Predictive Models of Recombination Rate Variation across the Drosophila melanogaster Genome

Targeted recombination of homologous chromosomes using CRISPR-Cas9

CRISPR mutagenesis is an efficient way to disrupt specific target genes in many model organisms. We previously devised a targeted CRISPR recombination method to generate intragenic recombinants of alleles in Drosophila. Here, we assessed the applicability of CRISPR targeting-induced recombination to different genetic loci. We compared the ectopic recombination rates in the male germline by CRISPR targeting at two neighboring genetic loci within the genomic region that consists of the repressed chromatin domain of the Lobe gene, and the transcriptionally active domain of PRAS40. Targeting around the transcription initiation of PRAS40 resulted in higher recombination rates of homologous chromosomes than targeting at the Lobe intron. Based on the efficient homologous recombination by CRISPR targeting observed around transcriptionally active loci, we further investigated targeted recombination between P-elements that are inserted at different genomic locations. Male recombination by CRISPR targeting of P-elements located proximally and distally to the ebony gene produced recombinants deficient for the intervening region of ebony transcription. Taken together, we suggest that targeted homologous recombination by CRISPR targeting may have specific genetic applications, such as generation of allelic combinations or chromosomal variations.

Modeling the evolution of recombination plasticity: A prospective review

Meiotic recombination is one of the main sources of genetic variation, a fundamental factor in the evolutionary adaptation of sexual eukaryotes. Yet, the role of variation in recombination rate and other recombination features remains underexplored. In this review, we focus on the sensitivity of recombination rates to different extrinsic and intrinsic factors. We briefly present the empirical evidence for recombination plasticity in response to environmental perturbations and/or poor genetic background and discuss theoretical models developed to explain how such plasticity could have evolved and how it can affect important population characteristics. We highlight a gap between the evidence, which comes mostly from experiments with diploids, and theory, which typically assumes haploid selection. Finally, we formulate open questions whose solving would help to outline conditions favoring recombination plasticity. This will contribute to answering the long-standing question of why sexual recombination exists despite its costs, since plastic recombination may be evolutionary advantageous even in selection regimes rejecting any non-zero constant recombination.

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

Introduction: Deep learning technology has been widely used in genetic research because of its characteristics of computability, statistical analysis, and predictability. Herein, we aimed to summarize standardized knowledge and potentially innovative approaches for deep learning applications of genetics by evaluating publications to encourage more research.

Methods: The Science Citation Index Expanded TM (SCIE) database was searched for deep learning applications for genomics-related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics, and emerging topics.

Results: We assessed the rapidly increasing publications concerned about deep learning applications of genomics approaches and identified 1,754 articles that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, and the US has been in close collaborations with China and Germany. The reference clusters of SCI articles were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq (single-cell RNA-seq), genomic regulation, and recombination. The keywords representing the research frontiers by year were prediction (2016–2021), sequence (2017–2021), mutation (2017–2021), and cancer (2019–2021).

Conclusion: Here, we summarized the current literature related to the status of deep learning for genetics applications and analyzed the current research characteristics and future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourages more researchers to overcome the research of deep learning applications in genetics.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Meiotic, genomic and evolutionary properties of crossover distribution in Drosophila yakuba

The number and location of crossovers across genomes are highly regulated during meiosis, yet the key components controlling them are fast evolving, hindering our understanding of the mechanistic causes and evolutionary consequences of changes in crossover rates. Drosophila melanogaster has been a model species to study meiosis for more than a century, with an available high-resolution crossover map that is, nonetheless, missing for closely related species, thus preventing evolutionary context. Here, we applied a novel and highly efficient approach to generate whole-genome high-resolution crossover maps in D. yakuba to tackle multiple questions that benefit from being addressed collectively within an appropriate phylogenetic framework, in our case the D. melanogaster species subgroup. The genotyping of more than 1,600 individual meiotic events allowed us to identify several key distinct properties relative to D. melanogaster. We show that D. yakuba, in addition to higher crossover rates than D. melanogaster, has a stronger centromere effect and crossover assurance than any Drosophila species analyzed to date. We also report the presence of an active crossover-associated meiotic drive mechanism for the X chromosome that results in the preferential inclusion in oocytes of chromatids with crossovers. Our evolutionary and genomic analyses suggest that the genome-wide landscape of crossover rates in D. yakuba has been fairly stable and captures a significant signal of the ancestral crossover landscape for the whole D. melanogaster subgroup, even informative for the D. melanogaster lineage. Contemporary crossover rates in D. melanogaster, on the other hand, do not recapitulate ancestral crossovers landscapes. As a result, the temporal stability of crossover landscapes observed in D. yakuba makes this species an ideal system for applying population genetic models of selection and linkage, given that these models assume temporal constancy in linkage effects. Our studies emphasize the importance of generating multiple high-resolution crossover rate maps within a coherent phylogenetic context to broaden our understanding of crossover control during meiosis and to improve studies on the evolutionary consequences of variable crossover rates across genomes and time.

