The interplay of recombination landscape, a transposable element and population history in European populations ofChironomus riparius

The interplay of recombination landscape, a transposable element and population history in European populations of Chironomus riparius | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The interplay of recombination landscape, a transposable element and population history in European populations of Chironomus riparius View ORCID Profile Laura C. Pettrich , View ORCID Profile Robert King , View ORCID Profile Linda M. Field , View ORCID Profile Ann-Marie Waldvogel doi: https://doi.org/10.1101/2024.04.04.588075 Laura C. Pettrich 1 Institute of Zoology, University of Cologne , Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura C. Pettrich For correspondence: l.pettrich{at}uni-koeln.de a.waldvogel{at}uni-koeln.de Robert King 2 Rothamsted Research , Harpenden, Hertfordshire, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robert King Linda M. Field 2 Rothamsted Research , Harpenden, Hertfordshire, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Linda M. Field Ann-Marie Waldvogel 1 Institute of Zoology, University of Cologne , Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ann-Marie Waldvogel For correspondence: l.pettrich{at}uni-koeln.de a.waldvogel{at}uni-koeln.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Genome resolution is often constrained for non-model species. This can be challenging for population genomic studies as estimations are highly dependent on the quality of the reference genome. This is the case for population history inferences where accuracy relies on the correct detection of single-nucleotide polymorphisms (SNPs) and accurate recombination rates. Here, we utilize a novel long-read genome assembly of Chironomus riparius with high resolution at a chromosome-scale and reanalyse Illumina resequencing data of five European populations. With the model MSMC2, new population demographies were inferred and compared to an older study based on a fragmented genome. Assembly contiguity and completeness led to an increase in accuracy of past demography, suggesting the onset of divergence of an ancestral population between late Pleistocene and early Holocene. These estimates are additionally supported by paleotemperature data, which reveal significant climate shifts in Central Europe during these times. Recombination severely influences population history estimates. With the new reference genome, it was possible to resolve the recombination landscape on the genome-wide scale across different populations using the tool iSMC. Recombination and the dispersal of transposable elements (TEs) in the genome are suspected to influence each other. One TE, known as Cla -element, is suspected to be involved in the divergence of the different C. riparius populations. As it seems to be highly dynamic in the genome, its potential impact on the recombination landscape was explored. No global pattern could be detected which demonstrated a higher resistance of the recombination landscape to the impact of repetitive elements on genome integrity. Significance Population history models can be influenced by recombination variation. Based on a new genome assembly the recombination variation of different European Chironomus riparius populations was explored and compared to a transposable element suspected of driving population divergence. Resolving influencers of recombination variation have the potential to improve population demography studies. Introduction Patterns of population growth or decline ( Li & Durbin 2011 ; Schiffels & Durbin 2014 ) and admixture among populations ( Luikart et al. 2003 ) can be read out from genomic data. Studying evolution and demography of natural populations is facilitated by whole-genome resequencing data of multiple individuals ( Waldvogel et al. 2020 ; Bourgeois & Warren 2021 ). Thus, a rise in population genomic studies can be associated with the increasing availability of high-quality reference genomes due to advances in long-read sequencing technologies ( Guiglielmoni et al. 2023 ; The Darwin Tree of Life Project Consortium 2022; Blaxter et al. 2022 ). Chironomus riparius , a non-biting midge, commonly known as the harlequin fly and distributed in Europe, has been the focus of many molecular genomic studies (e.g. Oppold et al. 2017 and Waldvogel et al. 2018), with work dating back to 1984 ( Hägele 1984 ). Polytene chromosomes in their salivary glands became the basis to study the species’ genome structure and trace banding patterns of heterochromatic regions in the genome during crossings with related Chironomus species ( Schmidt 1981 ; Hankeln & Schmidt 1987 ; Bovero et al. 2002 ; Hägele 1984 ). These early studies started to document the species’ genome and many molecular genetic works followed, applying different methodology and sequencing strategies ( Schreiber & Pfenninger 2021 ; Oppold et al. 2017 ; Schmidt et al. 2020 ). In this respect, the Chironomus riparius genome project also reflects the continuous advancement of sequencing technology and assembly strategy. With this study, we present a high-quality genome assembly for the species that reaches chromosome scale, marking a new milestone for future studies with this model system. Furthermore, this genomic resource now allows for a more accurate estimation of population history and reconstruction of the recombination landscape. For the inference of population demography, sequential Markovian coalescent models (SMC) ( Li & Durbin 2011 ; Schiffels & Wang 2020 ; Wilton et al. 2015 ) trace back mutation and recombination events. In this, a hidden Markov model (HMM) ( Wiuf & Hein 1999 ) in a sequential Markovian coalescence (SMC) framework ( Marjoram & Wall 2006 ; McVean & Cardin 2005 ) derives hidden states together with its transition rates and estimates coalescence times along the sequence. Applying genomic approaches like these to a broader taxonomic spectrum can help to revisit traditional and long-standing questions about the evolution of genetic diversity and genomic mechanisms of biodiversification ( Blaxter et al. 2022 ).The Lewontin’s paradox ( Lewontin 1974 ), one such long-standing open question of biology, suggests that neutral genetic diversity remains stable between species despite varying population census sizes with many orders of magnitude differences ( Charlesworth & Jensen 2022 ; Roberts 2015 ). It has been discussed whether natural selection causes this kind of equilibrium between population size and heterozygosity. Advantageous variations that are passed down to the next generation, together with hitchhiking genes, might cause a reduction in diversity of certain genomic regions ( Roberts 2015 ). Eradicating disadvantageous variations may have a similar effect ( Roberts 2015 ). Determining the molecular cause of Lewontin’s paradox remains challenging due to the convolution of genomic signatures that result from selection and/or demographic changes ( Charlesworth & Jensen 2022 ). It is thus crucial to explore aspects that can help to improve approaches that allow for a more accurate inference of demographic histories and resolve periods of expansion in population size ( Charlesworth & Jensen 2022 ), in particular for a broader range of taxa that also differ in their life history traits ( Sellinger et al. 2020 ). Gaining a comprehensive understanding of the crucial parameters, such as recombination and mutation rates, is essential to accurately infer population histories. Mutation rates have been shown to depend on certain environmental conditions ( Waldvogel & Pfenninger 2021 ) and recombination rates show variations on a chromosomal scale differing tremendously between species ( Nachman 2002 ). Genomic regions with low recombination often show lower nucleotide diversity ( Nachman 2002 ). When inferring population history, it may be necessary to exclude heterogeneous regions with five-fold differences in recombination rates, as false estimates in past effective population sizes have been documented ( Sellinger et al. 2021 ). On top of this, the landscape of a genome is severely impacted by genetic markers, with transposable elements (TEs) playing a key role in genomic rearrangements ( Montgomery et al. 1991 ). Despite this knowledge on the genomic impact of TEs, how they influence the genome-wide landscape of recombination is still poorly understood ( Kent et al. 2017 ). It appears that TEs are often associated with low recombing regions and the extent of interaction hints at a possible contribution of TE insertions to recombination rate variation (Kent et al. 2017). Repetitive regions are generally expected to have lower recombination rates (Kent et al. 2017), especially in centromere regions and regions with large regions of heterochromatin, where recombination is known to be reduced, while it can be elevated in telomere regions (Kent et al. 2017; Nachman 2002 ). Recombination hotspots are known to occur in GC-rich regions ( Marsolier-Kergoat & Yeramian 2009 ). A high-quality genome gives access to regions that are known to vary in recombination rates ( Pollard et al. 2018 ; Nachman 2002 ) which is expected to make demographic estimates more robust, as subsequently more information uncovering genealogy becomes available. Recombination is an important factor to consider when using SMC models, as they utilise ancestral recombination events (i.e ancestral recombination graphs (ARG)) to infer population history ( McVean & Cardin 2005 ; Marjoram & Wall 2006 ; Mather et al. 2020 ). As such, it becomes important to look at the whole genomic landscape to investigate population history ( Soni et al. 2024 ). Many population demography models were developed using genome data of humans or model organisms like Drosophila melanogaster and Arabidopsis thaliana ( Li & Durbin 2011 ; Schiffels & Durbin 2014 ) with the result that these models were built to rely on high-quality reference genomes with known genomic