Meiotic double strand DNA breaks and spontaneous mutation in Drosophila melanogaster

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If meiotic exchange is mutagenic, this would add to the costs of sexual reproduction and affect patterns of genome evolution, but much of the evidence for this is indirect. In the fruit fly Drosophila melanogaster , it is possible to completely suppress endogenous DSBs while retaining normal fertility. We took advantage of this system to generate fly strains with and without a mutant allele of mei-P22 , a gene that is essential for meiotic DSB formation, on a common genetic background. This allowed us to investigate the relationship between DSBs and genome-wide mutation patterns, using a mutation accumulation design to allow un-selected spontaneous mutations to be observed. Following 30 generations of mutation accumulation, we identified over 1800 mutations by whole-genome sequencing. The presence of meiotic DSBs had little effect on the rate and spectrum of point mutations. We found that mutations were more likely to occur in areas of the genome with higher rates of crossover recombination, regardless of whether meiotic DSBs were occurring. We also found that the rate of transposable element insertions across multiple TE families was substantially elevated in the group lacking meiotic DSBs, suggesting that host suppression of mobile genetic elements is closely associated with meiotic recombination mechanisms. Introduction Meiotic recombination (MR) is a key driving force of evolution. MR is ubiquitous across eukaryotes and is a core aspect of sexual reproduction. While MR is generally considered evolutionarily favorable ( Keightley & Otto, 2006 ), these benefits come with costly tradeoffs. One obvious cost of MR is the time and energy cells must spend to perform this process. For example, in the model yeast Saccharomyces cerevisiae , the mitotic cell cycle takes approximately 90 minutes under optimal conditions, whereas meiosis and sporulation require at least 8-12 hours to complete ( Duina et al., 2014 ; Ray & Ye, 2013 ). From a population genetics perspective, beneficial alleles that work well in concert with one another can be broken apart by MR, slowing adaptation ( Charlesworth & Barton, 1996 ; Charlesworth & Charlesworth, 1975 ). At the molecular level, while MR typically leads to proper chromosome segregation, errors in this process can lead to chromosomal nondisjunction and aneuploidy ( Koehler et al., 1996 ). Another potential molecular cost relates to the formation and repair of meiotic double strand DNA breaks (DSBs), which are required for MR. Some DSBs occur spontaneously as a result of DNA damage; these events, described as “non-programmed” or “exogenous” DSBs, are considered highly mutagenic because error prone pathways are commonly used to repair them ( Rodgers & McVey, 2016 ; Strathern et al., 1995 ). In contrast, the “endogenous” or “programmed” DSBs that occur during meiosis are thought be more tightly regulated and repaired using specialized pathways ( Longhese et al., 2009 ; Wei et al., 2019 ). Nevertheless, endogenous DSBs likely contribute at least some additional risk of mutation, and there is some evidence for such an effect. A positive correlation between crossover recombination rate and molecular diversity may be an indirect source of evidence on the relationship between endogenous DSBs and mutation, but this pattern is not observed in all species, and can instead be explained by reduced background selection in areas of higher crossover recombination ( Begun & Aquadro, 1992 ; Cutter & Payseur, 2013 ; Smukowski & Noor, 2011 ). Stronger evidence of a relationship between MR and mutation comes from humans, where SNPs and de novo germline mutations are found more commonly near known recombination hotspots ( Hinch et al., 2023 ; Pratto et al., 2014 ). Similarly, short insertions and deletions (INDELs) and structural variants have been shown to be associated with recombination hotspots in human pedigree studies ( Beyter et al., 2021 ; Hinch et al., 2023 ). Parent-offspring sequencing has also revealed an association between crossover events and germline mutations in humans ( Halldorsson et al., 2019 ) and to a lesser extent in bees ( Liu et al., 2017 ; Yang et al., 2015 ). Parent-offspring sequence data in Drosophila melanogaster and D. simulans did not detect any relationship between crossovers and germline mutations, but the number of relevant phased mutations was low (14 female-derived mutations) ( Wang et al., 2023 ). In S. cerevisiae , meiosis is associated with a higher mutation rate than mitosis ( Magni, 1963 ), and this effect has been directly linked to endogenous DSBs ( Rattray et al., 2015 ). Additional direct tests of the impact of MR on the rate and spectrum of point mutations would be valuable. Another mutational process associated with recombination is the insertion of transposable element (TE) sequences. TEs