Chromosome size in diploid eukaryotic species centers on the average length with a conserved boundary

The Genomics Revolution in Nonmodel Species: Predictions vs. Reality for Salmonids

The increasing feasibility of whole-genome sequencing has been highly anticipated, promising to transform our understanding of the biology of nonmodel species. Notably, dramatic cost reductions beginning around 2007 with the advent of high-throughput sequencing inspired publications heralding the 'genomics revolution', with predictions about its future impacts. Although such predictions served as useful guideposts, value is added when statements are evaluated with the benefit of hindsight. Here, we review 10 key predictions made early in the genomics revolution, highlighting those realised while identifying challenges limiting others. We focus on predictions concerning applied aspects of genomics and examples involving salmonid species which, due to their socioeconomic and ecological significance, have been frontrunners in applications of genomics in nonmodel species. Predicted outcomes included enhanced analytical power, deeper insights into the genetic basis of phenotype and fitness variation, disease management and breeding program advancements. Although many predictions have materialised, several expectations remain unmet due to technological, analytical and knowledge barriers. Additionally, largely unforeseen advancements, including the identification and management applicability of large-effect loci, close-kin mark-recapture, environmental DNA and gene editing have added under-anticipated value. Finally, emerging innovations in artificial intelligence and bioinformatics offer promising new directions. This retrospective evaluation of the impacts of the genomic revolution offers insights into the future of genomics for nonmodel species.

MaxTemp: A Method to Maximise Precision of the Temporal Method for Estimating Ne in Genetic Monitoring Programs

We introduce a new software program, MaxTemp, that increases precision of the temporal method for estimating effective population size (Ne) in genetic monitoring programs, which are increasingly used to systematically track changes in global biodiversity. Scientists and managers are typically most interested in Ne for individual generations, either to match with single-generation estimates of census size (N) or to evaluate consequences of specific management actions or environmental events. Systematically sampling every generation produces a time series of single-generation estimates of temporal F (F ̂ ) $$ \hat{F}\Big) $$ , which can then be used to estimate Ne; however, these estimates have relatively low precision because each reflects just a single episode of genetic drift. Systematic sampling also produces an array of multigenerational temporal estimates that collectively contain a great deal of information about genetic drift that, however, can be difficult to interpret. Here, we show how additional information contained in multigenerational temporal estimates can be leveraged to increase precision ofF ̂ $$ \hat{F} $$ for individual generations. Using information from one additional generation before and after a target generation can reduce the standard deviation ofF ̂ $$ \hat{F} $$ (σ F ̂ $$ {\sigma}_{\hat{F}} $$ ) by up to 50%, which not only tightens confidence intervals aroundN ̂ e $$ {\hat{N}}_e $$ but also reduces the incidence of extreme estimates, including infinite estimates of Ne. Practical application of MaxTemp is illustrated with data for a long-term genetic monitoring program for California delta smelt. A second feature of MaxTemp, which allows one to estimate Ne in an unsampled generation using a combination of temporal and single-sample estimates of Ne from sampled generations, is also described and evaluated.

Potential Benefits and Challenges of Quantifying Pseudoreplication in Genomic Data with Entropy Statistics

Generating vast arrays of genetic markers for evolutionary ecology studies has become routine and cost-effective. However, analyzing data from large numbers of loci associated with a small number of finite chromosomes introduces a challenge: loci on the same chromosome do not assort independently, leading to pseudoreplication. Previous studies have demonstrated that pseudoreplication can substantially reduce precision of genetic analyses (and make confidence intervals wider), such as FST and linkage disequilibrium (LD) measures between pairs of loci. In LD analyses, another type of dependency (overlapping pairs of the same loci) also creates pseudoreplication. Building on previous work, we explore the potential of entropy metrics to improve the status quo, particularly total correlation (TC), to assess pseudoreplication in LD studies. Our simulations, performed on a monoecious population with a range of effective population sizes (Ne) and numbers of loci, attempted to isolate the overlapping-pairs-of-loci effect by considering unlinked loci and using entropy to quantify inter-locus relationships. We hypothesized a positive correlation between TC and the number of loci (L), and a negative correlation between TC and Ne. Results from our statistical models predicting TC demonstrate a strong effect of the number of loci, and muted effects of Ne and other predictors, adding support to the use of entropy-based metrics as a tool for estimating the statistical information of complex genetic datasets. Our results also highlight a challenge regarding scalability; computational limitations arise as the number of loci grows, making our current approach limited to smaller datasets. Despite these challenges, this work further refines our understanding of entropy measures, and offers insights into the complex dynamics of genetic information in evolutionary ecology research.

