Genotyping-by-sequencing Data Research Articles

Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species

BackgroundDiversifying animal cultivation demands efficient genotyping for enabling genomic selection, but non-model species lack efficient genotyping solutions. The aim of this study was to optimize a genotyping-by-sequencing (GBS) double-digest RAD-sequencing (ddRAD) pipeline. Bovine data was used to automate the bioinformatic analysis. The application of the optimization was demonstrated on non-model European whitefish data.ResultsDdRAD data generation was designed for a reliable estimation of relatedness and is scalable to up to 384 samples. The GBS sequencing yielded approximately one million reads for each of the around 100 assessed samples. Optimizing various strategies to create a de-novo reference genome for variant calling (mock reference) showed that using three samples outperformed other building strategies with single or very large number of samples. Adjustments to most pipeline tuning parameters had limited impact on high-quality data, except for the identity criterion for merging mock reference genome clusters. For each species, over 15k GBS variants based on the mock reference were obtained and showed comparable results with the ones called using an existing reference genome. Repeatability analysis showed high concordance over replicates, particularly in bovine while in European whitefish data repeatability did not exceed earlier observations.ConclusionsThe proposed cost-effective ddRAD strategy, coupled with an efficient bioinformatics workflow, enables broad adoption of ddRAD GBS across diverse farmed species. While beneficial, a reference genome is not obligatory. The integration of Snakemake streamlines the pipeline usage on computer clusters and supports customization. This user-friendly solution facilitates genotyping for both model and non-model species.

Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover

Key messageGenomic selection using white clover multi-year-multi-site data showed predicted genetic gains through integrating among-half-sibling-family phenotypic selection and within-family genomic selection were up to 89% greater than half-sibling-family phenotypic selection alone.Genomic selection, an effective breeding tool used widely in plants and animals for improving low-heritability traits, has only recently been applied to forages. We explored the feasibility of implementing genomic selection in white clover (Trifolium repens L.), a key forage legume which has shown limited genetic improvement in dry matter yield (DMY) and persistence traits. We used data from a training population comprising 200 half-sibling (HS) families evaluated in a cattle-grazed field trial across three years and two locations. Combining phenotype and genotyping-by-sequencing (GBS) data, we assessed different two-stage genomic prediction models, including KGD-GBLUP developed for low-depth GBS data, on DMY, growth score, leaf size and stolon traits. Predictive abilities were similar among the models, ranging from −0.17 to 0.44 across traits, and remained stable for most traits when reducing model input to 100–120 HS families and 5500 markers, suggesting genomic selection is viable with fewer resources. Incorporating a correlated trait with a primary trait in multi-trait prediction models increased predictive ability by 28–124%. Deterministic modelling showed integrating among-HS-family phenotypic selection and within-family genomic selection at different selection pressures estimated up to 89% DMY genetic gain compared to phenotypic selection alone, despite a modest predictive ability of 0.3. This study demonstrates the potential benefits of combining genomic and phenotypic selection to boost genetic gains in white clover. Using cost-effective GBS paired with a prediction model optimized for low read-depth data, the approach can achieve prediction accuracies comparable to traditional models, providing a viable path for implementing genomic selection in white clover.

GWAS for Drought Resilience Traits in Red Clover (Trifolium pratense L.)