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC's performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.

Parallel Molecular Evolution in Pathways, Genes, and Sites in High-Elevation Hummingbirds Revealed by Comparative Transcriptomics

High-elevation organisms experience shared environmental challenges that include low oxygen availability, cold temperatures, and intense ultraviolet radiation. Consequently, repeated evolution of the same genetic mechanisms may occur across high-elevation taxa. To test this prediction, we investigated the extent to which the same biochemical pathways, genes, or sites were subject to parallel molecular evolution for 12 Andean hummingbird species (family: Trochilidae) representing several independent transitions to high elevation across the phylogeny. Across high-elevation species, we discovered parallel evolution for several pathways and genes with evidence of positive selection. In particular, positively selected genes were frequently part of cellular respiration, metabolism, or cell death pathways. To further examine the role of elevation in our analyses, we compared results for low- and high-elevation species and tested different thresholds for defining elevation categories. In analyses with different elevation thresholds, positively selected genes reflected similar functions and pathways, even though there were almost no specific genes in common. For example, EPAS1 (HIF2α), which has been implicated in high-elevation adaptation in other vertebrates, shows a signature of positive selection when high-elevation is defined broadly (>1,500 m), but not when defined narrowly (>2,500 m). Although a few biochemical pathways and genes change predictably as part of hummingbird adaptation to high-elevation conditions, independent lineages have rarely adapted via the same substitutions.

DNA Motifs Are Not General Predictors of Recombination in Two Drosophila Sister Species

Meiotic recombination is crucial for chromosomal segregation and facilitates the spread of beneficial and removal of deleterious mutations. Recombination rates frequently vary along chromosomes and Drosophila melanogaster exhibits a remarkable pattern. Recombination rates gradually decrease toward centromeres and telomeres, with a dramatic impact on levels of variation in natural populations. Two close sister species, Drosophila simulans and Drosophila mauritiana do not only have higher recombination rates but also exhibit a much more homogeneous recombination rate that only drops sharply very close to centromeres and telomeres. Because certain sequence motifs are associated with recombination rate variation in D. melanogaster, we tested whether the difference in recombination landscape between D. melanogaster and D. simulans can be explained by the genomic distribution of recombination rate–associated sequence motifs. We constructed the first high-resolution recombination map for D. simulans based on 189 haplotypes from a natural D. simulans population and searched for short sequence motifs linked with higher than average recombination in both sister species. We identified five consensus motifs significantly associated with higher than average chromosome-wide recombination rates in at least one species and present in both. Testing fine resolution associations between motif density and recombination, we found strong and positive associations genome-wide over a range of scales in D. melanogaster, while the results were equivocal in D. simulans. Despite the strong association in D. melanogaster, we did not find a decreasing density of these short-repeat motifs toward centromeres and telomeres. We conclude that the density of recombination-associated repeat motifs cannot explain the large-scale recombination landscape in D. melanogaster, nor the differences to D. simulans. The strong association seen for the sequence motifs in D. melanogaster likely reflects their impact influencing local differences in recombination rates along the genome.