are found more often in genomic regions of low recombination ( Kent et al., 2017 ; Kofler et al., 2012 ; Petrov et al., 2011 ). The Hill-Robertson effect and selection against ectopic recombination could drive this pattern ( Biemont et al., 1997 ; Dolgin & Charlesworth, 2008 ; Kelleher et al., 2020 ), but there is also evidence that TEs suppress recombination ( Huang et al., 2025 ). The formation of endogenous DSBs has been shown to be related to both TE mobility and TE silencing ( Dufourt et al., 2014 ; Wei et al., 2019 ; Wylie et al., 2016 ). This suggests that altering the formation of endogenous DSBs could influence TE mobility in an activating or suppressing manner, but this possibility has not been thoroughly explored. Quantifying the mutational consequences of endogenous DSBs is challenging. In many organisms, endogenous DSBs are essential for proper chromosomal segregation and gamete formation ( Petronczki et al., 2003 ). In such systems, researchers are typically limited to comparing mutation between mitotically and meiotically dividing cells or examining mutational patterns near crossover events and recombination hotspots, which are approaches with significant limitations. Comparing mitotic versus meiotic cells confounds the effects of DSBs with the broader differences between fundamentally distinct cell division processes. Additionally, focusing on crossover-associated mutations provides only a partial view of mutations that may be associated with endogenous DSBs, since many meiotic DSBs are resolved without crossing over. These methodological constraints therefore limit our understanding of how endogenous DSBs truly impact the overall mutational landscape. Conveniently, D. melanogaster can perform meiosis and form viable gametes without endogenous DSBs. In nearly all Drosophila species, males naturally perform meiosis without endogenous DSBs or crossover recombination ( Lee & Rosin, 2024 ). Laboratory strains have been developed where meiotic DSBs are also absent in the female germline. One example contains the allele mei-P22[P22] (also known as mei-P22[1], mei-P22[McKim] and henceforth referred to as mei-P22 − ), generated with P-element mutagenesis ( Sekelsky et al., 1999 ). The mei-P22 protein forms a dimer with mei-W68 (a paralog of spo-11 ) and interacts with Trem , Vilya, and Narya to form endogenous DSBs ( Lake et al., 2011 , 2015 , 2019 ; Liu et al., 2002 ; Mehrotra & McKim, 2006 ). The mei-P22 − allele effectively eliminates transcription of mei-P22 , preventing endogenous DSB formation and MR in the female germline while permitting viable gamete production ( Iida & Lilly, 2004 ; Liu et al., 2002 ; McKim et al., 1998 ). Importantly, the effect of mei-P22 − is distinct from that of balancer chromosomes, which inhibit crossing over but do not prevent DSBs ( Miller et al., 2016 ). In D. melanogaster females, meiosis involves about six crossovers throughout the genome, but a total of 20-24 endogenous DSBs that must be repaired ( Carpenter, 1987 ; Mehrotra & McKim, 2006 ). Compared to organisms with recombination hotspots, the landscape of crossing over in D. melanogaster females is relatively uniform. The correlation between mutation and recombination found in humans may be more apparent than such a relationship in flies would be, because there are specific 1-2 kb regions where recombination (and thus endogenous DSBs) tend to occur, mediated by PRDM9 hotspots ( Baudat et al., 2010 ; Jeffreys et al., 2001 ). Endogenous DSBs in D. melanogaster have only been observed to occur within euchromatin, and are influenced by accessible chromatin states linked to active transcription but are otherwise broadly distributed throughout the genome ( Adrian et al., 2016 ; Adrian & Comeron, 2013 ; Mehrotra & McKim, 2006 ). To study the effect of meiotic DSBs on mutation, we established two sets of mutation accumulation (MA) lines: one using females undergoing normal endogenous double-strand break (DSB) formation, and another using mei-P22 − females in which endogenous DSBs were absent. In MA experiments, repeated bottlenecking minimizes the efficacy of natural selection, allowing most new mutations to become fixed with neutral probability (reviewed in Katju & Bergthorsson, 2019 ). Such experiments are a powerful means of generating large, unbiased mutation datasets with relatively modest sequencing effort. We used this approach to compare the pattern of germline mutation in the presence and absence of endogenous meiotic DSBs. We found that eliminating endogenous DSBs had little impact on the rate and spectrum of point mutations but caused a significant increase in the rate of transposable element insertion throughout the genome. Methods Fly stocks and mutation accumulation We reared flies on defined medium (14.3 g/L agar, 92.3 g/L white sugar, 46 g/L debittered yeast, 7.4 g/L potassium sodium tartrate, 0.93 g/L potassium phosphate, 0.46 g/L sodium chloride, 0.46 g/L