Red clover (Trifolium pratense L.) is a well-appreciated grassland crop in temperate climates but suffers from increasingly frequent and severe drought periods. Molecular markers for drought resilience (DR) would benefit breeding initiatives for red clover, as would a better understanding of the genes involved in DR. Two previous studies, as follows, have: (1) identified phenotypic DR traits in a diverse set of red clover accessions; and (2) produced genotypic data using a pooled genotyping-by-sequencing (GBS) approach in the same collection. In the present study, we performed genome-wide association studies (GWAS) for DR using the available phenotypic and genotypic data. Single nucleotide polymorphism (SNP) calling was performed using GBS data and the following two red clover genome assemblies: the recent HEN-17 assembly and the Milvus assembly. SNP positions with significant associations were used to delineate flanking regions in both genome assemblies, while functional annotations were retrieved from Medicago truncatula orthologs. GWAS revealed 19 significant SNPs in the HEN-17-derived SNP set, explaining between 5.3 and 23.2% of the phenotypic variation per SNP–trait combination for DR traits. Among the genes in the SNP-flanking regions, we identified candidate genes related to cell wall structuring, genes encoding sugar-modifying proteins, an ureide permease gene, and other genes linked to stress metabolism pathways. GWAS revealed 29 SNPs in the Milvus-derived SNP set that explained substantially more phenotypic variation for DR traits, between 5.3 and 42.3% per SNP–trait combination. Candidate genes included a DEAD-box ATP-dependent RNA helicase gene, a P-loop nucleoside triphosphate hydrolase gene, a Myb/SANT-like DNA-binding domain protein, and an ubiquitin–protein ligase gene. Most accessions in this study are genetically more closely related to the Milvus genotype than to HEN-17, possibly explaining how the Milvus-derived SNP set yielded more robust associations. The Milvus-derived SNP set pinpointed 10 genomic regions that explained more than 25% of the phenotypic variation for DR traits. A possible next step could be the implementation of these SNP markers in practical breeding programs, which would help to improve DR in red clover. Candidate genes could be further characterized in future research to unravel drought stress resilience in red clover in more detail.

Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool.

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

Characterization of fine geographic scale population genetics in sugar kelp (Saccharina latissima) using genome-wide markers

BackgroundKelps are not only ecologically important, being primary producers and habitat forming species, they also hold substantial economic potential. Expansion of the kelp cultivation industry raises the interest for genetic improvement of kelp for cultivation, as well as concerns about genetic introgression from cultivated to wild populations. Thus, increased understanding of population genetics in natural kelp populations is crucial. Genotyping-by-sequencing (GBS) is a powerful tool for studying population genetics. Here, using Saccharina latissima (sugar kelp) as our study species, we characterize the population genetics at a fine geographic scale, while also investigating the influence of marker type (biallelic SNPs versus multi-allelic short read-backed haplotypes) and minor allele count (MAC) thresholds on estimated population genetic metrics.ResultsWe examined 150 sporophytes from 10 locations within a small area in Mid-Norway. Employing GBS, we detected 20,710 bi-allelic SNPs and 42,264 haplotype alleles at 20,297 high quality GBS loci. We used both marker types as well as two MAC filtering thresholds (3 and 15) in the analyses. Overall, higher genetic diversity, more outbreeding and stronger substructure was estimated using haplotypes compared to SNPs, and with MAC 15 compared to MAC 3. The population displayed high genetic diversity (HE ranging from 0.18–0.37) and significant outbreeding (FIS ≤ − 0.076). Construction of a genomic relationship matrix, however, revealed a few close relatives within sampling locations. The connectivity between sampling locations was high (FST ≤ 0.09), but subtle, yet significant, genetic substructure was detected, even between sampling locations separated by less than 2 km. Isolation-by-distance was significant and explained 15% of the genetic variation, while incorporation of predicted currents in an “isolation-by-oceanography” model explained a larger proportion (~ 27%).ConclusionThe studied population is diverse, significantly outbred and exhibits high connectivity, partly due to local currents. The use of genome-wide markers combined with permutation testing provides high statistical power to detect subtle population substructure and inbreeding or outbreeding. Short haplotypes extracted from GBS data and removal of rare alleles enhances the resolution. Careful consideration of marker type and filtering thresholds is crucial when comparing independent studies, as they profoundly influence numerical estimates of population genetic metrics.

Genetic characterization of a captive marmoset (Callithrix jacchus) colony using genotype-by-sequencing.

The marmoset is a fundamental nonhuman primate model for the study of aging, neurobiology, and many other topics. Genetic management of captive marmoset colonies is complicated by frequent chimerism in the blood and other tissues, a lack of tools to enable cost-effective, genome-wide interrogation of variation, and historic mergers and migrations of animals between colonies. We implemented genotype-by-sequencing (GBS) of hair follicle derived DNA (a minimally chimeric DNA source) of 82 marmosets housed at the Southwest National Primate Research Center (SNPRC). Our primary goals were the genetic characterization of our marmoset population for pedigree verification and colony management and to inform the scientific community of the functional genetic makeup of this valuable resource. We used the GBS data to reconstruct the genetic legacy of recent mergers between colonies, to identify genetically related animals whose relationships were previously unknown due to incomplete pedigree information, and to show that animals in the SNPRC colony appear to exhibit low levels of inbreeding. Of the >99,000 single-nucleotide variants (SNVs) that we characterized, >9800 are located within gene regions known to harbor pathogenic variants of clinical significance in humans. Overall, we show the combination of low-resolution (sparse) genotyping using hair follicle DNA is a powerful strategy for the genetic management of captive marmoset colonies and for identifying potential SNVs for the development of biomedical research models.