characteristics like ploidy, maps of recombination and a mutational landscape. Studying population genomics in a broader taxonomic spectrum, beyond genomically well-investigated model species, however, often involves reference genomes of lower quality and more complex genomic characteristics, such as polyploidy or life cycles with reduced sexual reproduction what affects the frequency of meiosis, i.e. recombination. To account for these challenges, model refinements are in development to better account for violations of SMC models, as for example dormancy ( Sellinger et al. 2020 ). The importance of considering markers like TEs, microsatellites and DNA methylation in demographic inferences are also being explored in model organisms and yield improved results in assessing structuring events like bottlenecks or expansions ( Sellinger et al. 2023 ). Genomic markers with elevated mutation rates have been shown to improve temporal resolution of recent evolutionary estimates ( Sellinger et al. 2023 ) hence showing the importance of including not only Single-Nucleotide Polymorphisms (SNP). The genome of Chironomus riparius contains a ‘special’ transposable element, known as the Cla -element, that has attracted attention in molecular genetic as well as population genomic studies and is hypothesized to have been involved in the speciation process of the species ( Schmidt 1984 ; Oppold et al. 2017 ). The Cla -element has a consensus sequence of 120 bp ( Schmidt 1981 ) and can be found in repetitive clusters ( Hankeln et al. 2011 ) or as monomeric repeats ( Hankeln & Schmidt 1987 ). The element has been shown to drive genomic patterns of population divergence in C. riparius ( Oppold et al. 2017 ). Genome-wide patterns of Cla-element insertions were found to vary among natural populations, which, together with the finding of Cla transcripts and heterozygous monomer insertions, led to the assumption of ongoing transposition activity of the element ( Oppold et al. 2017 ). In the model organism Drosophila , piRNA and siRNA pathways are involved in the control of different TEs ( Mérel et al. 2020 ) and it seems that piRNA-mediated silencing could be ancestral in arthropods ( Lewis et al. 2018 ). In the grasshopper Angaracris rhodopa lower piRNA concentrations are suspected to be involved in the expansion of its genome due to higher TE activity ( Liu et al. 2022 ). In the Antarctic midge chironomid Belgica antarctica piRNAs have been documented which might explain low TE abundance ( Kozeretska et al. 2022 ; Kelley et al. 2014 ). As such, regulation of TEs may also play an important role in the genome evolution of C. riparius . Compared to its sister species C. piger , C. riparius has a larger genome ( Hankeln & Schmidt 1987 ; Schaefer & Schmidt 1981 ). A lack of Cla -element suppression could be the explanation for the spread of this element into chromosomal arms of C. riparius compared to its sister species C. piger Crossing of different populations resulted in less fit offspring and it was observed that heterozygous Cla -element patterns lead to incorrect synapsis of homologous chromosomes ( Oppold et al. 2017 ). The Cla -element seems to cause Dobzhansky-Muller incompatibilities (DMI) between the investigated populations, showing the evolution of an endogenous genetic barrier ( Oppold et al. 2017 ). This TE is known to form hairpin loop formations ( Schmidt 1984 ; Israelewski 1983 ) which might cause ectopic recombination events ( Oppold et al. 2017 ). These different aspects indicate a particular speciation potential of the Cla -element in C. riparius , making it particularly relevant to investigate its impact on the recombination landscape of the diverging populations. This study aims to investigate the interplay of recombination, the Cla -element as a candidate TE with high potential to impact in recombination processes and population history of the non-biting midge C. riparius . Utilising a new long-read reference genome, this is the first attempt to understand variation of the recombination landscape in different populations of the harlequin fly while comparing them to the pattern of a particular transposable element (TE)-the Cla -element. For this species and the exact same populations, a previous study has been investigating the population history based on a fragmented genome draft ( Waldvogel et al. 2018 ). Direct comparison of the inferred demographic histories to Waldvogel et al. (2018) provides direct insight into the importance and impact of the resolution of a reference genome on SMC studies. To estimate the population demography, we used the Multiple Markovian Coalescent (MSMC2) model, which uses a hidden Markov model to estimate genealogies along the sequences ( Schiffels & Wang 2020 ). Further correlating the demographic estimates with climate history models ( Karger et al. 2021 ), we outline how such genomic data can help to understand biogeographic histories and how additional data sources, here climate data, can serve as validation stringency when interpreting genomically inferred demographic histories. We also test the hypothesis that recombination landscape and distribution of TEs (here the Cla -element) are correlated and population-specific. Results Influence of improved genome assembly on inferred population history We mapped the Illumina reads of five European populations of C. riparius , from Hesse in Germany (MG), Rhône-Alpes (MF) and Lorraine (NMF) in France, Piemont in Italy (SI) and Andalusia in Spain (SS), to the novel high-quality reference genome assembly (mapping statistics can be found in Supplementary Table S3). The assembly resolves all four chromosomes of C. riparius with ten remaining unplaced scaffolds. Genomes of two endosymbionts, Enterobacter sp. and Wolbachia sp. were assembled from the metafraction of the data. The C. riparius ’ assembly spans 192 Mb with N50 of 59 Mb ( Table 1 , Fig. 1A ). The assembly’s completeness when compared to the single ortholog database of Diptera (diptera_odb10, n=3285) scores 92.6 % complete, 1.8 % duplicated, 1.3 % fragmented and 6.1 % missing BUSCO genes. Download figure Open in new tab Figure 1: A) Snail plot summarising the assembly statistics. Scaffold statistics can be found on the top right. The longest scaffold (i.e. chromosome 1) is marked in red. The N50 value is marked in orange and the N90 value in pale orange. The GC and AT composition are in dark and light blue. A BUSCO analysis was performed against the diptera_odb10 database and values on completeness, fragmentation, duplication, and missing genes can be found on the top right. B) Plot of the four chromosomes with the total number of Cla -elements found by MELT across all populations. Red lines indicate Cla -elements. C) Density plots showing the distribution of the different lengths of Cla -element clusters (in kb) for each chromosome. View this table: View inline View popup Download powerpoint Table 1: Assembly statistics of the genome. Chromosome numbers were ranked with descending length, with chromosome 1 being the longest scaffold of 61 Mb. The genome-wide GC-content was estimated to be 30.7 %. The assembly had a repeat content of 15.85 %. In total 15439 protein-coding genes were annotated. Cla -elements were detected using RepeatMasker and MELT and a total number of 433 Cla -elements insertions were detected (1492 monomeric repeats were detected with RepeatMasker) across the four chromosomes integrating all populations ( Figure 1B ), with 8 insertions on the unplaced scaffolds. Cla-elements are distributed between chromosomes with 148 on chromosome 1, 143 on chromosome 2, 119 on chromosome 3 and 20 on chromosome 4. The distribution of the different Cla -element cluster lengths (in kb) for each chromosome can be found in Figure 1C . The Multiple Sequentially Markovian Coalescent (MSMC2) model was used to infer the population history of the five European C. riparius populations based on the novel high-quality genome assembly (details on the input files in Supplementary Table S4). We assessed admixture between populations ( Figure 2A ) and the history of effective population size ( Figure 2B ). The first five and the last time intervals of the estimation were excluded from the analysis to avoid uncertainties caused by the model due to overfitting. To improve the estimation robustness of the recent time horizon, we determined the time to the most recent common ancestor (tMRCA) of the individuals of a population by integrating a mean heterozygosity of 0.0083 and an informative mean haplotype length (MHL) of 6,023 bases. The tMRCA was determined to be 6,092 generations, when using the mean recombination rate of C. riparius (1.36 cM/Mb) or, alternatively, 3,953 generations when applying the recombination rate of D. melanogaster (2.1 cM/Mb) as applied in Waldvogel et al. (2018) . Considering the species-specific recombination rate, informative time intervals range from 6,100 to 7,400,000 generations. When translating these coalescence times in years, the tMRCA in generations was multiplied with the mean generation time of the respective population (referring to estimates reported in Oppold et al. 2016 ) which resulted in a tMRCA of 609 years (see also Supplementary Table S6). After incorporating the generation time of each population into the inferred population history, the coalescence estimates of the ancient past converged, supported by a high relative cross-coalescence rate (rCCR) ( Figure 2A ) indicative for good admixture between populations. This admixture persisted until a peak 20,000 years ago ( Figure 2B ). Subsequent to the peak, in the more recent past, the effective population size experienced a decline, leading to a dispersal of the population-specific estimates. Concurrently, there was a reduction in rCCR estimated. The rCCR ranges between 0 and 1 and every value above one is considered an artefact caused by the model. A high rCCR indicates a high admixture between populations which can be found for our populations earlier