calcium chloride, 0.46 g/L magnesium chloride, 0.46 g/L iron sulfate, 0.5% propionic acid) in standard vials seeded with live yeast, incubating at 25 C with a 12-hour light:dark cycle. We obtained stock 4931 from the Bloomington Drosophila Stock Center (RRID:BDSC_4931) which is y 1 w 1 /Dp(1;Y) y + ; P{ w [+mC]=lacW} mei-P22 P22 ; sv spa-pol . To ensure a fair comparison of the mutation patterns between treatment groups we sought to establish MA lines with and without mei-P22 − from the same genetic background. To create these lines we first backcrossed mei-P22 − into a stock of w 1 y 1 /Dp(1;Y) y + for five generations; mei-P22 − can be tracked in these crosses because it is marked with a mini-white allele, which partially reverses the eye color effect of w 1 ( mei-P22 − does not eliminate crossing over in the heterozygous state). In this genetic background, XX females are y 1 / y 1 and express a yellow body color phenotype; XY males are y 1 /Dp(1;Y) y + , where Dp(1;Y) y + marks the Y chromosome with a functional copy of y , such that males express a wild type body color. The presence of mei-P22 − increases the rate of non-disjunction ( Liu et al., 2002 ; Sekelsky et al., 1999 ), resulting in viable XXY females and sterile X∅∥ males, which can be identified by body color (wild type body color in XXY females and yellow body color in X∅∥ males). After backcrossing for five generations, we imposed single-pair bottlenecks for an additional five generations to remove standing genetic variation. We then performed a final set of expansion crosses, selecting either for or against mei-P22 − , establishing 20 lines in each treatment group. For MA, each line was bottlenecked for 30 consecutive generations using one randomly selected male and one randomly selected female, excluding XXY and X∅∥ flies. Five lines went extinct during the course of the experiment, which ultimately concluded with 16 lines where endogenous DSBs occurred ( mei-P22 + ) and 19 lines where they did not ( mei-P22 − ). DNA Extraction and Sequencing Upon completion of the 30 th generation of MA, we collected all female offspring (6-24 individuals per line) and froze them at –80 C. We extracted genomic DNA from stored flies using the Qiagen DNeasy Blood & Tissue Kit with the insect protocol (catalog #69504), purified using the Qiagen DNeasy PowerClean Kit (catalog #12877-50), and quantified using a Qubit Fluorometer (ThermoFisher Scientific). We submitted DNA to the University of Wisconsin Biotechnology Center DNA Sequencing Core Facility (RRID:SCR_017759) for paired-end whole genome sequencing using an Illumina NovaSeq X Plus. Calling point mutations We obtained about 2.3 billion reads in total, which we quality checked using FastQC (v0.12.1) and trimmed using Trimmomatic (v0.39) ( Andrews, 2010 ; Bolger et al., 2014 ). After masking repetitive regions using RepeatMasker (v4.1.5), we mapped reads to the D. melanogaster reference genome (v6.59) using bwa-mem2 (v2.2.1) ( Dos Santos et al., 2015 ; Smit et al., 2013 ; Vasimuddin et al., 2019 ), resulting in a mean depth of coverage of 99. We sorted and indexed BAM files using Samtools (v1.6), removed duplicate reads using Picard Tools MarkDuplicates (v3.1.1), and called variants using GATK HaplotypeCaller (v4.5.0.0) ( Broad Institute, 2019 ; Li et al., 2009 ). Finally, we used Rstudio (v4.3.2) for variant filtering, statistical modeling, and figure creation ( R Core Team, 2023 ; Van der Auwera & O’Connor, 2020 ). We utilized glmmTMB (v1.1.11; Brooks et al., 2017 ) to fit generalized linear mixed effect models of genome wide mutation patterns. To filter our callset we first selected variants that were only present in one line and required that the site of interest have a called genotype in all lines (finding the exact same mutation in two or more lines due to convergent mutation is highly unlikely, and such cases should represent sequencing errors or preexisting variation). We required there to be at least 10 supporting reads for the variant allele and considered only variants passing the filters recommended by GATK: mapping quality ≥ 50, quality by depth ≥ 2, Fisher strand bias ≤ 60, strand odds ratio ≤ 3, absolute mapping quality rank sum ≤ 8, and absolute read position rank sum ≤ 4. We additionally required that depth at a variant site was no less than half and no greater than twice the median chromosome-wide depth in that line. Finally, we removed any single-nucleotide variants (SNV) or INDELs called within 1 kb of a structural variant (SV) or TE insertion. Where possible, we applied the same filtering criteria to non-variant sites when calculating the number of callable sites in the genome. For a site to be considered callable we required that site to pass the filters in all lines. We considered variants only on the major contigs listed in the D. melanogaster reference genome that are present in female flies and that normally recombine (2L, 2R, 3L, 3R, and