polyRAD: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids

Genome-wide analysis tracks the emergence of intraspecific polyploids in Phragmites australis

Polyploidization plays an important role in plant speciation and adaptation. To address the role of polyploidization in grass diversification, we studied Phragmites australis, an invasive species with intraspecific variation in chromosome numbers ranging from 2n = 36 to 144. We utilized a combined analysis of ploidy estimation, phylogeny, population genetics and model simulations to investigate the evolution of P. australis. Using restriction site-associated DNA sequencing (RAD-seq), we conducted a genome-wide analysis of 88 individuals sourced from diverse populations worldwide, revealing the presence of six distinct intraspecific lineages with extensive genetic admixture. Each lineage was characterized by a specific ploidy level, predominantly tetraploid or octoploid, indicative of multiple independent polyploidization events. The population size of each lineage has declined moderately in history while remaining large, except for the North American native and the US Land types, which experienced constant population size contraction throughout their history. Our investigation did not identify direct association between polyploidization events and grass invasions. Nonetheless, we observed octoploid and hexaploid lineages at contact zones in Romania, Hungary, and South Africa, suggestively due to genomic conflicts arising from allotetraploid parental lineages.

Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa

Alfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability.

The value of long-term ecological research for evolutionary insights

Scientists must have an integrative understanding of ecology and evolution across spatial and temporal scales to predict how species will respond to global change. Although comprehensively investigating these processes in nature is challenging, the infrastructure and data from long-term ecological research networks can support cross-disciplinary investigations. We propose using these networks to advance our understanding of fundamental evolutionary processes and responses to global change. For ecologists, we outline how long-term ecological experiments can be expanded for evolutionary inquiry, and for evolutionary biologists, we illustrate how observed long-term ecological patterns may motivate new evolutionary questions. We advocate for collaborative, multi-site investigations and discuss barriers to conducting evolutionary work at network sites. Ultimately, these networks offer valuable information and opportunities to improve predictions of species' responses to global change.

'Drifting' Buchnera genomes track the microevolutionary trajectories of their aphid hosts

Evolution of Buchnera-aphid host symbioses is often studied among species at macroevolutionary scales. Investigations within species offer a different perspective about how eco-evolutionary processes shape patterns of genetic variation at microevolutionary scales. Our study leverages new and publicly available whole-genome sequencing data to study Buchnera-aphid host evolution in Myzus persicae, the peach potato aphid, a globally invasive and polyphagous pest. Across 43 different asexual, clonally reproducing isofemale strains, we examined patterns of genomic covariation between Buchnera and their aphid host and considered the distribution of mutations in protein-coding regions of the Buchnera genome. We found Buchnera polymorphisms within aphid strains, suggesting the presence of genetically different Buchnera strains within the same clonal lineage. Genetic distance between pairs of Buchnera samples was positively correlated to genetic distance between their aphid hosts, indicating shared evolutionary histories. However, there was no segregation of genetic variation for both M. persicae and Buchnera with plant host (Brassicaceae and non-tobacco Solanaceae) and no associations between genetic and geographic distance at global or regional spatial scales. Abundance patterns of non-synonymous mutations were similar to synonymous mutations in the Buchnera genome, and both mutation classes had similar site frequency spectra. We hypothesize that a predominance of neutral processes results in the Buchnera of M. persicae to simply 'drift' with the evolutionary trajectory of their aphid hosts. Our study presents a unique microevolutionary characterization of Buchnera-aphid host genomic covariation across multiple aphid clones. This provides a new perspective on the eco-evolutionary processes generating and maintaining polymorphisms in a major pest aphid species and its obligate primary endosymbiont.

Potato soup: analysis of cultivated potato gene bank populations reveals high diversity and little structure

Cultivated potatoes are incredibly diverse, ranging from diploid to pentaploid and encompass four different species. They are adapted to disparate environments and conditions and carry unique alleles for resistance to pests and pathogens. Describing how diversity is partitioned within and among these populations is essential to understanding the potato genome and effectively utilizing landraces in breeding. This task is complicated by the difficulty of making comparisons across cytotypes and extensive admixture within section petota. We genotyped 730 accessions from the US Potato genebank including wild diploids and cultivated diploids and tetraploids using Genotype-by-sequencing. This data set allowed us to interrogate population structure and diversity as well as generate core subsets which will support breeders in efficiently screening genebank material for biotic and abiotic stress resistance alleles. We found that even controlling for ploidy, tetraploid material exhibited higher observed and expected heterozygosity than diploid accessions. In particular group chilotanum material was the most heterozygous and the only taxa not to exhibit any inbreeding. This may in part be because group chilotanum has a history of introgression not just from wild species, but landraces as well. All group chilotanum, exhibits introgression from group andigenum except clones from Southern South America near its origin, where the two groups are not highly differentiated. Moving north, we do not observe evidence for the same level of admixture back into group andigenum. This suggests that extensive history of admixture is a particular characteristic of chilotanum.

Variant calling in polyploids for population and quantitative genetics

Advancements in genome assembly and sequencing technology have made whole genome sequence (WGS) data and reference genomes accessible to study polyploid species. Compared to popular reduced-representation sequencing approaches, the genome-wide coverage and greater marker density provided by WGS data can greatly improve our understanding of polyploid species and polyploid biology. However, biological features that make polyploid species interesting also pose challenges in read mapping, variant identification, and genotype estimation. Accounting for characteristics in variant calling like allelic dosage uncertainty, homology between subgenomes, and variance in chromosome inheritance mode can reduce errors. Here, I discuss the challenges of variant calling in polyploid WGS data and discuss where potential solutions can be integrated into a standard variant calling pipeline.