than 10,000 years ago. Over time the admixture decreased and when the rCCR was below 0.5, the populations are estimated to have split into subpopulations. This process was estimated to have started approximately 10,000 years ago. We compared the inferred population demography to paleoclimate models from the CHELSA database, to assess whether certain temperature events could be responsible for the formation of different subpopulations ( Figure 2C -F). By reviewing the annual mean temperature of the population’s habitat over time ( Figure 2C ) a gradient can be found. Hesse and Lorraine are the coldest regions, followed by Rhône-Alpes and Piemont and the warmest region has been Andalusia. Shifting the focus from only the population’s habitat to the European region ( Figure 2D -F) a warming of all of Europe can be observed. The Last-Glacial Maximum (LGM) was 22,000 years before present (22k-BP) where many regions had a mean temperature lower than 0 °C ( Figure 2F ). In the latest period, 1k-BP ( Figure 2D ), most of the regions were warmer with only mountain ranges and the far North showing mean temperatures lower than 0 °C. The historical temperature estimates north of the alps indicate a shift from relatively cold temperatures (-15 °C to 5 °C) to moderate temperatures (0 °C to 15 °C). According to the model, southern Europe exhibited a consistently moderate climate (5 °C to 20 °C) starting from the earliest time point. Download figure Open in new tab Figure 2: Comparison of the demographic history of C. riparius populations to available paleoclimate models. A) The relative cross-coalescence rate of the populations over the years reaching into the past estimated by the MSMC2 model. The population pairs are indicated by colour, as shown in the legend on top. B) Inferred effective population size over years reaching into the past estimated by MSMC2. Colour indicated by legend at the bottom. C) Annual mean temperature every thousand years for the separate sampling locations. D-F) Maps of Europe showing the spatial temperature pattern across Europe 1k-BP, 10k-BP and 22k-BP. Temperature (°C) is indicated by the gradient bar on the right. Dots refer to the sampling locations of the populations with corresponding colour code. Analysing the estimated population demography to the millennial temperature intervals revealed that the zenith of population sizes occurred prior to a temperature decline about 16,000 years ago. The lowest temperature, with an annual mean temperature of-4.6 °C, was registered 17,000 years ago at the site where the MG population is located today ( Figure 2C ). Subsequently, temperatures increased, interrupted only by a minor decrease in temperature 12,000 years ago. The temperature increment slowed down from 7,000 years till 1,000 years ago. The genomic influence of the Cla-element on the recombination landscape To gain insights into the topology of the recombination landscape of the C. riparius populations, we estimated recombination rates (ρ) of the populations with four individuals each in 10 kb windows along the genome using the tool iSMC (Supplementary Table S5). For each chromosome, mean recombination rates were calculated based on the matching windows for each population and then compared to the population-specific position of Cla -elements ( Figure 3 ). The variation in recombination rate across chromosomes was heterogeneous among populations, especially the populations from Lorraine (NMF), Rhône-Alpes (MF) and Piemont (SI) which revealed a lot of intrapopulation variation. Download figure Open in new tab Figure 3: Mean recombination rate (ρ) (n=4) with confidence intervals in 10 kb windows on a log10-scale compared to the position of the Cla -elements along each chromosome, bottom of each chart. Position is given in Mb. Recombination rates and Cla -elements are shown separately for each population and colour-coded accordingly. Red lines mark the presence of a Cla -element. Most Cla -elements were found on chromosome 1 and the fewest on the shortest chromosome 4. When summarising the total number of insertions (shared and unique) 441 insertions were identified. When looking at the populations independently, a total number of 188 insertions for the Andalusia (SS) population, 157 for the Piemont (SI) population, 148 for the Lorraine (NMF) population, 129 f or the Rhône-Alpes (MF) population and 107 for the Hesse (MG) population was detected. To further investigate the relationship between the presence of Cla -elements and the local recombination landscape, we investigated the decay of ρ to the distance of the next Cla-element ( Figure 4 , Supplementary Figure S1, Supplementary Figure S2). For the cluster smaller than 500 bp there was a total of eight chromosomes of the analysed mean ρ with a moderate positive correlation (> 0.125) to the increasing distance of the Cla -element and for one we could find a strong positive correlation (> 0.25). Download figure Open in new tab Figure 4: A-B) Mean recombination rate ρ/bp against distance to nearest Cla-element on chromosome 1. Mean values were calculated for each window based on each individual (n = 20). Displayed in grey are 100 bootstrap values calculated by resampling the mean to visualise its distribution. A) Cla-element cluster size =500 bp. C-D) Pearson correlation between recombination rate ρ and distance to next Cla-element. An asterisk indicates significance (* p < 0.05, ** p < 0.01, *** p < 0.001). Positive correlation indicates a higher recombination rate with a more distant Cla-element. C) Cla-element cluster size =500 bp. For these figures only complete windows of 10 kb were regarded. Cla-elements more distant than 7×10 6 bp were excluded. For clusters larger than or equal to 500 bp there was a moderate positive correlation and a strong positive correlation for five chromosomes each. However, not all chromosomes were positively correlated to higher recombination rates in regions with more distant Cla -elements ( Figure 4A -B). For all clusters a highly variable mean ρ/bp x 10 -3 could be identified compared to the distance to the nearest Cla -element supported by bootstrapping. As such, there is no consistent global pattern between recombination and the Cla -element, neither on the genome-wide nor on the level of particular chromosomes. Discussion This study presents a novel genome assembly of chromosome-resolution for the emerging model organism C. riparius , a non-biting midge species also called harlequin fly. We test the impact of genome quality on the increase in resolution of demographic inferences by comparing estimates to a previous study. To reveal the genomic patterns that underly estimates of population history, we uncover the genome-wide landscape of recombination rates also looking at the potential role of a particular TE in these patterns. Over and above these novel genomic insights into the genomic landscape of C. riparius , these genomic resources will be more generally valuable for comparative studies on insect genomics ( Blackmon et al. 2017 ), experimental population genomics ( Foucault et al. 2019 ) and chromosome evolution ( Shaikhutdinov & Gusev 2022 ). More ancient population history estimates could be inferred for C. riparius In a previous study, Waldvogel et al. (2018) used a fragmented genome assembly for an initial inference of population history in C. riparius, applying multiple sequential Markovian coalescence (MSMC2). Many short scaffolds and the lack of information about their placement hindered the inference of recombination sites and thus only 17.34 % (30 scaffolds ≥ 100 kb) of the assembly were suitable for the analysis. With the new genome assembly, we were able to input 99.28 % of the genome to the analysis-only excluding ten unplaced scaffolds. The new assembly has a BUSCO (diptera_odb10) completeness of 99.28 % ( Table 1 ) demonstrating the advantage of long-read sequencing coupled with Hi-C scaffolding ( Guiglielmoni et al. 2022 ). Our results indicate the importance of high-quality genomes as the time range of demographic events was shifted significantly in the new inference ( Figure 2A-B ). Waldvogel et al. (2018) estimated an informative time horizon between 150,000 and 351,000 generations in the past, while in our study this range was 6,100 to 7,400,000 generations (Supplementary Figure S3). This increase in resolution on the demographic history of C. riparius in European populations can at least partially be explained by the high accuracy of PacBio long-reads leading to more accurate assemblies with better coverage and contiguity in low complexity and repetitive regions ( Pollard et al. 2018 ). The chromosome-resolution of the assembly also allowed the improvement of MSMC2 preconditions, i.e. restricting the analysis to scaffolds with a minimum length of 500 kb ( Schiffels & Wang 2020 ). The inference of the recent past is known to reach better resolution when more sequences are investigated ( Schiffels & Wang 2020 ). Under the assumption that young haplotype blocks should have larger sizes, the improved genome quality additionally contributes to the resolution of the more recent population history ( Stumpf & McVean 2003 ).The new genome assembly provides a contiguous overview of repetitive regions and enables access to them, which, in the past has been proven to be difficult with short-read sequencing as repetitive elements may not be mapped uniquely and as a result sequencing reads would stay ambiguous or collapsed with high coverage but poor mapping quality ( Pollard et al. 2018 ). Furthermore, when inferring population history, the accuracy of (short) sequences was demonstrated to be important ( Sellinger et al. 2021 ). The likelihood of unmapped reads is higher in assemblies based on short-read sequencing which means that if a fragmented genome is not of high resolution in problematic regions, estimates of population history will be biased ( Sellinger et al. 2021 ). Technical errors, like spurious SNP calling or incorrect detection