Crop-to-wild gene flow in wild coffee species: the case of Coffea canephora in the Democratic Republic of the Congo.

Plant breeders are increasingly turning to crop wild relatives (CWRs) to ensure food security in a rapidly changing environment. However, CWR populations are confronted with various human-induced threats, including hybridization with their nearby cultivated crops. This might be a particular problem for wild coffee species, which often occur near coffee cultivation areas. Here, we briefly review the evidence for wild Coffea arabica (cultivated as Arabica coffee) and Coffea canephora (cultivated as Robusta coffee) and then focused on C. canephora in the Yangambi region in the Democratic Republic of the Congo. There, we examined the geographical distribution of cultivated C. canephora and the incidence of hybridization between cultivated and wild individuals within the rainforest. We collected 71 C. canephora individuals from home gardens and 12 C. canephora individuals from the tropical rainforest in the Yangambi region and genotyped them using genotyping-by-sequencing (GBS). We compared the fingerprints with existing GBS data from 388 C. canephora individuals from natural tropical rainforests and the INERA Coffee Collection, a Robusta coffee field gene bank and the most probable source of cultivated genotypes in the area. We then established robust diagnostic fingerprints that genetically differentiate cultivated from wild coffee, identified cultivated-wild hybrids and mapped their geographical position in the rainforest. We identified cultivated genotypes and cultivated-wild hybrids in zones with clear anthropogenic activity, and where cultivated C. canephora in home gardens may serve as a source for crop-to-wild gene flow. We found relatively few hybrids and backcrosses in the rainforests. The cultivation of C. canephora in close proximity to its wild gene pool has led to cultivated genotypes and cultivated-wild hybrids appearing within the natural habitats of C. canephora. Yet, given the high genetic similarity between the cultivated and wild gene pool, together with the relatively low incidence of hybridization, our results indicate that the overall impact in terms of risk of introgression remains limited so far.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

GBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencing

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutio nary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms. However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).

STAC: A tool to leverage genetic marker data for crop research and breeding

AbstractAs genotyping by sequencing (GBS) becomes more prevalent and cost‐effective, there is a benefit in being able to apply the data to solve a variety of problems. However, high degrees of missing data and overreliance on single nucleotide polymorphisms (SNPs), while ignoring other forms of genetic variation, frequently plague attempts to make full use of GBS sequence data. Here we have developed two R scripts to serve as a tool in haplotype determination at loci of interest within biparental populations. One of these scripts, Sparse Tag Allele Caller (STAC), provides both automated calling and visual representations of the data around a locus of interest to assist in rapid data compilation decision‐making. The other script, STAC Integrate, allows automated quality control and logic‐based integration of presence/absence data with SNP data, while also rendering global overviews of recombination and coverage across the genome. These scripts are designed to be used together to maximize the utility of the available data. These tools were validated on a biparental population of wheat that was genotyped through GBS. They successfully enabled haplotype determination of a locus that was difficult to directly genotype, and their systemic accuracy was demonstrated in multiple populations and species. These scripts may serve as a tool for researchers attempting to make better use of GBS and other genetic marker data for both research and crop breeding decisions.

Developing adapted wheat lines with broad-spectrum resistance to stem rust: Introgression of Sr59 through backcrossing and selections based on genotyping-by-sequencing data.