Genetic diversity, population structure, and taxonomic confirmation in annual medic (Medicago spp.) collections from Crimea, Ukraine

Annual medic (Medicago spp.) germplasm was collected from the Crimean Peninsula of Ukraine in 2008 to fill gaps in geographic coverage in the United States department of Agriculture, Agricultural Research Service, National Plant Germplasm System (NPGS) temperate-adapted forage legume collection. A total of 102 accessions across 10 Medicago species were collected. To assess genetic diversity, population structure, and to confirm taxonomic identities, the collections were phenotypically and genetically characterized. Phenotyping included the use of 24 descriptor traits while genetic characterization was accomplished using a 3K Diversity Array Technologies (DArTag) panel developed for alfalfa (Medicago sativa L.). For both field and molecular characterizations, a reference set of 92 geographically diverse and species-representative accessions were obtained from the NPGS collection. Phenotypic descriptors showed consistency among replicated plants within accessions, some variation across accessions within species, and evident distinctions between species. Because the DArTag panel was developed for cultivated alfalfa, the transferability of markers to the species being evaluated was limited, resulting in an average of ~1,500 marker loci detected per species. From these loci, 448 markers were present in 95% of the samples. Principal component and phylogenetic analysis based on a larger set of 2,396 selected markers clustered accessions by species and predicted evolutionary relationships among species. Additionally, the markers aided in the taxonomic identity of a few accessions that were likely mislabeled. The genotyping results also showed that sampling individual plants for these mostly self-pollinating species is sufficient due to high reproducibility between single (n=3) and pooled (n=7) biological replicate leaf samples. The phenotyping and the 2,396 Single Nucleotide Polymorphism (SNP) marker set were useful in estimating population structure in the Crimean and reference accessions, highlighting novel and unique genetic diversity captured in the Crimean accessions. This research not only demonstrated the utility of the DArTag marker panel in evaluating the Crimean germplasm but also highlighted its broader application in assessing genetic resources within the Medicago genus. Furthermore, we anticipate that our findings will underscore the importance of leveraging genetic resources and advanced genotyping tools for sustainable crop improvement and biodiversity conservation in annual medic species.

The Effect of Genome Parametrization and SNP Marker Subsetting on Genomic Selection in Autotetraploid Alfalfa

BACKGROUND

Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and the extent of the non-additive genetic variance associated with its autotetraploid genome.

METHODS

To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as tetraploid and diploid dosages, plus allele ratios) and SNP marker subsetting (all available SNPs, only genic regions, and only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used genotyping by sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and the onset of flowering, which were assessed in 143 genotyped plants from a genetically broad European reference population and their phenotyped half-sib progenies.

RESULTS

On average, the allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), while using all the SNPs offered an advantage compared with any specific SNP subsetting (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting achieved better performances. We also released Legpipe2, an SNP calling pipeline tailored for reduced representation (GBS, RAD) in medium-sized genotyping experiments.

Botany and geogenomics: Constraining geological hypotheses in the neotropics with large-scale genetic data derived from plants

Decades of empirical research have revealed how the geological history of our planet shaped plant evolution by establishing well-known patterns (e.g., how mountain uplift resulted in high rates of diversification and replicate radiations in montane plant taxa). This follows a traditional approach where botanical data are interpreted in light of geological events. In this synthesis, I instead describe how by integrating natural history, phylogenetics, and population genetics, botanical research can be applied alongside geology and paleontology to inform our understanding of past geological and climatic processes. This conceptual shift aligns with the goals of the emerging field of geogenomics. In the neotropics, plant geogenomics is a powerful tool for the reciprocal exploration of two long standing questions in biology and geology: how the dynamic landscape of the region came to be and how it shaped the evolution of the richest flora. Current challenges that are specific to analytical approaches for plant geogenomics are discussed. I describe the scale at which various geological questions can be addressed from biological data and what makes some groups of plants excellent model systems for geogenomics research. Although plant geogenomics is discussed with reference to the neotropics, the recommendations given here for approaches to plant geogenomics can and should be expanded to exploring long-standing questions on how the earth evolved with the use of plant DNA.

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data

KEY MESSAGE

An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Use of continuous genotypes for genomic prediction in sugarcane

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

How Can Genomics Help or Hinder Wildlife Conservation?

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

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.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

A medium-density genotyping platform for cultivated strawberry using DArTag technology

Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high-density genetic marker platforms designed for strawberry. We developed mid-density panels of molecular inversion probes (MIPs) to be deployed with the "DArTag" marker platform to provide a low-cost, high-throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome-specific amplicon alignments to the FaRR1 assembly and subsequent alignment-based variant calls with strong concordance to DArT's alignment-free, count-based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low-amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33, respectively.

Bayesian tests for random mating in polyploids

Hardy-Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations and demonstrate on two real datasets how testing for random mating is more useful for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of the hwep R package on the Comprehensive R Archive Network https://cran.r-project.org/package=hwep.