of TEs have been shown to decrease the accuracy of population history estimates ( Sellinger et al. 2021 ). These errors are more likely, and more difficult to control for, in fragmented genomes with low resolution of low complexity regions. In summary, the more complete and chromosome-level assembly used here likely resulted in a more accurate estimation of population recombination (ρ) and a more robust inference of population history spanning a wider period and reaching far deeper into the past. Our study also benefitted from the availability of a species-specific mutation rate estimate ( Waldvogel & Pfenninger 2021 ) that additionally contributed to the increased resolution of population history in C. riparius . Biogeography supports population demography Accurate population demography models allow us to interpret and correlate a population’s history with the biogeographic history of its habitat. The temperature developments of the CHELSA traCE21k time-series dataset, covering the LGM up to 1,000 years before present ( Karger et al. 2021 ), were compared to the geographical coordinates of the sampling sites of the five C. riparius populations ( Figure 2C-D ). Correlation of these two very different data types, allowed us to investigate whether paleoclimate models could have the potential to support sequence-based demographic estimations. For our MSMC2 analysis we adapted the time segment pattern, approximated the time to the most recent common ancestor (tMRCA) of one population and trimmed outer values to account for overestimations of the most recent and most ancient time interval, resulting from false positive or negative signatures of recombination ( Schiffels & Wang 2020 ). Our results suggest the origin of one ancestral population for the five investigated populations ( Figure 2A ) as has been proposed ( Waldvogel et al. 2018 ). Whilst the split of the ancestral population has been proposed to have happened around 10,000 to 1,000 generations ago, our new estimates re-define this time frame. The admixture between the population shows a reduction between 500,000 to 10,000 generations ago and the rCCR reached a value of 0.5 at approximately 100,000 generations in the past. When multiplying these coalescence estimates with the population-specific generation time available for this multivoltine insect species (based on Oppold et al. 2016 ) the estimates were converted into years. This conversion defines the period of divergence of the populations between late Pleistocene and early Holocene ( Stroeven et al. 2016 ) ( Figure 2 ). Starting from the Last Glacial Maximum (LGM,) between 22,000 and 17,000 years ago, the ice margins in Europe started to recede which led to an almost ice-free central Europe 16,000 years ago ( Douda et al. 2014 ; Ehlers 1990 ; Stroeven et al. 2016 ). Two major climate events can be found in the temperature data ( Figure 2C )-the Heinrich event (H1) around 16,800 years ago ( Bond et al. 1992 ; Heinrich 1988 ; Hemming 2004 ) and the Younger Dryas around 12,000 years ago ( Carlson 2010 ; Keigwin & Lehman 1994 ). Considering the biogeographic history of central Europe, it seems plausible that these climatic changes have contributed to the decreasing effective population size in the ancient population of C. riparius . The increase in temperature in the late Pleistocene might have led to a first dispersal of the midges as more habitats became available ( Oppold et al. 2016 ). Both, the potential expansion across an increased habitat space, as well as the two cooling events are likely to have contributed to the decrease of effective population size of the ancient population. The decrease in effective population size and potential dispersal (see also in Waldvogel et al. 2018 ) might have also led to a reduced admixture, finally leading to a split of populations. As such, climate data could be used as an observational measure to support the demographic history estimation of C. riparius in Europe. A higher resolution of the recombination landscape and genome-wide patterns of repetitive elements is an advantage of high-quality assemblies Advanced genome assemblies offer improved coverage of repetitive regions, unveiling a novel perspective on genome dynamics. Based on these advances we were able to estimate population-specific recombination landscapes ( Figure 3 ). For each chromosome, five different landscapes could be identified and for each population the mean recombination rate was based on the recombination landscape of four individuals. Certain regions of low recombination were shown to match between the populations – this could indicate lower recombination in the range of centromeres as generally expected ( Choo 1998 ). The cause for the heterogenous pattern of recombination (especially the population Lorraine (NMF), Rhône-Alpes (MF) and Piemont (SI)) could be genomic markers, which led us to the initial study on the effect of the transposable Cla -element on the recombination landscape (Kent et al. 2017). As potential driver of population divergence, the Cla -element was of special interest ( Oppold et al. 2017 ). Genomic markers like microsatellites, TEs, telomeres, centromeres or heterochromatin-rich regions are known influencers of the dynamics within a genome (Kent et al. 2017; Sellinger et al. 2023 ; Oppold et al. 2017 ; Bascón-Cardozo et al. 2024 ), as they strongly influence the genomic landscape of an organism. They also should have a direct effect on the calculations done by models like the ones used to infer population history estimates. A recently published study ( Sellinger et al. 2023 ) suggests an improvement in the reliability of population history estimates through incorporation of additional markers beyond SNP data, as demonstrated by more accurate estimates through the addition of Single Methylated Polymorphisms (SMPs) as markers. The authors propose that the addition of multiple genomic or epigenomic markers in population genomic models might aid future studies when sample number is limited, i.e. in most non-model species. Due to the complex dynamics of genomes, it is reasonable to assume that some genomic markers might be more fitted for one taxon than another. If looking for a genomic marker which could aid in explaining genome evolutionary processes in C. riparius , a promising candidate would be the transposable Cla -element. This element has been suggested to play a role in the speciation within the genus as it is only found in the centromeric regions of C. piger but spreads across chromosomal arms in C. riparius ( Hankeln & Schmidt 1987 ). It has been shown that it is a driver for population divergence in C. riparius and only 25 % of the Cla -insertions have been found to be shared among all populations investigated, showing that there are many population-specific insertions ( Oppold et al. 2017 ). These population specific insertions also become apparent in the analysis with MELT reported here, where the population of Andalusia (SS) is showing the highest number of Cla -insertions. As the proportion of shared Cla -elements is limited, an ongoing or recent activity in this TE is probable ( Oppold et al. 2017 ). Overall, the incorporation of regions that were challenging to sequence in the past have the potential to enhance population genomic studies. Such regions may evolve at a different evolutionary speed. This underscores the necessity for highly resolved genome assemblies when studying the population dynamics of non-model organisms. Recombination rate ρ and its correlation to the next Cla -element The Cla -element has an influence on chromosomal breaks in regions with heterochromatin ( Bovero et al. 2002 ), as such, it seems likely that it has a general effect on recombination variation ( Oppold et al. 2017 ). Ectopic recombination can serve as a selective mechanism against TEs but proves to be a fitness disadvantage if it is occurring in an organism (Kent et al. 2017). Ectopic recombination is generally associated with genomic instability compared to meiotic recombination as a highly regulated process ( Lam & Keeney 2015 ; Pâques & Haber 1999 ; Hastings 2010 ). Recombination has a huge influence on the genetic diversity of a species and the length of haplotype blocks divided through recombination events gives insights on the past effective population size. Thus, investigating recombination is crucial to resolve Lewontin’s Paradox ( Stumpf & McVean 2003 ; Charlesworth & Jensen 2022 ; Buffalo 2021 ). In addition, it has been demonstrated that TEs might modulate the local recombination landscape; some TEs seem to co-localize with recombination hotspots in humans ( McVean 2010 ) and other TEs seem to accumulate in low recombining regions (Kent et al. 2017). Generally, TE density is suggested to correlate negatively with recombination, because selection against ectopic recombination and gene disruption can lead to accumulation of TEs in low recombining regions (Kent et al. 2017). Regulations of TEs might also influence local recombination rates (Kent et al. 2017). As such, it is difficult to disentangle cause and effect: are TEs actively shaping recombination variation or is it the reversed effect caused by the silencing of these elements (Kent et al. 2017)? To investigate this in C. riparius , we compared the recombination landscape with the Cla -element pattern along the four different chromosomes ( Figure 3 and 4 ). Most of the populations displayed a positive correlation, however there was no consistent pattern. A positive correlation would indicate a higher recombination rate if the next Cla -element is more distant. However, there were different levels of correlation between chromosomes of the different populations. Accordingly, there is no global pattern that connects the recombination landscape with the genome-wide distribution for Cla -elements in the genome of C. riparius even though the element has been described to drive population divergence ( Oppold et al. 2017 ). However, it cannot be ruled