Dynamics of karyotype evolution

In the evolution of species, the karyotype changes with a timescale of tens to hundreds of thousand years. In the development of cancer, the karyotype often is modified in cancerous cells over the lifetime of an individual. Characterizing these changes and understanding the mechanisms leading to them has been of interest in a broad range of disciplines including evolution, cytogenetics, and cancer genetics. A central issue relates to the relative roles of random vs deterministic mechanisms in shaping the changes. Although it is possible that all changes result from random events followed by selection, many results point to other non-random factors that play a role in karyotype evolution. In cancer, chromosomal instability leads to characteristic changes in the karyotype, in which different individuals with a specific type of cancer display similar changes in karyotype structure over time. Statistical analyses of chromosome lengths in different species indicate that the length distribution of chromosomes is not consistent with models in which the lengths of chromosomes are random or evolve solely by simple random processes. A better understanding of the mechanisms underlying karyotype evolution should enable the development of quantitative theoretical models that combine the random and deterministic processes that can be compared to experimental determinations of the karyotype in diverse settings.

Practical application of the linkage disequilibrium method for estimating contemporary effective population size: A review

The method to estimate contemporary effective population size (Ne ) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy.

ARHap: Association Rule Haplotype Phasing

This article proposes a novel approach for Individual Human phasing through discovery of interesting hidden relations among single variant sites. The proposed framework, called ARHap, learns strong association rules among variant loci on the genome and develops a combinatorial approach for fast and accurate haplotype phasing based on the discovered associations. ARHap is composed of two main modules or processing phases. In the first phase, called association rule learning, ARHap identifies quantitative association rules from a collection of DNA reads of the organism under study, resulting in a set of strong rules that reveal the inter-dependency of alleles. In the next phase, called haplotype reconstruction, we develop algorithms to utilize the learned rules to construct highly reliable haplotypes at individual single nucleotide polymorphism (SNP) sites. ARHap has several features that lead to both fast and accurate haplotyping. It uses an incremental haplotype reconstruction approach that enables us to generate association rules according to the unreconstructed SNP sites during each round of the algorithm. During each round, the association rule learning module generates rules while constraining the length of the rules and limiting the rules to those that contribute to reconstruction of unreconstructed sites only. The framework begins by generating rules of small size and highly strong. The rule length can increase and/or criteria about strongness of the rule are adjusted gradually, during subsequent rounds, if some SNP sites have remained unreconstructed. This adaptive approach, which uses feedback from haplotype reconstruction module, eliminates generation of rules that do not contribute to haplotype reconstruction as well as weak rules that may introduce error in the final haplotypes. Extensive experimental analyses on datasets representing diploid organisms demonstrate superiority of ARHap in diploid haplotyping compared to the state-of-the-art algorithms. In particular, we show that this novel approach to haplotype phasing not only is fast but also achieves significantly better accuracy performance compared to other read-based computational approaches.

Celebrating Mendel, McClintock, and Darlington: On end-to-end chromosome fusions and nested chromosome fusions

The evolution of eukaryotic genomes is accompanied by fluctuations in chromosome number, reflecting cycles of chromosome number increase (polyploidy and centric fissions) and decrease (chromosome fusions). Although all chromosome fusions result from DNA recombination between two or more nonhomologous chromosomes, several mechanisms of descending dysploidy are exploited by eukaryotes to reduce their chromosome number. Genome sequencing and comparative genomics have accelerated the identification of inter-genome chromosome collinearity and gross chromosomal rearrangements and have shown that end-to-end chromosome fusions (EEFs) and nested chromosome fusions (NCFs) may have played a more important role in the evolution of eukaryotic karyotypes than previously thought. The present review aims to summarize the limited knowledge on the origin, frequency, and evolutionary implications of EEF and NCF events in eukaryotes and especially in land plants. The interactions between nonhomologous chromosomes in interphase nuclei and chromosome (mis)pairing during meiosis are examined for their potential importance in the origin of EEFs and NCFs. The remaining open questions that need to be addressed are discussed.