X; variants called on chromosome 4 are also listed in Supplementary Material). The reference genome for these contigs includes 133,880,608 sites and our final callset includes 99,458,130 sites (74%). Of the 34,422,478 non-callable sites, 20,052,895 were not considered callable because they were deemed repetitive by Repeat Masker. The remaining 14,369,583 sites were considered non-callable because they failed one or more of our filtering metrics. We categorized events as single nucleotide variants (SNV), short insertions and deletions (INDEL), multi-nucleotide variants (MNV; two or more SNV within 1 kb in the same line), and complex variants (COMPLEX; two or more variants within 1 kb in the same line where at least one variant was an INDEL). For the purposes of mutation rate estimation, each MNV and COMPLEX group of variants was counted only once. In order to understand the predicted functional impacts of the variants in our dataset, we used Ensemble’s variant effect predictor (release 113) to identify the most severe effect predicted for each variant ( McLaren et al., 2016 ). We set the upstream/downstream distance to zero and kept all other parameters at their defaults. We estimated mutation rates using both homozygous and heterozygous variants, and dividing by 30 MA generations. By including heterozygous mutations, this procedure will overestimate the true mutation rate, since more than one copy of a given chromosome is effectively included in each line. We did not find evidence that the proportion of mutations that are heterozygous differs between treatment groups (Results), and so we don’t expect this bias to affect our treatment comparison. Considering only homozygous mutations would produce the opposite bias, since mutations cannot become homozygous immediately. Performing our statistical comparisons using only homozygous mutations did not alter our conclusions (supplemental R script). Calling structural variants To identify larger structural variants that may have been missed by HaplotypeCaller we used MANTA (v.1.6.0) (X. Chen et al., 2016 ). For practical purposes we analyzed our 35 samples in 3 blocks (10, 10 and 15 samples) and then combined the outputs. Each analysis block had lines from both treatment groups. We then filtered variants by selecting only those that were present in one line with a called genotype for every line present in that block. We removed any variants that did not have at least 10 supporting spanning reads or 10 supporting split reads, and considered only cases with the maximum possible quality score. Calling transposable element variants We used the McClintock meta-pipeline (v.2.0.0) to detect transposable element (TE) insertions and excisions using multiple detection methods (J. Chen et al., 2023 ; Nelson et al., 2017 ), specifically, the McClintock implementation of TEMP2 ( Yu et al., 2021 ) employing the default settings from McClintock. Because TE callers rely on information from discordant read pairs, where one read in the pair maps to the reference genome and the other maps to a TE sequence, several studies have shown that trimming reads can improve the detection accuracy of these tools, despite a reduction in coverage depth ( Daigle et al., 2025 ; Yu et al., 2021 ). This is especially relevant for our dataset, where the median insert size (about 300 bp) is close to the total length of read pairs (150 × 2 bp), which will lead to many overlapping pairs. For this reason, we trimmed all reads at the 3’ end down to 50 bp using fastp (J. Chen et al., 2023 ) before TE detection. We used theconsensus TE sequences and reference genome annotation created by Rech et al. (available at http://doi.org/10.20350/DIGITALCSIC/13765 and http://doi.org/10.20350/DIGITALCSIC/13894 , respectively) as inputs to McClintock ( Rech et al., 2022 ). To identify non-reference TE insertions unique to each MA line, we compared the sets of predicted insertions across all samples. Because TE callers often report slightly different breakpoint coordinates for the same insertion (J. Chen et al., 2023 ; Daigle et al., 2025 ), we treated insertions in different lines from the same TE family located within 50 bp of one another as representing the same event. Using this criterion, we identified TE insertions confidently present in only one MA line, which we interpreted as de novo mutations specific to that lineage. We excluded non-reference insertions present in more than one line, as it is unclear if these are the result of detection error, recurrent mutation TE insertions at the same site, or variants that remained segregating in the stocks used to create the MA lines. TEMP2 additionally checks for the presence or absence of reference TE sequences. We interpreted reference TEs uniquely absent in one MA line as de novo TE excision events. However, we note that we do not have the ability to distinguish between a deletion that removes a TE and a true enzymatic excision of a TE. Analysis