out that the high number of Cla -insertions in the genome are convoluting the global analysis. The high number might be of varying age, and it is possible that older insertions already led to suppressed recombination and newer insertions might be too recent to find a co-evolving effect (Kent et al. 2017). If the transposition of the Cla -element is still active ( Oppold et al. 2017 ) these signals might be covered. It could be also possible that it needs a certain number of insertions in close proximity, to detect reduced recombination to avoid ectopic recombination (Kent et al. 2017). The so-called insertion bias describes how TEs might be more commonly found in heterochromatin regions which indicates that TEs are expected to occur more frequently in low-recombing regions (Kent et al. 2017). However, there are also indicators for differential and purifying selection against TEs to select against ectopic recombination or gene disruption (Kent et al. 2017). There is not only a selective force due to direct fitness effects, but hosts appear to have evolved TE silencing mechanisms to decrease negative fitness effects (Kent et al. 2017). Natural selection and genetic drift could drive TEs in low recombining regions, but TE silencing could actively suppress recombination to repress transposition and ectopic recombination to avoid harmful fitness effects (Kent et al. 2017). As such, TE silencing could be an important modulator of local and large-scale recombination rates and might also explain differences in recombination landscape of con-specific populations (Kent et al. 2017; Mérel et al. 2020 ). Here, we could not confirm a higher occurrence of the Cla -element in low recombining regions. This might be explained by the theory of an ongoing transposition suspected for Cla -elements ( Oppold et al. 2017 ). Additionally, there could be little to no regulation of this TE based on characteristics ( Oppold et al. 2017 ). If there are regulatory processes controlling transposition of TEs in C. riparius , they might influence local recombination rates. It can be noted that a differential pattern of recombination can be observed in those chromosomes with many Cla -elements ( Figure 3 ). It might be useful to assess the age of TEs and proportion of TEs that are silenced and compare them to the local recombination rates as suggested by Kent et al. (2017)-with this a better understanding of local recombination variation could be obtained. Possible further investigations through comparative genomics might offer insights in resolving this question. Investigating this mechanism could create new perspectives in the genome evolution of C. riparius , especially considering the involvement of the Cla -element in the speciation process. Conclusions Based on a novel genome assembly at a chromosome scale, population history estimates of improved accuracy were obtained. We could show a shift in the coalescence estimates using MSMC2 compared to the previous study and could match these new results to past climate events. As recombination variation can highly influence population history estimates, the recombination landscape of C. riparius should be investigated. Population-specific recombination landscapes were successfully estimated giving a first insight into recombination variation of the five different European populations. With a genome assembly of increased resolution more regions of the genome including repetitive elements, like the Cla -element, could be investigated. The potential impact of Cla insertions on the recombination landscape was explored but no global pattern could be seen. We could show that the recombination landscape of C. riparius seems to be more stable regarding the impact of repetitive elements on genome integrity than previously assumed. Materials and Methods Data The reference genome of Chironomus riparius provided by Rothamsted Research (West Common, Harpenden, United Kingdom) was assembled from a single female individual (German origin, procured Innovative Environmental services (IES) Ltd, Switzerland). DNA was extracted using MagAttract and 450ng was sequenced with PacBio HiFi using one SMRT cell and Hi-C sequencing using Illumina. The genome contains four chromosomes and ten scaffolds with a size of 192 Mb (NCBI accession number: PRJEB47883). Five different C. riparius populations, which were directly taken from their natural habitat, were studied. These are the same populations as were investigated in a previous study ( Waldvogel et al. 2018 ). The origin of the different populations was from Rhône-Alpes (MF) and Lorraine (NMF) in France, Hesse in Germany (MG), Piedmont in Italy (SI) and Andalusia in Spain (SS). Twenty individual resequencing datasets were downloaded from the European Nucleotide Archive (ENA: 150-bp paired-end, Illumina sequencing data; Project number PRJEB24868). For each population four individuals were available. Of the datasets, the forward and reverse reads were already trimmed. Genome assembly and annotation Hifiasm ( Cheng et al. 2022 , 2021 ) was used to assemble the PacBio HiFi data, then with Juicer ( Durand et al. 2016 ) and 3d-dna ( Dudchenko et al. 2017 ) the Hi-C data was used for chromosome level assembly. Haplotigs were removed using purge_haplotigs ( Roach et al. 2018 ). Unmapped reads were mapped back to the original assembly to check for missing sequences and incorporated into the final assembly. Manual curation was done to bring the genome together and check for miss-assemblies. To assess the general quality of the genome assembly the software Blobtoolskits (v2.6.5, Challis et al. 2020 ) was utilized and an analysis for BUSCO completeness was performed (v5.3.2, Manni, Berkeley, Seppey & Zdobnov 2021 ; Manni, Berkeley, Seppey, Simão, et al. 2021 ; Simão et al. 2015 ). A public RNA-seq transcriptome was assembled (BUSCO Insecta: C:94.7%[S:53.7%,D:41.0%],F:0.4%,M:4.9%) and used in the Maker2 ( Holt & Yandell 2011 ) annotation pipeline with trained Augustus ( Stanke et al. 2008 ) and Genemark ( Borodovsky & McIninch 1993 ) gene predictors. Data used included: PRJEB15223 (Larvae), PRJNA166085 (egg ropes, all four larval stages, pupae and male and female adults, larvae exposed to different concentrations of several model toxicants) PRJNA229141 (anterior and posterior early embryo), PRJNA675286 (larvae-transition metal oxide exposure). PASA ( Haas et al. 2003 ) was used to update the gene models to add UTR, correct existing models and add isoforms. Non-coding RNA was annotated using Infernal (v1.1.4, Nawrocki & Eddy 2013 ). A Pfam genomic track was created by converting to six reading frames and applying hmmer ( Finn et al. 2015 ) to identify loci of interest. Using this information, UDP, P450, ABC and IRAC gene models were found and curated using mapped RNA-seq and a Maker gene annotation. Two Endosymbionts were assembled which included an unknown Enterobacter sp. (1,661,850 bp) and Wolbachia sp. (559,667 bp). Data pre-processing Read quality was checked with FastQC (v0.11.9, Andrews 2010 ) and MultiQC (v1.12, Ewels et al. 2016 ). All pre-processing steps were performed according to Waldvogel et al. (2018) . The trimmed reads were mapped separately to our reference genome using the tool bwa mem (-M-R’@RG\tID:$Population\tSM:$Individual\tPL:ILLUMINA’, v0.7.17, Li 2013 ). Low quality alignments were removed using samtools (-q 30-f 0×0002-F 0×0004-F0×0008, v1.13, Li et al. 2009 ) and to remove duplicates PicardTools (VALIDATIONSTRINGENCY SILENT-REMOVEDUPLICATES true, v2.26.10, Broad Institute 2018 ) was utilised. Mapping statistics were obtained using Qualimap (v2.2.2d, Okonechnikov et al. 2016 ). Further details on all analysis steps can be found on GitHub: https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024 . Variant calling Variant calling and phasing were performed as suggested in the MSMC2 workflow ( Schiffels 2016 ) for the four chromosomes (99.28 % of the assembly). The reference genome was split by chromosome and mappability masks were created using SNPable ( Li 2009 ), indicating mappable regions of the genome assembly. Following the standard workflow, variant calling was performed on the filtered bam-files of the samples using bcftools (v1.13, Li 2011 ) and the script bamCaller.py supplied by msmc-tools ( Schiffels 2021 ) disregarding indels. This workflow generated VCF-and mask-files for each individual and chromosome which were necessary for generating the input files for MSMC2. Phasing was performed per chromosome utilising SHAPEIT4 (v4.2, Delaneau et al. 2019 ). Since no reference panel for C. riparius was available, we merged all VCF-files (bcftools merge) for phasing and separated them once again. To account for any missing information that is still contained in the unphased data, the phased and unphased VCF-files were merged while keeping the unphased data and replacing it with phased data where it was available. All multiallelic SNPs were discarded and only biallelic sites were kept. Using the obtained masking files and variant calls, multihetsep-files were generated using the script generatemultihetsep.py of the msmc-tools. Population demography The generated multihetsep-files were used for MSMC2 (v2.1.3, Schiffels & Wang (2020) ). Two populations were paired, resulting in a total of sixteen haplotypes (four diploid individuals per population) per dataset and a total of ten different population pairs that were later analysed in a cross-population analysis. The procedure of the cross-population analysis was to allow the first two MSMC2-runs that estimated the coalescence rate function within the population. Afterwards, an analysis across the populations was performed, selecting the population pairs. For the analysis the used time segment pattern was 1*3+1*2+22*1+1*2+1*3 and ambiguous sites were skipped. In the end the results were combined using the combineCrossCoal.py script from the msmc-tools. Overall, the obtained output per population included time and population size estimates, as