Chronosequence of invasion reveals minimal losses of population genomic diversity, niche expansion, and trait divergence in the polyploid, leafy spurge

Rapid evolution may play an important role in the range expansion of invasive species and modify forecasts of invasion, which are the backbone of land management strategies. However, losses of genetic variation associated with colonization bottlenecks may constrain trait and niche divergence at leading range edges, thereby impacting management decisions that anticipate future range expansion. The spatial and temporal scales over which adaptation contributes to invasion dynamics remain unresolved. We leveraged detailed records of the ~130-year invasion history of the invasive polyploid plant, leafy spurge (Euphorbia virgata), across ~500 km in Minnesota, U.S.A. We examined the consequences of range expansion for population genomic diversity, niche breadth, and the evolution of germination behavior. Using genotyping-by-sequencing, we found some population structure in the range core, where introduction occurred, but panmixia among all other populations. Range expansion was accompanied by only modest losses in sequence diversity, with small, isolated populations at the leading edge harboring similar levels of diversity to those in the range core. The climatic niche expanded during most of the range expansion, and the niche of the range core was largely non-overlapping with the invasion front. Ecological niche models indicated that mean temperature of the warmest quarter was the strongest determinant of habitat suitability and that populations at the leading edge had the lowest habitat suitability. Guided by these findings, we tested for rapid evolution in germination behavior over the time course of range expansion using a common garden experiment and temperature manipulations. Germination behavior diverged from the early to late phases of the invasion, with populations from later phases having higher dormancy at lower temperatures. Our results suggest that trait evolution may have contributed to niche expansion during invasion and that distribution models, which inform future management planning, may underestimate invasion potential without accounting for evolution.

Demographic history inference and the polyploid continuum

Polyploidy is an important generator of evolutionary novelty across diverse groups in the Tree of Life, including many crops. However, the impact of whole-genome duplication depends on the mode of formation: doubling within a single lineage (autopolyploidy) versus doubling after hybridization between two different lineages (allopolyploidy). Researchers have historically treated these two scenarios as completely separate cases based on patterns of chromosome pairing, but these cases represent ideals on a continuum of chromosomal interactions among duplicated genomes. Understanding the history of polyploid species thus demands quantitative inferences of demographic history and rates of exchange between subgenomes. To meet this need, we developed diffusion models for genetic variation in polyploids with subgenomes that cannot be bioinformatically separated and with potentially variable inheritance patterns, implementing them in the dadi software. We validated our models using forward SLiM simulations and found that our inference approach is able to accurately infer evolutionary parameters (timing, bottleneck size) involved with the formation of auto- and allotetraploids, as well as exchange rates in segmental allotetraploids. We then applied our models to empirical data for allotetraploid shepherd's purse (Capsella bursa-pastoris), finding evidence for allelic exchange between the subgenomes. Taken together, our model provides a foundation for demographic modeling in polyploids using diffusion equations, which will help increase our understanding of the impact of demography and selection in polyploid lineages.

The Conservation Genetics of Iris lacustris (Dwarf Lake Iris), a Great Lakes Endemic

Iris lacustris, a northern Great Lakes endemic, is a rare species known from 165 occurrences across Lakes Michigan and Huron in the United States and Canada. Due to multiple factors, including habitat loss, lack of seed dispersal, patterns of reproduction, and forest succession, the species is threatened. Early population genetic studies using isozymes and allozymes recovered no to limited genetic variation within the species. To better explore genetic variation across the geographic range of I. lacustris and to identify units for conservation, we used tunable Genotyping-by-Sequencing (tGBS) with 171 individuals across 24 populations from Michigan and Wisconsin, and because the species is polyploid, we filtered the single nucleotide polymorphism (SNP) matrices using polyRAD to recognize diploid and tetraploid loci. Based on multiple population genetic approaches, we resolved three to four population clusters that are geographically structured across the range of the species. The species migrated from west to east across its geographic range, and minimal genetic exchange has occurred among populations. Four units for conservation are recognized, but nine adaptive units were identified, providing evidence for local adaptation across the geographic range of the species. Population genetic analyses with all, diploid, and tetraploid loci recovered similar results, which suggests that methods may be robust to variation in ploidy level.

Investigating the genetic components of tuber bruising in a breeding population of tetraploid potatoes

BACKGROUND

Tuber bruising in tetraploid potatoes (Solanum tuberosum) is a trait of economic importance, as it affects tubers' fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study.

RESULTS

When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait.

CONCLUSION

This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.

Polyploid SNP Genotyping Using the MassARRAY System

Molecular marker discovery and genotyping are major challenges in polyploid breeding programs incorporating molecular biology tools. In this context, this work describes a method for single nucleotide polymorphism (SNP) genotyping in polyploid crops using matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry, the MassARRAY System.

A first look at sea-lavenders genomics - can genome wide SNP information tip the scales of controversy in the Limonium vulgare species complex?

BACKGROUND

Sea-lavenders (Limonium Mill., Plumbaginaceae) are a cosmopolitan group of diploid and polyploid plants often adapted to extreme saline environments, with a mostly Tethyan distribution, occurring in the Mediterranean, Irano-Turanian, Euro-Siberian and in the New World. The halophylic Limonium vulgare polyploid complex in particular, presents a large distribution throughout extreme salt-marsh habitats and shows little morphological but high taximetric variation, frequently blurring species delimitation. In this work we pursue three main goals: assert whether SNP data from polyploid individuals has the resolution to distinguish the seven sampled species, to better understand how genetically structured Limonium vulgare is, and attempt to identify specific molecular mechanisms for the differentiation between L. maritimum and L. vulgare. For this purpose, 95 individuals were genotyped using Genotyping by Sequencing (GBS), which were assembled as two independent datasets using IPYRAD. All analyses performed downstream of assembly were fully automated. Phylogenetic inference, PCA, and admixture plots were used to infer answers to the study's main goals.