3D chromatin remodelling in the germ line modulates genome evolutionary plasticity

Chromosome folding has profound impacts on gene regulation, whose evolutionary consequences are far from being understood. Here we explore the relationship between 3D chromatin remodelling in mouse germ cells and evolutionary changes in genome structure. Using a comprehensive integrative computational analysis, we (i) reconstruct seven ancestral rodent genomes analysing whole-genome sequences of 14 species representatives of the major phylogroups, (ii) detect lineage-specific chromosome rearrangements and (iii) identify the dynamics of the structural and epigenetic properties of evolutionary breakpoint regions (EBRs) throughout mouse spermatogenesis. Our results show that EBRs are devoid of programmed meiotic DNA double-strand breaks (DSBs) and meiotic cohesins in primary spermatocytes, but are associated in post-meiotic cells with sites of DNA damage and functional long-range interaction regions that recapitulate ancestral chromosomal configurations. Overall, we propose a model that integrates evolutionary genome reshuffling with DNA damage response mechanisms and the dynamic spatial genome organisation of germ cells.

The role of genome folding in the heritability and evolvability of structural variations is not well understood. Here the authors investigate the impact of the three-dimensional genome topology of germ cells in the formation and transmission of gross structural genomic changes detected from comparing whole-genome sequences of 14 rodent species.

What is N e, anyway?

Few doubt that effective population size (Ne) is one of the most important parameters in evolutionary biology, but how many can say they really understand the concept? Ne is the evolutionary analogue of the number of individuals (or adults) in the population, N. Whereas ecological consequences of population size depend on N, evolutionary consequences (rates of loss of genetic diversity and increase in inbreeding; relative effectiveness of selection) depend on Ne. Formal definitions typically relate effective size to a key population genetic parameter, such as loss of heterozygosity or variance in allele frequency. However, for practical application to real populations, it is more useful to define Ne in terms of three demographic parameters: number of potential parents (adult N), and mean and variance in offspring number. Defined this way, Ne determines the rate of random genetic drift across the entire genome in the offspring generation. Other evolutionary forces (mutation, migration, selection)—together with factors such as variation in recombination rate—can also affect genetic variation, and this leads to heterogeneity across the genome in observed rates of genetic change. For some, it has been convenient to interpret this heterogeneity in terms of heterogeneity in Ne, but unfortunately this has muddled the concepts of genetic drift and effective population size. A commonly-repeated misconception is that Ne is the number of parents that actually contribute genes to the next generation (NP). In reality, NP can be smaller or larger than Ne, and the NP/Ne ratio depends on the sex ratio, the mean and variance in offspring number, and whether inbreeding or variance Ne is of interest.

Chromosome size affects sequence divergence between species through the interplay of recombination and selection

The structure of the genome shapes the distribution of genetic diversity and sequence divergence. To investigate how the relationship between chromosome size and recombination rate affects sequence divergence between species, we combined empirical analyses and evolutionary simulations. We estimated pairwise sequence divergence among 15 species from three different mammalian clades-Peromyscus rodents, Mus mice, and great apes-from chromosome-level genome assemblies. We found a strong significant negative correlation between chromosome size and sequence divergence in all species comparisons within the Peromyscus and great apes clades but not the Mus clade, suggesting that the dramatic chromosomal rearrangements among Mus species may have masked the ancestral genomic landscape of divergence in many comparisons. Our evolutionary simulations showed that the main factor determining differences in divergence among chromosomes of different sizes is the interplay of recombination rate and selection, with greater variation in larger populations than in smaller ones. In ancestral populations, shorter chromosomes harbor greater nucleotide diversity. As ancestral populations diverge, diversity present at the onset of the split contributes to greater sequence divergence in shorter chromosomes among daughter species. The combination of empirical data and evolutionary simulations revealed that chromosomal rearrangements, demography, and divergence times may also affect the relationship between chromosome size and divergence, thus deepening our understanding of the role of genome structure in the evolution of species divergence.