well as the relative cross-coalescence rate (rCCR) which is a measure indicating the divergence of populations. Time and population size estimates were averaged per population and then scaled to real time and effective population size. Time estimates were converted into generations by dividing it through the mutation rate of 4.27 x 10 -9 ( Waldvogel & Pfenninger 2021 ). By multiplying it with the generation time ( Waldvogel et al. 2018 ; Oppold et al. 2016 ) these converted coalescence times were converted into years. The effective population size was obtained by inverting the coalescence rate and dividing it by two-times the mutation rate. The first five entries, as well as the last entry were excluded to account for uncertainties in the analysis. Additionally, the mean haplotype length (MHL) was determined through the mean genome-wide heterozygosity and with regards to the switch error rate (SER). The SER of 2 % in Drosophila melanogaster ( Bukowicki et al. 2016 ) was used, the same as in the previous study of Waldvogel et al. (2018) . The mean heterozygosity was determined from the ratio of diallelic SNPs per number of records. These values were needed to approximate the time to the most recent common ancestor (tMRCA) of one population with the following formula: tMRCA= 1/(2∗r∗MHL). The population recombination rate (ρ) (based on Schmidt et al. 2020 ) was approximated to the recombination rate in units of meiosis per generation (r) using this formula from Peñalba and Wolf (2020) : r=ρ/(2∗c∗Ne). In this context, c and Ne represented the organism’s diploidy and effective population size respectively. The effective population size ( Oppold & Pfenninger 2017 ) was used for the calculation. To get an approximation of r in cM/Mb the gene map length of D. melanogaster of 287.3 cM ( Comeron et al. 2012 ) was used as it should be comparable to C. riparius where the gene map length is not yet available. The mean value of r of 1.36 cM/Mb was then used to calculate tMRCA. This value was compared to the one using the recombination rate of D. melanogaster of 2.1 cM/Mb ( Mackay et al. 2012 ) which is the same value as used in Waldvogel et al. (2018) . Paleoclimate data Results of the MSMC2 model of C. riparius were further compared to paleoclimate temperature data. Therefore, twenty-two bio1-datasets of the CHELSA-TraCE21k climate time-series were downloaded from CHELSA ( Karger et al. 2021 ). Thus, the CHELSA-TraCE21k climate data provides information for the last 22,000 years before present (years BP) which referred to the Last Glacial Maximum (LGM). As such, contained the bio1-datsets annual mean temperatures and the here used twenty-two datasets (Supplementary Table S2) included timepoints from 1,000 years BP up to 22,000 years BP and were retrieved in steps of one thousand years (i.e., millennial time series). Recombination landscape To reconstruct the recombination landscape of C. riparius we used the tool iSMC ( Barroso et al. 2019 ) and the provided scripts of Robinson et al. (2021) . This tool uses SMC models to infer population history and spatial variation in recombination rates. The artificial phased VCF-files were used as input files. The chromosomes were analysed separately per individual. For each analysis an iSMC option file needed to be provided where we used the standard recommendations from the iSMC documentation (optimize = true, decode = true, decodebreakpointsparallel = false, num-berrhocategories = 5, numberintervals = 40, functiontolerance = 1e-4, fragmentsize = 3000000). To obtain recombination rates (ρ) and information on tMRCA we used the iSMC mapper with a bin size of 10 kb and 1 Mb. Transposable elements Sensitive soft masking of repeats on the genome was performed with RepeatMasker (v4.1.1, Smit et al. 2015 ) using a custom TE-library by Vladimir Kapitanov which was modified by adding TE-entries of Oppold et al. (2017) . A cutoff score of masking repeats of 250 bp and the engine rmblast (-s-xsmall-cutoff 250-u-gff-pa 10-lib $LIB-dir $DIR-enginermblast $GENOME) was chosen for the RepeatMasker analysis. The output file was converted to BED format using RM2Bed.py script supplied by RepeatMasker. Additionally, an annotation of the Cla-element population-wise was conducted using MELT (v2.2.2, Gardner et al. 2017 ). The necessary mei.zip file for the Cla-element was created following the instructions of the MELT documentation. To find the insertions we added the MQ tag using samblaster to our sam-files and used the unfiltered but sorted bam-files as input for MELT-SPLIT. Data for each of the four different analyses runs were obtained for each population and for all populations together. To visualise the insertions of the Cla -element in the genome of each population compared to their inferred recombination rates, R (v4.2.1, R Core Team 2020 ) and R-Studio (v2022.02.0+433, RStudio Team 2020 ) was used together with several R packages, like tidyverse (v2.0.0, Wickham et al. 2023 ), patchwork (v1.1.3, Pedersen 2024 ) or cowplot (v1.1.1, Wilke 2020 ). A detailed list of the R packages can be found in the Supplementary Table S1. To test whether the occurrence of Cla insertions is correlated to recombination events, we used the closest function of bedtools (v2.31.0, Quinlan & Hall 2010 ), according to Robinson et al. (2021) . R-Studio was used to create plots showing the recombination rate in relation to the nearest Cla-element. These decay plots were created using the distance of the next Cla-element to a recombination event. Cla -clusters with sizes smaller than 500 bp were investigated separately from Cla -Clusters larger or equal to 500 bp. The obtained measures were tested with the Pearson correlation coefficient using the R package stats and results were then visualised in a heatmap. Pearson correlation coefficients were calculated for each chromosome of each population (mean values were calculated from the four individuals per population) to investigate whether the distance of the Cla -element is correlated to the amplitude of ρ. Smaller and larger cluster were again analysed independent from each other. For the creation of loops in R and similar simplifications of the scripts ChatGPT was utilised ( OpenAI 2024 ). Author Contributions L.C.P. and A.M.W. perceived the study. L.C.P. performed the data analysis and drafted the manuscript. A.M.W. contributed to writing and supervised the work. L.M.F. and R.K. generated and provided the genome assembly. All authors approved the final version of the manuscript. Data Availability The genome assembly can be downloaded at ENA (accession PRJEB47883). The Illumina sequences of the five populations have been published under Waldvogel et al. (2018) and trimmed reads can be accessed at ENA (accession PRJEB24868). Scripts are available at the GitHub repository https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024 . Acknowledgments We thank Dr. Vladimir Kapitanov from the LOEWE Center for Translational Biodiversity Genomics, Frankfurt am Main, Germany for support with the development and nomenclature of the TE library that was used for the TE annotation of the genome. We thank Dr. Philipp Schiffer and Laura I. Villegas for their thoughtful comments on this manuscript. AMW acknowledges the funding of her junior professorship in the “Bund-Länder Programm” of the German Federal Ministry of Education and Research (BMBF). This work has additionally been funded as part of subproject B08 in the Collaborative Research Cluster 1211 (DFG grant number 268236062). We furthermore acknowledge the Regional Computing Center of the University of Cologne (RRZK) for providing computing time on the DFG-funded [Funding number INST 216/512/1FUGG] High Performance Computing (HPC) system CHEOPS as well as support. References 1. ↵ Andrews S. 2010 . FastQC: A Quality Control Tool for High Throughput Sequence Data. Babraham Bioinformatics . http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ . 2. ↵ Barroso GV. , Puzović N , Dutheil JY . 2019 . Inference of recombination maps from a single pair of genomes and its application to ancient samples . PLoS Genet . 15 : e1008449 . doi: 10.1371/JOURNAL.PGEN.1008449 . OpenUrl CrossRef 3. ↵ Bascón-Cardozo K et al. 2024 . Fine-Scale Map Reveals Highly Variable Recombination Rates Associated with Genomic Features in the Eurasian Blackcap . Genome Biol Evol . 16 . doi: 10.1093/GBE/EVAD233 . OpenUrl CrossRef 4. ↵ Blackmon H , Ross L , Bachtrog D . 2017 . Sex Determination, Sex Chromosomes, and Karyotype Evolution in Insects . Journal of Heredity . 108 : 78 – 93 . doi: 10.1093/JHERED/ESW047 . OpenUrl CrossRef PubMed 5. ↵ Blaxter M et al. 2022 . Why sequence all eukaryotes? Proceedings of the National Academy of Sciences . 119 : e2115636118 . doi: 10.1073/pnas.2115636118 . OpenUrl Abstract / FREE Full Text 6. ↵ Bond G et al. 1992 . Evidence for massive discharges of icebergs into the North Atlantic ocean during the last glacial period . Nature 1992 360 : 6401 . 360:245–249. doi: 10.1038/360245a0 . OpenUrl CrossRef 7. ↵ Borodovsky M , McIninch J . 1993 . GENMARK: Parallel gene recognition for both DNA strands . Comput Chem . 17 : 123 – 133 . doi: 10.1016/0097-8485(93)85004-V . OpenUrl CrossRef 8. ↵ Bourgeois YXC , Warren BH . 2021 . An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes . Mol Ecol . 30 : 6036 – 6071 . doi: 10.1111/MEC.15989 . OpenUrl CrossRef 9. ↵ Bovero S , Hankeln T , Michailova P , Schmidt E , Sella G . 2002 . Nonrandom chromosomal distribution of spontaneous breakpoints and satellite DNA clusters in two geographically distant populations of Chironomus riparius (Diptera: Chironomidae) . Genetica . 115 : 273 – 281 . doi: 10.1023/A:1020697228525 . OpenUrl CrossRef PubMed 10. ↵ Broad Institute . 2018 . Picard Tools . http://broadinstitute.github.io/picard/ (Accessed June 1, 2022). 11. ↵ Buffalo V. 2021 . Quantifying the relationship between genetic diversity and population size suggests natural selection cannot explain Lewontin’s paradox . Elife . 