Control of stem rust, caused by Puccinia graminis f.sp. tritici, a highly destructive fungal disease of wheat, faces continuous challenges from emergence of new virulent races across wheat-growing continents. Using combinations of broad-spectrum resistance genes could impart durable stem rust resistance. This study attempted transfer of Sr59 resistance gene from line TA5094 (developed through CSph1bM-induced T2DS·2RL Robertsonian translocation conferring broad-spectrum resistance). Poor agronomic performance of line TA5094 necessitates Sr59 transfer to adapted genetic backgrounds and utility evaluations for wheat improvement. Based on combined stem rust seedling and molecular analyses, 2070 BC1F1 and 1230 BC2F1 plants were derived from backcrossing BAJ#1, KACHU#1, and REEDLING#1 with TA5094. Genotyping-by-sequencing (GBS) results revealed the physical positions of 15,116 SNPs on chromosome 2R. The adapted genotypes used for backcrossing were found not to possess broad-spectrum resistance to selected stem rust races, whereas Sr59-containing line TA5094 showed resistance to all races tested. Stem rust seedling assays combined with kompetitive allele-specific PCR (KASP) marker analysis successfully selected and generated the BC2F2 population, which contained the Sr59 gene, as confirmed by GBS. Early-generation data from backcrossing suggested deviations from the 3:1 segregation, suggesting that multiple genes may contribute to Sr59 resistance reactions. Using GBS marker data (40,584 SNPs in wheat chromosomes) to transfer the recurrent parent background to later-generation populations resulted in average genome recovery of 71.2% in BAJ#1*2/TA5094, 69.8% in KACHU#1*2/TA5094, and 70.5% in REEDLING#1*2/TA5094 populations. GBS data verified stable Sr59 introgression in BC2F2 populations, as evidenced by presence of the Ph1 locus and absence of the 50,936,209 bp deletion in CSph1bM. Combining phenotypic selections, stem rust seedling assays, KASP markers, and GBS data substantially accelerated transfer of broad-spectrum resistance into adapted genotypes. Thus, this study demonstrated that the Sr59 resistance gene can be introduced into elite genetic backgrounds to mitigate stem rust-related yield losses.

A Comparison of Non-Destructive Visceral Swab and Tissue Biopsy Sampling Methods for Genotyping-by-Sequencing in the Freshwater Mussel Fusconaia askewi.

Limiting harm to organisms caused by genetic sampling is an important consideration for rare species, and a number of non-destructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the unionid freshwater mussel, the Texas pigtoe (Fusconaia askewi). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared with swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence), while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.

The role of geography, ecology, and hybridisation in the evolutionary history of Canary Island Descurainia.

Oceanic islands offer the opportunity to understand evolutionary processes underlying rapid diversification. Along with geographic isolation and ecological shifts, a growing body of genomic evidence has suggested that hybridisation can play an important role in island evolution. Here we use genotyping-by-sequencing (GBS) to understand the roles of hybridisation, ecology, and geographic isolation in the radiation of Canary Island Descurainia (Brassicaceae). We carried out GBS for multiple individuals of all Canary Island species and two outgroups. Phylogenetic analyses of the GBS data were performed using both super-matrix and gene tree approaches and hybridisation events were examined using D-statistics and Approximate Bayesian Computation (ABC). Climatic data were analysed to examine the relationship between ecology and diversification. Analysis of the supermatrix dataset resulted in a fully resolved phylogeny. Species networks suggest a hybridisation event has occurred for D. gilva, with these results being supported by ABC analysis. Strong phylogenetic signals for temperature and precipitation indicate one major ecological shift within Canary Island Descurainia. Inter-island dispersal played a significant role in the diversification of Descurainia, with evidence of only one major shift in climate preferences. Despite weak reproductive barriers and the occurrence of hybrids, hybridisation appears to have played only a limited role in the diversification of the group, with a single instance detected. The results highlight the need to use phylogenetic network approaches that can simultaneously accommodate ILS and gene flow when studying groups prone to hybridisation; patterns that might otherwise be obscured in species trees. This article is protected by copyright. All rights reserved.