The Sister Chromatid Division of the Heteromorphic Sex Chromosomes in Silene Species and Their Transmissibility towards the Mitosis

Young sex chromosomes possess unique and ongoing dynamics that allow us to understand processes that have an impact on their evolution and divergence. The genus Silene includes species with evolutionarily young sex chromosomes, and two species of section Melandrium, namely Silene latifolia (24, XY) and Silene dioica (24, XY), are well-established models of sex chromosome evolution, Y chromosome degeneration, and sex determination. In both species, the X and Y chromosomes are strongly heteromorphic and differ in the genomic composition compared to the autosomes. It is generally accepted that for proper cell division, the longest chromosomal arm must not exceed half of the average length of the spindle axis at telophase. Yet, it is not clear what are the dynamics between males and females during mitosis and how the cell compensates for the presence of the large Y chromosome in one sex. Using hydroxyurea cell synchronization and 2D/3D microscopy, we determined the position of the sex chromosomes during the mitotic cell cycle and determined the upper limit for the expansion of sex chromosome non-recombining region. Using 3D specimen preparations, we found that the velocity of the large chromosomes is compensated by the distant positioning from the central interpolar axis, confirming previous mathematical modulations.

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df') compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df'/df) for a common metric of genetic differentiation (F_ST ) and a common measure of linkage disequilibrium between pairs of loci (r² ). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df' and df'/df by measuring the rate of decline in the variance of mean F_ST and mean r² as more loci were used. For both indices, df' increases with N_e and genome size, as expected. However, even for large N_e and large genomes, df' for mean r² plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for F_ST , but df'/df ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(F_ST ), producing very conservative confidence intervals. Predicting df' based on our modeling results as a function of N_e , L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

Synteny-Based Genome Assembly for 16 Species of Heliconius Butterflies, and an Assessment of Structural Variation across the Genus

Heliconius butterflies (Lepidoptera: Nymphalidae) are a group of 48 neotropical species widely studied in evolutionary research. Despite the wealth of genomic data generated in past years, chromosomal level genome assemblies currently exist for only two species, Heliconius melpomene and Heliconius erato, each a representative of one of the two major clades of the genus. Here, we use these reference genomes to improve the contiguity of previously published draft genome assemblies of 16 Heliconius species. Using a reference-assisted scaffolding approach, we place and order the scaffolds of these genomes onto chromosomes, resulting in 95.7-99.9% of their genomes anchored to chromosomes. Genome sizes are somewhat variable among species (270-422 Mb) and in one small group of species (Heliconius hecale, Heliconius elevatus, and Heliconius pardalinus) expansions in genome size are driven mainly by repetitive sequences that map to four small regions in the H. melpomene reference genome. Genes from these repeat regions show an increase in exon copy number, an absence of internal stop codons, evidence of constraint on nonsynonymous changes, and increased expression, all of which suggest that at least some of the extra copies are functional. Finally, we conducted a systematic search for inversions and identified five moderately large inversions fixed between the two major Heliconius clades. We infer that one of these inversions was transferred by introgression between the lineages leading to the erato/sara and burneyi/doris clades. These reference-guided assemblies represent a major improvement in Heliconius genomic resources that enable further genetic and evolutionary discoveries in this genus.

Random models of Menzerath-Altmann law in genomes

Recently, a random breakage model has been proposed to explain the negative correlation between mean chromosome length and chromosome number that is found in many groups of species and is consistent with Menzerath-Altmann law, a statistical law that defines the dependency between the mean size of the whole and the number of parts in quantitative linguistics. Here, the central assumption of the model, namely that genome size is independent from chromosome number is reviewed. This assumption is shown to be unrealistic from the perspective of chromosome structure and the statistical analysis of real genomes. A general class of random models, including that random breakage model, is analyzed. For any model within this class, a power law with an exponent of -1 is predicted for the expectation of the mean chromosome size as a function of chromosome length, a functional dependency that is not supported by real genomes. The random breakage and variants keeping genome size and chromosome number independent raise no serious objection to the relevance of correlations consistent with Menzerath-Altmann law across taxonomic groups and the possibility of a connection between human language and genomes through that law.

Making a long story short: noncoding RNAs and chromosome change

As important as the events that influence selection for specific chromosome types in the derivation of novel karyotypes, are the events that initiate the changes in chromosome number and structure between species, and likewise polymorphisms, variants and disease states within species. Although once thought of as transcriptional 'noise', noncoding RNAs (ncRNAs) are now recognized as important mediators of epigenetic regulation and chromosome stability. Here we highlight recent work that illustrates the influence short and long ncRNAs have as participants in the function and stability of chromosome regions such as centromeres, telomeres, evolutionary breakpoints and fragile sites. We summarize recent evidence that ncRNAs can facilitate chromosome change and present mechanisms by which ncRNAs create DNA breaks. Finally, we present hypotheses on how they may create novel karyotypes and thus affect chromosome evolution.