RESULTS

Close to 10,000 SNPs were obtained for each dataset. Phylogenetic analyses reveal that polyploid data can be used to infer species relationships. Population structure analyses suggest a genetically structured L. vulgare. A set of 34 SNPs were found to be fully segregated between L. vulgare and L. maritimum, two of which are potentially linked to proteins that might be involved in the speciation process.

CONCLUSION

Despite polyploid data analyses shortcomings, GBS generated SNPs have the resolution to discern all seven included species. Limonium vulgare revealed pronounced genetic structure along a geographical north-south cline. L. maritimum always appears as a distinct genetic entity. Segregated SNPs between L. vulgare and L. maritimum indicate salinity response and morphological trait control genes as potentially interesting to follow up for studying these species' divergence process.

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

BACKGROUND

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.

RESULTS

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).

CONCLUSIONS

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.

vcfpop: Performing population genetics analyses for autopolyploids and aneuploids based on next-generation sequencing data sets

Polyploids are cells or organisms with a genome consisting of more than two sets of homologous chromosomes. Polyploid plants have important traits that facilitate speciation and are thus often model systems for evolutionary, molecular ecology and agricultural studies. However, due to their unusual mode of inheritance and double-reduction, diploid models of population genetic analysis cannot properly be applied to autopolyploids. To overcome this problem, we developed a software package entitled vcfpop to perform a variety of population genetic analyses for autopolyploids, such as parentage analysis, analysis of molecular variance, principal coordinates analysis, hierarchical clustering analysis and Bayesian clustering. We used three data sets to evaluate the capability of vcfpop to analyse large data sets on a desktop computer. This software is freely available at http://github.com/huangkang1987/vcfpop.

CAPG: comprehensive allopolyploid genotyper

MOTIVATION

Genotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls.

RESULTS

We introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole-genome resequencing data. We demonstrate CAPG in allotetraploids, where it performs better than Genome Analysis Toolkit's HaplotypeCaller applied to reads aligned to the combined subgenomic references.

AVAILABILITY AND IMPLEMENTATION

Code and tutorials are available at https://github.com/Kkulkarni1/CAPG.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

NGS allele counts versus called genotypes for testing genetic association

RNA sequence data are commonly summarized as read counts. By contrast, so far there is no alternative to genotype calling for investigating the relationship between genetic variants determined by next-generation sequencing (NGS) and a phenotype of interest. Here we propose and evaluate the direct analysis of allele counts for genetic association tests. Specifically, we assess the potential advantage of the ratio of alternative allele counts to the total number of reads aligned at a specific position of the genome (coverage) over called genotypes. We simulated association studies based on NGS data from HapMap individuals. Genotype quality scores and allele counts were simulated using NGS data from the Personal Genome Project. Real data from the 1000 Genomes Project was also used to compare the two competing approaches. The average proportions of probability values lower or equal to 0.05 amounted to 0.0496 for called genotypes and 0.0485 for the ratio of alternative allele counts to coverage in the null scenario, and to 0.69 for called genotypes and 0.75 for the ratio of alternative allele counts to coverage in the alternative scenario (9% power increase). The advantage in statistical power of the novel approach increased with decreasing coverage, with decreasing genotype quality and with decreasing allele frequency – 124% power increase for variants with a minor allele frequency lower than 0.05. We provide computer code in R to implement the novel approach, which does not preclude the use of complementary data quality filters before or after identification of the most promising association signals.

Author summary

Genetic association tests usually rely on called genotypes. We postulate here that the direct analysis of allele counts from sequence data improves the quality of statistical inference. To evaluate this hypothesis, we investigate simulated and real data using distinct statistical approaches. We demonstrate that association tests based on allele counts rather than called genotypes achieve higher statistical power with controlled type I error rates.

A population-level statistic for assessing Mendelian behavior of genotyping-by-sequencing data from highly duplicated genomes

Background

Given the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for methods to distinguish paralogs, i.e. duplicate sequences within a genome, from Mendelian loci, i.e. single copy sequences that pair at meiosis. The ratio of observed to expected heterozygosity is an effective tool for filtering loci but requires genotyping to be performed first at a high computational cost, whereas counting the number of sequence tags detected per genotype is computationally quick but very ineffective in inbred or polyploid populations. Therefore, new methods are needed for filtering paralogs.

Results

We introduce a novel statistic, H_ind/H_E, that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value of H_ind/H_E is the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. In addition to filtering paralogs, it can be used to filter loci with null alleles or high overdispersion, and identify individuals with unexpected ploidy and hybrid status. We demonstrate that the statistic is useful at read depths as low as five to 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species.

Conclusions

Our methodology for estimating H_ind/H_E across loci and individuals, as well as determining reasonable thresholds for filtering loci, is implemented in polyRAD v1.6, available at https://github.com/lvclark/polyRAD. In large sequencing datasets, we anticipate that the ability to filter markers and identify problematic individuals prior to genotype calling will save researchers considerable computational time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04635-9.