10 . doi: 10.7554/ELIFE.67509 . OpenUrl CrossRef 12. ↵ Bukowicki M , Franssen SU , Schlötterer C . 2016 . High rates of phasing errors in highly polymorphic species with low levels of linkage disequilibrium . Mol Ecol Resour . 16 : 874 – 882 . doi: 10.1111/1755-0998.12516 . OpenUrl CrossRef 13. ↵ Carlson AE . 2010 . What Caused the Younger Dryas Cold Event? Geology . 38 : 383 – 384 . doi: 10.1130/FOCUS042010.1 . OpenUrl FREE Full Text 14. ↵ Challis R , Richards E , Rajan J , Cochrane G , Blaxter M . 2020 . BlobToolKit – Interactive Quality Assessment of Genome Assemblies . G3 Genes|Genomes|Genetics . 10 : 1361 – 1374 . doi: 10.1534/G3.119.400908 . OpenUrl CrossRef 15. ↵ Charlesworth B , Jensen JD . 2022 . How Can We Resolve Lewontin’s Paradox? Genome Biol Evol . 14 . doi: 10.1093/GBE/EVAC096 . OpenUrl CrossRef 16. ↵ Cheng H et al. 2022 . Haplotype-resolved assembly of diploid genomes without parental data . Nature Biotechnology 2022 40 : 9 . 40: 1332 – 1335 . doi: 10.1038/s41587-022-01261-x . OpenUrl CrossRef 17. ↵ Cheng H , Concepcion GT , Feng X , Zhang H , Li H . 2021 . Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm . Nature Methods 2021 18 : 2 . 18:170–175. doi: 10.1038/s41592-020-01056-5 . OpenUrl CrossRef 18. ↵ Choo KHA . 1998 . Why Is the Centromere So Cold? Genome Res . 8 : 81 – 82 . doi: 10.1101/GR.8.2.81 . OpenUrl FREE Full Text 19. ↵ Comeron JM , Ratnappan R , Bailin S . 2012 . The Many Landscapes of Recombination in Drosophila melanogaster . PLoS Genet . 8 : e1002905 . doi: 10.1371/JOURNAL.PGEN.1002905 . OpenUrl CrossRef PubMed 20. ↵ Delaneau O , Zagury JF , Robinson MR , Marchini JL , Dermitzakis ET . 2019 . Accurate, scalable and integrative haplotype estimation . Nature Communications 2019 10 : 1 . 10:1–10. doi: 10.1038/s41467-019-13225-y . OpenUrl CrossRef PubMed 21. ↵ Douda J et al. 2014 . Migration Patterns of Subgenus Alnus in Europe since the Last Glacial Maximum: A Systematic Review . PLoS One . 9 : e88709 . doi: 10.1371/JOURNAL.PONE.0088709 . OpenUrl CrossRef PubMed 22. ↵ Dudchenko O et al. 2017 . De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds . Science (1979). 356 : 92 – 95 . doi: 10.1126/SCIENCE.AAL3327 . OpenUrl Abstract / FREE Full Text 23. ↵ Durand NC et al. 2016 . Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments . Cell Syst . 3 : 95 – 98 . doi: 10.1016/j.cels.2016.07.002 . OpenUrl CrossRef PubMed 24. ↵ Ehlers J . 1990 . Reconstructing the dynamics of the North-west European Pleistocene ice sheets . Quat Sci Rev . 9 : 71 – 83 . doi: 10.1016/0277-3791(90)90005-U . OpenUrl CrossRef GeoRef Web of Science 25. ↵ Ewels P , Magnusson M , Lundin S , Käller M . 2016 . MultiQC: Summarize analysis results for multiple tools and samples in a single report . Bioinformatics . 32 : 3047 – 3048 . doi: 10.1093/bioinformatics/btw354 . OpenUrl CrossRef PubMed 26. ↵ Finn RD et al. 2015 . HMMER web server: 2015 Update . Nucleic Acids Res . 43 : W30 – W38 . doi: 10.1093/NAR/GKV397 . OpenUrl CrossRef PubMed 27. ↵ Foucault Q , Wieser A , Waldvogel AM , Pfenninger M . 2019 . Establishing laboratory cultures and performing ecological and evolutionary experiments with the emerging model species Chironomus riparius . Journal of Applied Entomology . 143 : 584 – 592 . doi: 10.1111/JEN.12606 . OpenUrl CrossRef 28. ↵ Gardner EJ et al. 2017 . The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology . Genome Res . 27 : 1916 – 1929 . doi: 10.1101/GR.218032.116 . OpenUrl Abstract / FREE Full Text 29. ↵ Guiglielmoni N , Rivera-Vicéns R , Koszul R , Flot J-F . 2022 . A deep dive into genome assemblies of non-vertebrate animals . Peer Community Journal . 2 . doi: 10.24072/PCJOURNAL.128 . OpenUrl CrossRef 30. ↵ Guiglielmoni N , Villegas LI , Kirangwa J , Schiffer PH. 2023 . Revisiting genomes of non-model species with long reads yields new insights into their biology and evolution . bioRxiv . 2023.10.06.561169. doi: 10.1101/2023.10.06.561169 . OpenUrl Abstract / FREE Full Text 31. ↵ Haas BJ et al. 2003 . Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies . Nucleic Acids Res . 31 : 5654 – 5666 . doi: 10.1093/NAR/GKG770 . OpenUrl CrossRef PubMed Web of Science 32. ↵ Hägele K . 1984 . Different hybrid effects in reciprocal crosses between Chironomus thummi thummi and Ch. th. piger including spontaneous chromosome aberrations and sterility . Genetica . 63 : 105 – 111 . doi: 10.1007/BF00605894 . OpenUrl CrossRef 33. ↵ Hankeln T , Rohwedder A , Weich B , Schmidt ER . 2011 . Transposition of minisatellite-like DNA in Chironomus midges . Genome . 37 : 542 – 549 . doi: 10.1139/G94-077 . OpenUrl CrossRef 34. ↵ Hankeln T , Schmidt ER . 1987 . Cotransposition of a highly repetitive DNA element with flanking sequences in the genome of the midge Chironomus thummi . J Mol Evol . 26 : 311 – 319 . doi: 10.1007/BF02101149 . OpenUrl CrossRef PubMed 35. ↵ Hastings PJ . 2010 . Mechanisms of Ectopic Gene Conversion . Genes 2010, Vol. 1 , Pages 427 - 439 . 1:427–439. doi: 10.3390/GENES1030427 . OpenUrl CrossRef 36. ↵ Heinrich H . 1988 . Origin and consequences of cyclic ice rafting in the Northeast Atlantic Ocean during the past 130,000 years . Quat. Res . 29 : 142 – 152 . doi: 10.1016/0033-5894(88)90057-9 . OpenUrl CrossRef GeoRef Web of Science 37. ↵ Hemming SR . 2004 . Heinrich events: Massive late Pleistocene detritus layers of the North Atlantic and their global climate imprint . Reviews of Geophysics . 42 : 1005 . doi: 10.1029/2003RG000128 . OpenUrl CrossRef 38. ↵ Holt C , Yandell M . 2011 . MAKER2: An annotation pipeline and genome-database management tool for second-generation genome projects . BMC Bioinformatics . 12 : 1 – 14 . doi: 10.1186/1471-2105-12-491 . OpenUrl CrossRef PubMed 39. ↵ Israelewski N . 1983 . Structure and function of an AT-rich, interspersed repetitive sequence from Chironomus thummi: solenoidal DNA, 142 bp palindrome-frame and homologies with the sequence for site-specific recombination of bacterial transposons . Nucleic Acids Res. 11 : 6985 – 6996 . doi: 10.1093/NAR/11.20.6985 . OpenUrl CrossRef PubMed Web of Science 40. ↵ Karger DN , Nobis MP , Normand S , Graham CH , Zimmermann NE . 2021 . CHELSA-TraCE21k v1.0 . Downscaled transient temperature and precipitation data since the last glacial maximum. Clim. Past Discuss . 1 – 27 . doi: 10.5194/cp-2021-30 . OpenUrl CrossRef 41. ↵ Keigwin LD , Lehman SJ . 1994 . Deep circulation change linked to HEINRICH Event 1 and Younger Dryas in a middepth North Atlantic Core . Paleoceanography . 9 : 185 – 194 . doi: 10.1029/94PA00032 . OpenUrl CrossRef GeoRef Web of Science 42. ↵ Kelley JL et al. 2014 . Compact genome of the Antarctic midge is likely an adaptation to an extreme environment . Nature Communications 2014 5 : 1 . 5: 1 – 8 . doi: 10.1038/ncomms5611 . OpenUrl CrossRef 43. ↵ Kent TV. , Uzunović J , Wright SI . 2017 . Coevolution between transposable elements and recombination . Philosophical Transactions of the Royal Society B: Biological Sciences . 372 . doi: 10.1098/RSTB.2016.0458 . OpenUrl CrossRef 44. ↵ Kozeretska I , Serga S , Kovalenko P , Gorobchyshyn V , Convey P . 2022 . Belgica antarctica (Diptera: Chironomidae): A natural model organism for extreme environments . Insect Sci . 29 : 2 – 20 . doi: 10.1111/1744-7917.12925 . OpenUrl CrossRef 45. ↵ Lam I , Keeney S . 2015 . Mechanism and Regulation of Meiotic Recombination Initiation . Cold Spring Harb Perspect Biol . 7 : a016634 . doi: 10.1101/CSHPERSPECT.A016634 . OpenUrl Abstract / FREE Full Text 46. ↵ Lewis SH et al. 2018 . Pan-arthropod analysis reveals somatic piRNAs as an ancestral defence against transposable elements . Nature Ecology & Evolution 2017 2 : 1 . 2: 174 – 181 . doi: 10.1038/s41559-017-0403-4 . OpenUrl CrossRef PubMed 47. ↵ Lewontin RC . 1974 . The genetic basis of evolutionary change . >Columbia University Press New York . 48. ↵ Li H. 2011 . A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data . Bioinformatics . 27 : 2987 – 2993 . doi: 10.1093/BIOINFORMATICS/BTR509 . OpenUrl CrossRef PubMed Web of Science 49. ↵ Li H . 2013 . Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM . http://arxiv.org/abs/1303.3997 (Accessed February 6, 2021). 50. ↵ Li H. 2009 . SNPable Regions . http://lh3lh3.users.sourceforge.net/snpable.shtml (Accessed June 11, 2022). 51. ↵ Li H et al. 2009 . The Sequence Alignment/Map format and SAMtools . Bioinformatics . 25 : 2078 – 2079 . doi: 10.1093/bioinformatics/btp352 . OpenUrl CrossRef PubMed Web of Science 52. ↵ Li H , Durbin R . 2011 . Inference of human population history from individual whole-genome sequences . Nature 2011 475 : 7357 . 475: 493 – 496 . doi: 10.1038/nature10231 . OpenUrl CrossRef PubMed Web of Science 53. ↵ Liu Xuanzeng et al. 2022 . Transposable element expansion and low-level piRNA silencing in grasshoppers may cause genome gigantism . BMC Biology 2022 20 : 1 . 20:1–16. doi: 10.1186/S12915-022-01441-W . OpenUrl CrossRef 54. ↵ Luikart G , England PR , Tallmon D , Jordan S , Taberlet P . 2003 . The power and promise of population genomics: from genotyping to genome typing . Nature Reviews Genetics 2003 4 : 12 . 4:981–994. doi: 10.1038/nrg1226 . OpenUrl CrossRef 55. ↵ Mackay TFC et al. 2012 . The Drosophila melanogaster Genetic Reference Panel . Nature 2012 482 : 7384 . 482: 173 – 178 . doi: 10.1038/nature10811 . OpenUrl CrossRef PubMed Web of Science 56. ↵ Manni M , Berkeley MR , Seppey M , Simão FA , Zdobnov EM . 2021 . BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes . Mol Biol Evol . 38 : 4647 – 4654 . doi: 10.1093/MOLBEV/MSAB199 . OpenUrl CrossRef 57. ↵ Manni M , Berkeley MR , Seppey M , Zdobnov EM . 2021 . BUSCO: Assessing Genomic Data Quality and Beyond . Curr Protoc . 1 : e323 . doi: 10.1002/CPZ1.323 . OpenUrl CrossRef 58. ↵ Marjoram P , Wall JD . 2006 . Fast ‘coalescent’ simulation . BMC Genet . 7 : 1 – 9 . doi: 10.1186/1471-2156-7-16/TABLES/4 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Marsolier-Kergoat MC , Yeramian E . 2009 . GC Content and Recombination: Reassessing the Causal Effects for the Saccharomyces cerevisiae Genome . Genetics . 