and global invasion routes of B. dorsalis across Asia, Africa, and Oceania, and (iii) identify genomic regions that are responsible for the thermal adaptation of B. dorsalis. MethodsBased on a high-quality chromosome-level reference genome assembly, we explored the population relationship using a genome-scale single nucleotide polymorphism dataset generated from the resequencing data of 487 B. dorsalis genomes and 25 B. carambolae genomes. Genome-wide association studies and silencing using RNA interference were used to identify and verify the candidate genes associated with extreme thermal stress. ResultsWe showed that B. dorsalis originates from the Southern India region with three independent invasion and spread routes worldwide: (i) from Northern India to Northern Southeast Asia, then to Southern Southeast Asia; (ii) from Northern India to Northern Southeast Asian, then to China and Hawaii; and (iii) from Southern India toward the African mainland, then to Madagascar, which is mainly facilitated by human activities including trade and immigration. Twenty-seven genes were identified by a genome-wide association study to be associated with 11 temperature bioclimatic variables. The Cyp6a9 gene may enhance the thermal adaptation of B. dorsalis and thus boost its invasion, which tended to be upregulated at a hardening temperature of 38 °C. Functional verification using RNA interference silencing against Cyp6a9, led to the specific decrease in Cyp6a9 expression, reducing the survival rate of dsRNA-feeding larvae exposed to extreme thermal stress of 45 °C after heat hardening treatments in B. dorsalis. ConclusionThis study provides insights into the evolutionary history and genetic basis of temperature adaptation in B. dorsalis.