Little hope for the polyploid endemic Pyrenean Larkspur (Delphinium montanum): Evidences from population genomics and Ecological Niche Modeling

Species endemic to restricted geographical ranges represent a particular conservation issue, be it for their heritage interest. In a context of global change, this is particularly the case for plants which belong to high-mountain ecosystems and, because of their ecological requirements, are doomed to survive or disappear on their "sky islands". The Pyrenean Larkspur (Delphinium montanum, Ranunculaceae) is endemic to the Eastern part of the Pyrenees (France and Spain). It is now only observable at a dozen of localities and some populations show signs of decline, such as a recurrent lack of flowering. Implementing population genomics approach (e.g., RAD-seq like) is particularly useful to understand genomic patterns of diversity and differentiation in order to provide recommendations in term of conservation. However, it remains challenging for species such as D. montanum that are autotetraploid with a large genome size (1C-value >10 pg) as most methods currently available were developed for diploid species. A Bayesian framework able to call genotypes with uncertainty allowed us to assess genetic diversity and population structure in this system. Our results show evidence for inbreeding (mean G IS = 0.361) within all the populations and substantial population structure (mean G ST = 0.403) at the metapopulation level. In addition to a lack of connectivity between populations, spatial projections of Ecological Niche Modeling (ENM) analyses under different climatic scenarios predict a dramatic decrease of suitable habitat for D. montanum in the future. Based on these results, we discuss the relevance and feasibility of different conservation measures.

Benchmarking small-variant genotyping in polyploids

Genotyping from sequencing is the basis of emerging strategies in the molecular breeding of polyploid plants. However, compared with the situation for diploids, in which genotyping accuracies are confidently determined with comprehensive benchmarks, polyploids have been neglected; there are no benchmarks measuring genotyping error rates for small variants using real sequencing reads. We previously introduced a variant calling method, Octopus, that accurately calls germline variants in diploids and somatic mutations in tumors. Here, we evaluate Octopus and other popular tools on whole-genome tetraploid and hexaploid data sets created using in silico mixtures of diploid Genome in a Bottle (GIAB) samples. We find that genotyping errors are abundant for typical sequencing depths but that Octopus makes 25% fewer errors than other methods on average. We supplement our benchmarks with concordance analysis in real autotriploid banana data sets.

Insights into opium poppy (Papaver spp.) genetic diversity from genotyping-by-sequencing analysis

Opium poppy (Papaver somniferum) is one of the world’s oldest medicinal plants and a versatile model system to study secondary metabolism. However, our knowledge of its genetic diversity is limited, restricting utilization of the available germplasm for research and crop improvement. We used genotyping-by-sequencing to investigate the extent of genetic diversity and population structure in a collection of poppy germplasm consisting of 91 accessions originating in 30 countries of Europe, North Africa, America, and Asia. We identified five genetically distinct subpopulations using discriminate analysis of principal components and STRUCTURE analysis. Most accessions obtained from the same country were grouped together within subpopulations, likely a consequence of the restriction on movement of poppy germplasm. Alkaloid profiles of accessions were highly diverse, with morphine being dominant. Phylogenetic analysis identified genetic groups that were largely consistent with the subpopulations detected and that could be differentiated broadly based on traits such as number of branches and seed weight. These accessions and the associated genotypic data are valuable resources for further genetic diversity analysis, which could include definition of poppy core sets to facilitate genebank management and use of the diversity for genetic improvement of this valuable crop.

When do autopolyploids need poly-sequencing data?

The sequencing depth required to genotype autopolyploid populations is a very controversial topic. Different studies have adopted variable depth values without a clear guide on the optimal sequencing depth value. Many studies suggest high depth thresholds for different ploidies that may not be practical and substantially increase the overall genotyping cost for different projects. However, such conservative thresholds may not be required to achieve the most common research goals. In fact, some recent reports in the field of quantitative genetics found that much lower sequencing depth thresholds could achieve the same accuracy as high depth thresholds. In this manuscript, I discuss when researchers need to use stringent sequencing depth thresholds and when they can use more relaxed ones. I support my argument by calculating the probabilities of sampling different homologues at a given sequencing depth. I also discuss the uses and the uncertainty in calculating a continuous allelic dosage as the proportion of sequencing reads that hold the alternative allele, which is becoming a common method now in quantitative genetics to replace discrete dosage estimation.

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

Haplotype reconstruction in connected tetraploid F1 populations

In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequence-based genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3 × 3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations.

The genic view of hybridization in the Anthropocene

Human impact is noticeable around the globe, indicating that a new era might have begun: the Anthropocene. Continuing human activities, including land-use changes, introduction of non-native species and rapid climate change, are altering the distributions of countless species, often giving rise to human-mediated hybridization events. While the interbreeding of different populations or species can have detrimental effects, such as genetic extinction, it can be beneficial in terms of adaptive introgression or an increase in genetic diversity. In this paper, I first review the different mechanisms and outcomes of anthropogenic hybridization based on literature from the last five years (2016-2020). The most common mechanisms leading to the interbreeding of previously isolated taxa include habitat change (51% of the studies) and introduction of non-native species (34% intentional and 19% unintentional). These human-induced hybridization events most often result in introgression (80%). The high incidence of genetic exchange between the hybridizing taxa indicates that the application of a genic view of speciation (and introgression) can provide crucial insights on how to address hybridization events in the Anthropocene. This perspective considers the genome as a dynamic collection of genetic loci with distinct evolutionary histories, giving rise to a heterogenous genomic landscape in terms of genetic differentiation and introgression. First, understanding this genomic landscape can lead to a better selection of diagnostic genetic markers to characterize hybrid populations. Second, describing how introgression patterns vary across the genome can help to predict the likelihood of negative processes, such as demographic and genetic swamping, as well as positive outcomes, such as adaptive introgression. It is especially important to not only quantify how much genetic material introgressed, but also what has been exchanged. Third, comparing introgression patterns in pre-Anthropocene hybridization events with current human-induced cases might provide novel insights into the likelihood of genetic swamping or species collapse during an anthropogenic hybridization event. However, this comparative approach remains to be tested before it can be applied in practice. Finally, the genic view of introgression can be combined with conservation genomic studies to determine the legal status of hybrids and take appropriate measures to manage anthropogenic hybridization events. The interplay between evolutionary and conservation genomics will result in the constant exchange of ideas between these fields which will not only improve our knowledge on the origin of species, but also how to conserve and protect them.

Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty

Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https://cran.r-project.org/package=ldsep ).

Parallel introgression, not recurrent emergence, explains apparent elevational ecotypes of polyploid Himalayan snowtrout

The recurrence of similar evolutionary patterns within different habitats often reflects parallel selective pressures acting upon either standing or independently occurring genetic variation to produce a convergence of phenotypes. This interpretation (i.e. parallel divergences within adjacent streams) has been hypothesized for drainage-specific morphological 'ecotypes' observed in polyploid snowtrout (Cyprinidae: Schizothorax). However, parallel patterns of differential introgression during secondary contact are a viable alternative hypothesis. Here, we used ddRADseq (N = 35 319 de novo and N = 10 884 transcriptome-aligned SNPs), as derived from Nepali/Bhutanese samples (N = 48 each), to test these competing hypotheses. We first employed genome-wide allelic depths to derive appropriate ploidy models, then a Bayesian approach to yield genotypes statistically consistent under the inferred expectations. Elevational 'ecotypes' were consistent in geometric morphometric space, but with phylogenetic relationships at the drainage level, sustaining a hypothesis of independent emergence. However, partitioned analyses of phylogeny and admixture identified subsets of loci under selection that retained genealogical concordance with morphology, suggesting instead that apparent patterns of morphological/phylogenetic discordance are driven by widespread genomic homogenization. Here, admixture occurring in secondary contact effectively 'masks' previous isolation. Our results underscore two salient factors: (i) morphological adaptations are retained despite hybridization and (ii) the degree of admixture varies across tributaries, presumably concomitant with underlying environmental or anthropogenic factors.

Meiosis in Polyploids and Implications for Genetic Mapping: A Review

Plant cytogenetic studies have provided essential knowledge on chromosome behavior during meiosis, contributing to our understanding of this complex process. In this review, we describe in detail the meiotic process in auto- and allopolyploids from the onset of prophase I through pairing, recombination, and bivalent formation, highlighting recent findings on the genetic control and mode of action of specific proteins that lead to diploid-like meiosis behavior in polyploid species. During the meiosis of newly formed polyploids, related chromosomes (homologous in autopolyploids; homologous and homoeologous in allopolyploids) can combine in complex structures called multivalents. These structures occur when multiple chromosomes simultaneously pair, synapse, and recombine. We discuss the effectiveness of crossover frequency in preventing multivalent formation and favoring regular meiosis. Homoeologous recombination in particular can generate new gene (locus) combinations and phenotypes, but it may destabilize the karyotype and lead to aberrant meiotic behavior, reducing fertility. In crop species, understanding the factors that control pairing and recombination has the potential to provide plant breeders with resources to make fuller use of available chromosome variations in number and structure. We focused on wheat and oilseed rape, since there is an abundance of elucidating studies on this subject, including the molecular characterization of the Ph1 (wheat) and PrBn (oilseed rape) loci, which are known to play a crucial role in regulating meiosis. Finally, we exploited the consequences of chromosome pairing and recombination for genetic map construction in polyploids, highlighting two case studies of complex genomes: (i) modern sugarcane, which has a man-made genome harboring two subgenomes with some recombinant chromosomes; and (ii) hexaploid sweet potato, a naturally occurring polyploid. The recent inclusion of allelic dosage information has improved linkage estimation in polyploids, allowing multilocus genetic maps to be constructed.

Using probabilistic genotypes in linkage analysis of polyploids

KEY MESSAGE

In polyploids, linkage mapping is carried out using genotyping with discrete dosage scores. Here, we use probabilistic genotypes and we validate it for the construction of polyploid linkage maps. Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.

Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding

Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to expanding production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on recurrent phenotypic selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberry breeding cycles are costly and time consuming, which results in low genetic gains per unit of time. Motivated by applying molecular markers for a more accurate selection in the early stages of breeding, we performed pioneering genomic selection studies and optimization for its implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based genotyping and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic selection studies and showed for the first time its application in an independent validation set. In this paper, our contribution is three-fold: (i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; (ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; (iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for using genomic selection in blueberry, with the potential to be applied to other polyploid species of a similar background.

Genotyping single nucleotide polymorphisms and inferring ploidy by amplicon sequencing for polyploid, ploidy-variable organisms

Whole genome duplication is hypothesized to have played a critical role in the evolution of several major taxa, including vertebrates, and while many lineages have rediploidized, some retain polyploid genomes. Additionally, variation in ploidy can occur naturally or be artificially induced within select plant and animal species. Modern genetic techniques have not been widely applied to polyploid or ploidy-variable species, in part due to the difficulty of obtaining genotype data from polyploids. In this study, we demonstrate a strategy for developing an amplicon sequencing panel of single nucleotide polymorphisms for high-throughput genotyping of polyploid organisms. We then develop a method to infer ploidy of individuals from amplicon sequencing data that is generalized to apply to any ploidy and does not require prior identification of heterozygous genotypes. Combining these two techniques will allow researchers to both infer ploidy and generate ploidy-aware genotypes with the same amplicon sequencing panel. We demonstrate this approach with white sturgeon Acipenser transmontanus, a ploidy-variable (octoploid, decaploid and dodecaploid) imperiled species under conservation management in the Pacific Northwest and obtained a panel of 325 loci. These loci were validated by examining inheritance in known-cross families, and the ploidy inference method was validated with known ploidy samples. We provide scripts that adapt existing pipelines to genotype polyploids and an R package for application of the ploidy inference method. We expect that these techniques will empower studies of genetic variation and inheritance in polyploid organisms that vary in ploidy level, either naturally or as a result of artificial propagation practices.