Genomic insights into the NPGS intermediate wheatgrass germplasm collection

AbstractThe National Plant Germplasm System (NPGS) is a vital resource for genetic diversity, yet utilization of this resource requires a thorough understanding of the germplasm and genetic diversity. Intermediate wheatgrass (IWG, Thinopyrum intermedium) is a perennial grass species that has been improved for forage production through breeding utilizing the NPGS collection and has also been targeted for domestication as a perennial grain crop. To better characterize the IWG collection, we combined previously published forage data with new agronomic and genomic data. A total of 331 NPGS accessions were genomically profiled with genotyping‐by‐sequencing (GBS) and a genome‐wide association study (GWAS) was used to evaluate trait architecture. Along with the GWAS, in silico bulk samples were profiled by recoding GBS data to conduct association mapping through allele counting with extreme‐phenotype (XP)‐GWAS. Genomic analysis revealed two subpopulations, which were defined as European and Asian groups, and are differentiated around the Black Sea region. Phenotypic observations for forage and agronomic traits differed between the two groups (p < 0.05), even though greater than 70% of the genetic variance was partitioned within individual accessions. Finally, XP‐GWAS revealed 303 marker–trait associations for five agronomic and four forage traits. These results suggest that genetic diversity within the NPGS collection should lead to genetic gains for both forage and grain breeding as well as opportunities for breeding programs to enhance genetic diversity. More broadly, the methods we applied could be applicable to low‐resourced species, leveraging existing, and new data, to strengthen genetic characterization and breeding efficiency.

Efficiency of genotyping by sequencing in inferring genomic relatedness and molecular insights into fat tail selection in Tunisian sheep.

In developing countries, the use of simple and cost-efficient molecular technology is crucial for genetic characterization of local animal resources and better development of conservation strategies. The genotyping by sequencing (GBS) technique, also called restriction enzyme- reduced representational sequencing, is an efficient, cost-effective method for simultaneous discovery and genotyping of many markers. In the present study, we applied a two-enzyme GBS protocol (PstI/MspI) to discover and genotype SNP markers among 197 Tunisian sheep samples. A total of 100 333 bi-allelic SNPs were discovered and genotyped with an SNP call rate of 0.69 and mean sample depth 3.33. The genomic relatedness between 183 samples grouped the samples perfectly to their populations and pointed out a high genetic relatedness of inbred subpopulation reflecting the current adopted reproductive strategies. The genome-wide association study contrasting fat vs. thin-tailed breeds detected 41 significant variants including a peak positioned on OAR20. We identified FOXC1, GMDS, VEGFA, OXCT1, VRTN and BMP2 as the most promising for sheep tail-type trait. The GBS data have been useful to assess the population structure and improve our understanding of the genomic architecture of distinctive characteristics shaped by selection pressure in local sheep breeds. This study successfully investigates a cost-efficient method to discover genotypes, assign populations and understand insights into sheep adaptation to arid area. GBS could be of potential utility in livestock species in developing/emerging countries.

Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps.

Genotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent). We set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

Fusarium isolates from Belgium causing wilt in lettuce show genetic and pathogenic diversity

AbstractFusarium oxysporum f. sp. lactucae race 4 causes vascular necrosis and wilting of lettuce. First observed in Belgium in 2015, the lack of disease resistance in commercial cultivars allowed this pathogen to spread to nearly the entire Belgian production area within 4 years. Different levels of disease development were observed in different commercial greenhouses. To help explain this variation, we collected 78 Fusarium isolates and characterized them both physiologically and genetically. Molecular race identification indicated that 91% of the isolates belonged to race 4, while 6% of the isolates belonged to race 1, which was not previously reported in Belgium. Pathogenicity assays using differential cultivars confirmed the molecular race assignment of selected isolates. Cultivar Patriot was identified as a suitable new differential cultivar to race 4. Race 4 isolates were more aggressive than race 1 isolates at 24°C, but only when using chlamydospore inoculum instead of a root dip assay containing microconidia. Variation in pathogenicity and aggressiveness of the races may explain differences in disease development in commercial greenhouses. Based on genotyping‐by‐sequencing (GBS), race 1 and race 4 isolates were highly similar to reference isolates. Fusarium curvatum, F. oxysporum f. sp. tulipae and F. oxysporum f. sp. rhois were phylogenetically separated from F. oxysporum f. sp. lactucae races 1 and 4 based on the GBS data, but not when using multilocus sequence data. Within F. oxysporum f. sp. lactucae race 4, the GBS data differentiated two rather homogeneous groups, suggesting at least two introductions. However, the two groups did not differ in aggressiveness.