Divergent Fine-Scale Recombination Landscapes between a Freshwater and Marine Population of Threespine Stickleback Fish

Meiotic recombination is a highly conserved process that has profound effects on genome evolution. At a fine-scale, recombination rates can vary drastically across genomes, often localized into small recombination "hotspots" with highly elevated rates, surrounded by regions with little recombination. In most species studied, the location of hotspots within genomes is highly conserved across broad evolutionary timescales. The main exception to this pattern is in mammals, where hotspot location can evolve rapidly among closely related species and even among populations within a species. Hotspot position in mammals is controlled by the gene, Prdm9, whereas in species with conserved hotspots, a functional Prdm9 is typically absent. Due to a limited number of species where recombination rates have been estimated at a fine-scale, it remains unclear whether hotspot conservation is always associated with the absence of a functional Prdm9. Threespine stickleback fish (Gasterosteus aculeatus) are an excellent model to examine the evolution of recombination over short evolutionary timescales. Using a linkage disequilibrium-based approach, we found recombination rates indeed varied at a fine-scale across the genome, with many regions organized into narrow hotspots. Hotspots had highly divergent landscapes between stickleback populations, where only ∼15% of these hotspots were shared. Our results indicate that fine-scale recombination rates may be diverging between closely related populations of threespine stickleback fish. Interestingly, we found only a weak association of a PRDM9 binding motif within hotspots, which suggests that threespine stickleback fish may possess a novel mechanism for targeting recombination hotspots at a fine-scale.

Evolutionary insights from large scale resequencing datasets in Drosophila melanogaster

Drosophila melanogaster has long been used as an evolutionary model system. Its small genome size, well-annotated genome, and ease of sampling, also makes it a choice species for genome resequencing studies. Hundreds of genomic samples from populations worldwide are available and are currently being used to tackle a wide range of evolutionary questions. In this review, we focused on three insights that have increased our understanding of the evolutionary history of this species, and that have implications for the study of evolutionary processes in other species as well. Because of technical limitations, most of the studies so far have focused on SNP variants. However, long-read sequencing techniques should allow us in the near future to include other type of genomic variants that also influence genome evolution.

Supervised Machine Learning for Population Genetics: A New Paradigm

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

Testing the effect of paraquat exposure on genomic recombination rates in queens of the western honey bee, Apis mellifera

The rate of genomic recombination displays evolutionary plasticity and can even vary in response to environmental factors. The western honey bee (Apis mellifera L.) has an extremely high genomic recombination rate but the mechanistic basis for this genome-wide upregulation is not understood. Based on the hypothesis that meiotic recombination and DNA damage repair share common mechanisms in honey bees as in other organisms, we predicted that oxidative stress leads to an increase in recombination rate in honey bees. To test this prediction, we subjected honey bee queens to oxidative stress by paraquat injection and measured the rates of genomic recombination in select genome intervals of offspring produced before and after injection. The evaluation of 26 genome intervals in a total of over 1750 offspring of 11 queens by microsatellite genotyping revealed several significant effects but no overall evidence for a mechanistic link between oxidative stress and increased recombination was found. The results weaken the notion that DNA repair enzymes have a regulatory function in the high rate of meiotic recombination of honey bees, but they do not provide evidence against functional overlap between meiotic recombination and DNA damage repair in honey bees and more mechanistic studies are needed.

Meiotic crossovers are associated with open chromatin and enriched with Stowaway transposons in potato

Background

Meiotic recombination is the foundation for genetic variation in natural and artificial populations of eukaryotes. Although genetic maps have been developed for numerous plant species since the late 1980s, few of these maps have provided the necessary resolution needed to investigate the genomic and epigenomic features underlying meiotic crossovers.

Results

Using a whole genome sequencing-based approach, we developed two high-density reference-based haplotype maps using diploid potato clones as parents. The vast majority (81%) of meiotic crossovers were mapped to less than 5 kb. The fine-scale accuracy of crossover detection was validated by Sanger sequencing for a subset of ten crossover events. We demonstrate that crossovers reside in genomic regions of “open chromatin”, which were identified based on hypersensitivity to DNase I digestion and association with H3K4me3-modified nucleosomes. The genomic regions spanning crossovers were significantly enriched with the Stowaway family of miniature inverted-repeat transposable elements (MITEs). The occupancy of Stowaway elements in gene promoters is concomitant with an increase in recombination rate. A generalized linear model identified the presence of Stowaway elements as the third most important genomic or chromatin feature behind genes and open chromatin for predicting crossover formation over 10-kb windows.

Conclusions

Collectively, our results suggest that meiotic crossovers in potato are largely determined by the local chromatin status, marked by accessible chromatin, H3K4me3-modified nucleosomes, and the presence of Stowaway transposons.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1326-8) contains supplementary material, which is available to authorized users.