Genetic basis and identification of candidate genes for wooden breast and white striping in commercial broiler chickens

Wooden breast (WB) and white striping (WS) are highly prevalent and economically damaging muscle disorders of modern commercial broiler chickens characterized respectively by palpable firmness and fatty white striations running parallel to the muscle fiber. High feed efficiency and rapid growth, especially of the breast muscle, are believed to contribute to development of such muscle defects; however, their etiology remains poorly understood. To gain insight into the genetic basis of these myopathies, a genome-wide association study was conducted using a commercial crossbred broiler population (n = 1193). Heritability was estimated at 0.5 for WB and WS with high genetic correlation between them (0.88). GWAS revealed 28 quantitative trait loci (QTL) on five chromosomes for WB and 6 QTL on one chromosome for WS, with the majority of QTL for both myopathies located in a ~ 8 Mb region of chromosome 5. This region has highly conserved synteny with a portion of human chromosome 11 containing a cluster of imprinted genes associated with growth and metabolic disorders such as type 2 diabetes and Beckwith-Wiedemann syndrome. Candidate genes include potassium voltage-gated channel subfamily Q member 1 (KCNQ1), involved in insulin secretion and cardiac electrical activity, lymphocyte-specific protein 1 (LSP1), involved in inflammation and immune response.

Maximum likelihood parentage assignment using quantitative genotypes

The cost of parentage assignment precludes its application in many selective breeding programmes and molecular ecology studies, and/or limits the circumstances or number of individuals to which it is applied. Pooling samples from more than one individual, and using appropriate genetic markers and algorithms to determine parental contributions to pools, is one means of reducing the cost of parentage assignment. This paper describes and validates a novel maximum likelihood (ML) parentage-assignment method, that can be used to accurately assign parentage to pooled samples of multiple individuals-previously published ML methods are applicable to samples of single individuals only-using low-density single nucleotide polymorphism (SNP) 'quantitative' (also referred to as 'continuous') genotype data. It is demonstrated with simulated data that, when applied to pools, this 'quantitative maximum likelihood' method assigns parentage with greater accuracy than established maximum likelihood parentage-assignment approaches, which rely on accurate discrete genotype calls; exclusion methods; and estimating parental contributions to pools by solving the weighted least squares problem. Quantitative maximum likelihood can be applied to pools generated using either a 'pooling-for-individual-parentage-assignment' approach, whereby each individual in a pool is tagged or traceable and from a known and mutually exclusive set of possible parents; or a 'pooling-by-phenotype' approach, whereby individuals of the same, or similar, phenotype/s are pooled. Although computationally intensive when applied to large pools, quantitative maximum likelihood has the potential to substantially reduce the cost of parentage assignment, even if applied to pools comprised of few individuals.

Genomic Selection for F1 Hybrid Breeding in Strawberry (Fragaria × ananassa)

Cultivated strawberry is the most widely consumed fruit crop in the world, and therefore, many breeding programs are underway to improve its agronomic traits such as fruit quality. Strawberry cultivars were vegetatively propagated through runners and carried a high risk of infection with viruses and insects. To solve this problem, the development of F₁ hybrid seeds has been proposed as an alternative breeding strategy in strawberry. In this study, we conducted a potential assessment of genomic selection (GS) in strawberry F₁ hybrid breeding. A total of 105 inbred lines were developed as candidate parents of strawberry F₁ hybrids. In addition, 275 parental combinations were randomly selected from the 105 inbred lines and crossed to develop test F₁ hybrids for GS model training. These populations were phenotyped for petiole length, leaf area, Brix, fruit hardness, and pericarp color. Whole-genome shotgun sequencing of the 105 inbred lines detected 20,811 single nucleotide polymorphism sites that were provided for subsequent GS analyses. In a GS model construction, inclusion of dominant effects showed a slight advantage in GS accuracy. In the across population prediction analysis, GS models using the inbred lines showed predictability for the test F₁ hybrids and vice versa, except for Brix. Finally, the GS models were used for phenotype prediction of 5,460 possible F₁ hybrids from 105 inbred lines to select F₁ hybrids with high fruit hardness or high pericarp color. These F₁ hybrids were developed and phenotyped to evaluate the efficacy of the GS. As expected, F₁ hybrids that were predicted to have high fruit hardness or high pericarp color expressed higher observed phenotypic values than the F₁ hybrids that were selected for other objectives. Through the analyses in this study, we demonstrated that GS can be applied for strawberry F₁ hybrid breeding.