Range expansion and contraction of Tillandsia landbeckii lomas in the hyperarid Chilean Atacama Desert indicates ancient introgression and geneflow

Among the fascinating and highly specialized vascular plants in the hyperarid core of the Chilean and Peruvian Atacama Desert there are few Tillandsia species from the bromeliad family (Bromeliaceae). These grow epiarenically on bare sand without a functional root system, and in some rare cases they build up a monospecific and often the only landscape characterizing vegetation type, which is called Tillandsiales or Tillandsia loma. Tillandsia landbeckii is the dominating epiarenic species in Chile totally dependent on fog serving as the only water resource. Herein we elaborate on the hypothesis that migration and multiple colonization in concert with putative introgression from other Tillandsia species build up the present day phylogeographic distribution pattern and may contribute to the evolutionary dynamics and long-term success in hyperarid desert systems. Genomic analyses using GBS (genotyping-by-sequencing) data from the nuclear and plastid genome were conducted at the population level. A genome skimming approach was used to generate reference plastome data. The results indicate that both, multiple colonization and secondary contact of old gene pools and interspecies geneflow, contribute to present-day population genetic structure. Local-scale analysis also indicates that these past footprints of evolutionary history do contribute to present-day local adaptive potential of the species.

Genetic Diversity and Pedigree Analysis of Red Currant Germplasm

This represents the first report on the genetic diversity of red currant germplasm collections based on genotyping-by-sequencing (GBS) data. Genotypes of 75 individuals of different origin were assessed in more than 7.5K genome positions. Multidimensional scaling (MDS) analysis has been performed. There are five accessions that are significantly isolated from each other and from the rest of the analyzed cultivars. F1 offspring of R. petraeum Wulf (Rote Hollandische) and Gondouin, as well as Rote Spatlese (F2 of R. petraeum and F2 of R. multiflorum Kit.), are the most genetically isolated on the MDS plot. Ribes multiflorum is closer to the rest of cultivars than the three abovementioned accessions. Purpurnaya cultivar (F1 of Rote Spatlese) is located between Rote Hollandische and R. multiflorum. Other genotypes, mostly represented by varieties having several species in a pedigree, occupied the rest of MDS plot relatively evenly. Descendants of R. multiflorum have been placed in the left part of MDS plot, which underlines their genetic diversity from other accessions. White- and pink-fruited cultivars were clustered together, underlining genetic relatedness. Admixture analysis of GBS data reveals six clusters (K = 6). Presumably, clustering reflects relatedness to R. petraeum, R. rubrum, R. vulgare var macrocarpum, R. multiflorum, R. vulgare, and Jonker van Tets. Based on genotyping data, F1 offspring of R. warscewiczs Jancz (cultivar Viksne), R. altissimum Turcz (Cirald), and R. palczewskii (Jancz.) Pojark (Skorospelaya) have not exhibited strict separation and were placed in a pool with other varieties. This supports modern taxonomic classifications that do not consider R. altissimum and R. palczewskii as independent species.

Genome-wide investigations reveal the population structure and selection signatures of Nigerian cattle adaptation in the sub-Saharan tropics

BackgroundCattle are considered to be the most desirable livestock by small scale farmers. In Africa, although comprehensive genomic studies have been carried out on cattle, the genetic variations in indigenous cattle from Nigeria have not been fully explored. In this study, genome-wide analysis based on genotyping-by-sequencing (GBS) of 193 Nigerian cattle was used to reveal new insights on the history of West African cattle and their adaptation to the tropical African environment, particularly in sub-Saharan region. ResultsThe GBS data were evaluated against whole-genome sequencing (WGS) data and high rate of variant concordance between the two platforms was evident with high correlated genetic distance matrices genotyped by both methods suggestive of the reliability of GBS applicability in population genetics. The genetic structure of Nigerian cattle was observed to be homogenous and unique from other African cattle populations. Selection analysis for the genomic regions harboring imprints of adaptation revealed genes associated with immune responses, growth and reproduction, efficiency of feeds utilization, and heat tolerance. Our findings depict potential convergent adaptation between African cattle, dogs and humans with adaptive genes SPRY2 and ITGB1BP1 possibly involved in common physiological activities.ConclusionThe study presents unique genetic patterns of Nigerian cattle which provide new insights on the history of cattle in West Africa based on their population structure and the possibility of parallel adaptation between African cattle, dogs and humans in Africa which require further investigations.