183 : 31 – 38 . doi: 10.1534/GENETICS.109.105049 . OpenUrl Abstract / FREE Full Text 60. ↵ Mather N , Traves SM , Ho SYW . 2020 . A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data . Ecol Evol . 10 : 579 – 589 . doi: 10.1002/ECE3.5888 . OpenUrl CrossRef 61. ↵ McVean G . 2010 . What drives recombination hotspots to repeat DNA in humans? Philosophical Transactions of the Royal Society B: Biological Sciences . 365 : 1213 – 1218 . doi: 10.1098/RSTB.2009.0299 . OpenUrl CrossRef PubMed 62. ↵ McVean GAT , Cardin NJ . 2005 . Approximating the coalescent with recombination . Philosophical Transactions of the Royal Society B: Biological Sciences . 360 : 1387 – 1393 . doi: 10.1098/RSTB.2005.1673 . OpenUrl CrossRef PubMed 63. ↵ Mérel V , Boulesteix M , Fablet M , Vieira C . 2020 . Transposable elements in Drosophila . Mobile DNA 2020 11 : 1 . 11: 1 – 20 . doi: 10.1186/S13100-020-00213-Z . OpenUrl CrossRef 64. ↵ Montgomery EA , Huang SM , Langley CH , Judd BH . 1991 . Chromosome rearrangement by ectopic recombination in Drosophila melanogaster : genome structure and evolution . Genetics . 129 : 1085 – 1098 . doi: 10.1093/GENETICS/129.4.1085 . OpenUrl Abstract / FREE Full Text 65. ↵ Nachman MW . 2002 . Variation in recombination rate across the genome: evidence and implications . Curr Opin Genet Dev . 12 : 657 – 663 . doi: 10.1016/S0959-437X(02)00358-1 . OpenUrl CrossRef PubMed Web of Science 66. ↵ Nawrocki EP , Eddy SR . 2013 . Infernal 1.1: 100-fold faster RNA homology searches . Bioinformatics . 29 : 2933 – 2935 . doi: 10.1093/BIOINFORMATICS/BTT509 . OpenUrl CrossRef PubMed Web of Science 67. ↵ Okonechnikov K , Conesa A , García-Alcalde F . 2016 . Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data . Bioinformatics . 32 : 292 – 294 . doi: 10.1093/bioinformatics/btv566 . OpenUrl CrossRef PubMed 68. ↵ OpenAI . 2024 . ChatGPT (Feb 28 version). 69. ↵ Oppold A-M et al. 2017 . Chironomus riparius (Diptera) genome sequencing reveals the impact of minisatellite transposable elements on population divergence . Mol Ecol . 26 : 3256 – 3275 . doi: 10.1111/mec.14111 . OpenUrl CrossRef 70. ↵ Oppold AM et al. 2016 . Support for the evolutionary speed hypothesis from intraspecific population genetic data in the non-biting midge Chironomus riparius . Proceedings of the Royal Society B: Biological Sciences . 283 . doi: 10.1098/RSPB.2015.2413 . OpenUrl CrossRef 71. ↵ Oppold AM , Pfenninger M . 2017 . Direct estimation of the spontaneous mutation rate by short-term mutation accumulation lines in Chironomus riparius . Evol Lett . 1 : 86 – 92 . doi: 10.1002/EVL3.8 . OpenUrl CrossRef 72. ↵ Pâques F , Haber JE . 1999 . Multiple Pathways of Recombination Induced by Double-Strand Breaks in Saccharomyces cerevisiae . Microbiology and Molecular Biology Reviews . 63 : 349 – 404 . doi: 10.1128/MMBR.63.2.349-404.1999 . OpenUrl Abstract / FREE Full Text 73. ↵ Pedersen TL. 2024 . patchwork: The Composer of Plots . 74. ↵ Peñalba JV. , Wolf JBW . 2020 . From molecules to populations: appreciating and estimating recombination rate variation . Nature Reviews Genetics 2020 21 : 8 . 21: 476 – 492 . doi: 10.1038/s41576-020-0240-1 . OpenUrl CrossRef 75. ↵ Pollard MO , Gurdasani D , Mentzer AJ , Porter T , Sandhu MS . 2018 . Long reads: their purpose and place . Hum Mol Genet . 27 : R234 – R241 . doi: 10.1093/HMG/DDY177 . OpenUrl CrossRef PubMed 76. ↵ Quinlan AR , Hall IM . 2010 . BEDTools: a flexible suite of utilities for comparing genomic features . Bioinformatics . 26 : 841 – 842 . doi: 10.1093/BIOINFORMATICS/BTQ033 . OpenUrl CrossRef PubMed Web of Science 77. ↵ R Core Team . 2020 . R: A language and environment for statistical computing . https://www.R-project.org/ . 78. ↵ Roach MJ , Schmidt SA , Borneman AR . 2018 . Purge Haplotigs: Allelic contig reassignment for third-gen diploid genome assemblies . BMC Bioinformatics . 19 : 1 – 10 . doi: 10.1186/S12859-018-2485-7 . OpenUrl CrossRef PubMed 79. ↵ Roberts RG . 2015 . Lewontin’s Paradox Resolved? In Larger Populations, Stronger Selection Erases More Diversity . PLoS Biol . 13 : e1002113 . doi: 10.1371/JOURNAL.PBIO.1002113 . OpenUrl CrossRef 80. ↵ Robinson JA et al. 2021 . Genome-wide diversity in the California condor tracks its prehistoric abundance and decline . Current Biology . 31 : 2939 – 2946 .e5. doi: 10.1016/J.CUB.2021.04.035 . OpenUrl CrossRef 81. ↵ RStudio Team . 2020 . RStudio: Integrated Development Environment for R . https://www.rstudio.com/ . 82. ↵ Schaefer J , Schmidt ER. 1981 . Different repetition frequencies of a 120 base-pair DNA-element and its arrangement in Chironomus thummi thummi and Chironomus thummi piger . Chromosoma . 84 : 61 – 66 . doi: 10.1007/BF00293363 . OpenUrl CrossRef PubMed 83. ↵ Schiffels S. 2016 . Short guide to MSMC . https://github.com/stschiff/msmc/blob/master/guide.md (Accessed June 11, 2022). 84. ↵ Schiffels S. 2021 . Tools and Utilities for msmc and msmc2 . https://github.com/stschiff/msmc-tools (Accessed June 11, 2022). 85. ↵ Schiffels S , Durbin R . 2014 . Inferring human population size and separation history from multiple genome sequences . Nature Genetics 2014 46 : 8 . 46: 919 – 925 . doi: 10.1038/ng.3015 . OpenUrl CrossRef PubMed 86. ↵ Schiffels S , Wang K . 2020 . MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent . Methods in Molecular Biology . 2090 : 147 – 166 . doi: 10.1007/978-1-0716-0199-0_7 . OpenUrl CrossRef PubMed 87. ↵ Schmidt ER . 1984 . Clustered and interspersed repetitive DNA sequence family of Chironomus : The nucleotide sequence of the Cla -elements and of various flanking sequences . J Mol Biol . 178 : 1 – 15 . doi: 10.1016/0022-2836(84)90227-4 . OpenUrl CrossRef PubMed 88. ↵ Schmidt ER . 1981 . The development of a 120 basepair repetitive DNA sequence in Chironomus thummi is correlated to the duplication of defined chromosomal segments . FEBS Lett . 129 : 21 – 24 . doi: 10.1016/0014-5793(81)80745-4 . OpenUrl CrossRef 89. ↵ Schmidt H et al. 2020 . A High-Quality Genome Assembly from Short and Long Reads for the Non-biting Midge Chironomus riparius (Diptera) . G3 Genes|Genomes|Genetics . 10 : 1151 – 1157 . doi: 10.1534/G3.119.400710 . OpenUrl CrossRef 90. ↵ Schreiber D , Pfenninger M . 2021 . Genomic divergence landscape in recurrently hybridizing Chironomus sister taxa suggests stable steady state between mutual gene flow and isolation . Evol Lett . 5 : 86 – 100 . doi: 10.1002/EVL3.204 . OpenUrl CrossRef 91. ↵ Sellinger TPP , Abu-Awad D , Tellier A . 2021 . Limits and convergence properties of the sequentially Markovian coalescent . Mol Ecol Resour . 21 : 2231 – 2248 . doi: 10.1111/1755-0998.13416 . OpenUrl CrossRef 92. ↵ Sellinger TPP , Awad DA , Moest M , Tellier A . 2020 . Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data Mauricio, R, editor . PLoS Genet . 16 : e1008698 . doi: 10.1371/journal.pgen.1008698 . OpenUrl CrossRef 93. ↵ Sellinger TPP , Johannes F , Tellier A . 2023 . Improved inference of population histories by integrating genomic and epigenomic data . Elife . 12 . doi: 10.7554/ELIFE.89470.1 . OpenUrl CrossRef 94. ↵ Shaikhutdinov N , Gusev O . 2022 . Chironomid midges (Diptera) provide insights into genome evolution in extreme environments . Curr Opin Insect Sci . 49 : 101 – 107 . doi: 10.1016/J.COIS.2021.12.009 . OpenUrl CrossRef 95. ↵ Simão FA , Waterhouse RM , Ioannidis P , Kriventseva EV. , Zdobnov EM . 2015 . BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs . Bioinformatics . 31 : 3210 – 3212 . doi: 10.1093/BIOINFORMATICS/BTV351 . OpenUrl CrossRef PubMed 96. ↵ Smit AFA , Hubley R , Grenn P. 2015 . RepeatMasker Open-4.0 . RepeatMasker Open . 97. ↵ Soni V , Pfeifer SP , Jensen JD . 2024 . The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects . Genome Biol Evol . 16 . doi: 10.1093/GBE/EVAE004 . OpenUrl CrossRef 98. ↵ Stanke M , Diekhans M , Baertsch R , Haussler D . 2008 . Using native and syntenically mapped cDNA alignments to improve de novo gene finding . Bioinformatics . 24 : 637 – 644 . doi: 10.1093/BIOINFORMATICS/BTN013 . OpenUrl CrossRef PubMed Web of Science 99. ↵ Stroeven AP et al. 2016 . Deglaciation of Fennoscandia . Quat Sci Rev . 147 : 91 – 121 . doi: 10.1016/J.QUASCIREV.2015.09.016 . OpenUrl CrossRef 100. ↵ Stumpf MPH , McVean GAT . 2003 . Estimating recombination rates from population-genetic data . Nature Reviews Genetics 2003 4 : 12 . 4: 959 – 968 . doi: 10.1038/nrg1227 . OpenUrl CrossRef PubMed Web of Science 101. The Darwin Tree of Life Project Consortium . 2022 . Sequence locally, think globally: The Darwin Tree of Life Project . Proceedings of the National Academy of Sciences . 119 : e2115642118 . doi: 10.1073/pnas.2115642118 . OpenUrl Abstract / FREE Full Text 102. ↵ Waldvogel AM et al. 2020 . Evolutionary genomics can improve prediction of species’ responses to climate change . Evol Lett . 4 : 4 – 18 . doi: 10.1002/EVL3.154 . OpenUrl CrossRef 103. ↵ Waldvogel AM et al. 2018 . The genomic footprint of climate adaptation in Chironomus riparius . Mol Ecol . 27 : 1439 – 1456 . doi: 10.1111/MEC.14543 . OpenUrl CrossRef 104. ↵ Waldvogel AM , Pfenninger M . 2021 . Temperature dependence of spontaneous mutation rates . Genome Res . 31 : 1582 – 1589 . doi: 10.1101/GR.275168.120 . OpenUrl Abstract / FREE Full Text 105. ↵ Wickham H , François R , Henry L , Müller K , Vaughan D. 2023 . dplyr: A Grammar of Data Manipulation . 106. ↵ Wilke CO. 2020 . cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’ . URL: https://CRAN.R-project.org/package=cowplot . Acessed 18 October 2018. 107. ↵ Wilton PR , Carmi S , Hobolth A . 2015 . The SMC’ is a highly accurate approximation to the ancestral recombination graph . Genetics . 200 : 343 – 355 . doi: 10.1534/GENETICS.114.173898 . OpenUrl Abstract / FREE Full Text 108. ↵ Wiuf C , Hein J . 1999 . Recombination as a Point Process along Sequences . Theor Popul Biol . 55 : 248 – 259 . doi: 10.1006/TPBI.1998.1403 . OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted April 06, 2024. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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