Pairwise linkage disequilibrium estimation for polyploids

Many tasks in statistical genetics involve pairwise estimation of linkage disequilibrium (LD). The study of LD in diploids is mature. However, in polyploids, the field lacks a comprehensive characterization of LD. Polyploids also exhibit greater levels of genotype uncertainty than diploids, yet no methods currently exist to estimate LD in polyploids in the presence of such genotype uncertainty. Furthermore, most LD estimation methods do not quantify the level of uncertainty in their LD estimates. Our study contains three major contributions. (i) We characterize haplotypic and composite measures of LD in polyploids. These composite measures of LD turn out to be functions of common statistical measures of association. (ii) We derive procedures to estimate haplotypic and composite LD in polyploids in the presence of genotype uncertainty. We do this by estimating LD directly from genotype likelihoods, which may be obtained from many genotyping platforms. (iii) We derive standard errors of all LD estimators that we discuss. We validate our methods on both real and simulated data. Our methods are implemented in the R package ldsep, available on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low-depth sequencing across a larger fraction of the genome than with high-depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

What do tropical cryptogams reveal? Strong genetic structure in Amazonian bryophytes

Lowland tropical bryophytes have been perceived as excellent dispersers. In such groups, the inverse isolation hypothesis proposes that spatial genetic structure is erased beyond the limits of short-distance dispersal. Here, we determine the influence of environmental variation and geographic barriers on the spatial genetic structure of a widely dispersed and phylogenetically independent sample of Amazonian bryophytes. Single nucleotide polymorphism data were produced from a restriction site-associated DNA sequencing protocol for 10 species and analyzed through F-statistics and Mantel tests. Neither isolation-by-environment nor the impact of geographic barriers were recovered from the analyses. However, significant isolation-by-distance patterns were observed for 8 out of the 10 investigated species beyond the scale of short-distance dispersal (> 1 km), offering evidence contrary to the inverse isolation hypothesis. Despite a cadre of life-history traits and distributional patterns suggesting that tropical bryophytes are highly vagile, our analyses reveal spatial genetic structures comparable to those documented for angiosperms, whose diaspores are orders of magnitude larger. Dispersal limitation for tropical bryophytes flies in the face of traditional assumptions regarding their dispersal potential, and suggests that the plight of this component of cryptic biodiversity is more dire than previously considered in light of accelerated forest fragmentation in the Amazon.

Genetic Mapping in Autohexaploid Sweet Potato with Low-Coverage NGS-Based Genotyping Data

Next-generation sequencing (NGS)-based genotyping methods can generate numerous genetic markers in a single experiment and have contributed to plant genetic mapping. However, for high precision genetic analysis, the complicated genetic segregation mode in polyploid organisms requires high-coverage NGS data and elaborate analytical algorithms. In the present study, we propose a simple strategy for the genetic mapping of polyploids using low-coverage NGS data. The validity of the strategy was investigated using simulated data. Previous studies indicated that accurate allele dosage estimation from low-coverage NGS data (read depth < 40) is difficult. Therefore, we used allele dosage probabilities calculated from read counts in association analyses to detect loci associated with phenotypic variations. The allele dosage probabilities showed significant detection power, although higher allele dosage estimation accuracy resulted in higher detection power. On the contrary, differences in the segregation patterns between the marker and causal genes resulted in a drastic decrease in detection power even if the marker and casual genes were in complete linkage and the allele dosage estimation was accurate. These results indicated that the use of a larger number of markers is advantageous, even if the accuracy of allele dosage estimation is low. Finally, we applied the strategy for the genetic mapping of autohexaploid sweet potato (Ipomoea batatas) populations to detect loci associated with agronomic traits. Our strategy could constitute a cost-effective approach for preliminary experiments done performed to large-scale studies.

Training Population Optimization for Genomic Selection in Miscanthus

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.

Genotype calling of triploid offspring from diploid parents

BACKGROUND

Polyploidy is widespread in animals and especially in plants. Different kinds of ploidies exist, for example, hexaploidy in wheat, octaploidy in strawberries, and diploidy, triploidy, tetraploidy, and pseudo-tetraploidy (partly tetraploid) in fish. Triploid offspring from diploid parents occur frequently in the wild in Atlantic salmon (Salmo salar) and, as with triploidy in general, the triploid individuals are sterile. Induced triploidy in Atlantic salmon is common practice to produce sterile fish. In Norwegian aquaculture, production of sterile triploid fish is an attempt by government and industry to limit genetic introgression between wild and farmed fish. However, triploid fish may have traits and properties that differ from those of diploids. Investigating the genetics behind traits in triploids has proved challenging because genotype calling of genetic markers in triploids is not supported by standard software. Our aim was to develop a method that can be used for genotype calling of genetic markers in triploid individuals.

RESULTS

Allele signals were produced for 381 triploid Atlantic salmon offspring using a 56 K Thermo Fisher GeneTitan genotyping platform. Genotypes were successfully called by applying finite normal mixture models to the (transformed) allele signals. Subsets of markers were filtered by quality control statistics for use with downstream analyses. The quality of the called genotypes was sufficient to allow for assignment of diploid parents to the triploid offspring and to discriminate between maternal and paternal parents from autosomal inheritance patterns. In addition, as the maternal inheritance in triploid offspring is identical to gynogenetic inheritance, the maternal recombination pattern for each chromosome could be mapped by using a similar approach as that used in gene-centromere mapping.

CONCLUSIONS

We show that calling of dense marker genotypes for triploid individuals is feasible. The resulting genotypes can be used in parentage assignment of triploid offspring to diploid parents, to discriminate between maternal and paternal parents using autosomal inheritance patterns, and to map the maternal recombination pattern using an approach similar to gene-centromere mapping. Genotyping of triploid individuals is important both for selective breeding programs and unravelling the underlying genetics of phenotypes recorded in triploids. In principle, the developed method can be used for genotype calling of other polyploid organisms.