of genome wide mutation patterns We used statistical models to test whether specific genomic features could account for variation in the locations of mutations throughout the genome, and whether these patterns differed between the two treatment groups. We considered crossover recombination rates, replication timing, chromatin state information and G/C content. We used 7657 genomic windows defined by chromatin state ( Filion et al., 2010 ), and added information on crossover recombination rates ( Comeron et al., 2012 ) and replication timing ( Schwaiger et al., 2009 ) for each window, using weighted averaging to account for the different window sizes for each variable. We used the Kc cell type replication timing data in our analyses ( Schwaiger et al., 2009 ), but note that the Kc and Cl8 cell type replication timing values are strongly correlated ( P < 2.2 × 10 −16 ; R 2 = 0.812). For each window we used a custom bash script to count the number of sites that were considered callable in our dataset as well as the total number of G/C sites in that window according to the reference genome. The chromatin state data includes five categories ( Filion et al., 2010 ), with two being distinct types of euchromatin, referred to as “red” (RE) and “yellow” (YE), two being distinct types of heterochromatin, referred to as “blue” (BL) and “green” (GR), and one being a highly repressed type of chromatin, referred to as “black” (BK). The replication timing and chromatin state datasets were built under an older release of the D. melanogaster reference genome, so we used the flybase Drosophila Sequence Coordinates Converter ( https://flybase.org/convert/coordinates ) to convert coordinates to reference version r6. We excluded chromosomes Y and 4, any windows missing replication timing or recombination data, as well as any windows with zero callable sites. This windowed approach allowed us to run generalized mixed effect models with the number of variants observed in that window as the response variable and genomic features as predictors. We scaled recombination rate and G/C content to their respective means to facilitate model convergence. We also included power (the number of callable sites multiplied by the number of lines, scaled linearly) as either an additional predictor or an offset term. To compare treatment groups, we duplicated the window data, included treatment as a predictor, and included window identity as a random effect. We found that including chromosome as a random effect did not improve model fit. We used negative binomial generalized linear mixed effect models (GLMMs) with the nbinom2 family ( Hardin & Hilbe, 2007 ) implemented using glmmTMB ( Brooks et al., 2017 ), which accounts for any overdispersion. Results Point mutation rate We identified a total of 991 point mutations across 35 MA lines that accumulated mutations for 30 generations ( Fig. 1A ). The presence of meiotic DSBs did not detectably influence the rate of any point mutation type (SNV: t-test, P = 0.92; INDEL: t-test, P = 0.95; MNV: Wilcoxon rank sum test, P = 0.25; COMPLEX: Wilcoxon rank sum test, P = 0.24), or the distribution of point mutations among chromosome arms (χ 2 = 1.36, P = 0.85). The overall point mutation rate did not vary significantly among chromosome arms (χ 2 = 4.11, P = 0.39), or on the X chromosome versus autosomes (binomial test, P = 0.45). The majority of the point mutations we identified were in the homozygous state (55.3%), as expected given that MA took place under full-sib mating. We found that that the frequency of homozygous versus heterozygous variants did not differ significantly between groups (Fisher’s exact test, P = 0.11; Fig. S1). Regardless of the treatment group, variants on the X chromosome were more likely to be homozygous than those on the autosomes (Fisher’s exact test, P = 2.57 × 10 −4 ). We found no evidence that the amount of variance among MA lines differed between treatment groups for SNVs, INDELs, MNVs, COMPLEXs, or SVs (Levene tests, all P > 0.25). Download figure Open in new tab Figure 1. Rates of mutation by group and mutation type. Error bars represent 95% confidence intervals. (a) Rates of single nucleotide variants (SNV), short insertions and deletions (INDEL), multinucleotide variants (MNV), complex variants (COMPLEX), and structural variants (SV) per callable site per generation. (b) Transposable element (TE) insertions and excisions per line per generation. Transposable element movement rate We detected 855 cases of TE movement, of which 82% were insertions. We found a significant difference between treatments in the average number of TE events per line (Welch’s t-test, t = 2.35, P = 0.027) where the group with endogenous DSBs averaged 0.68 events per line per generation and the group without endogenous DSBs averaged 0.93 TE events per line per generation ( Fig. 1B ). This result persists when putative TE excisions are excluded (Welch t-test, t = 2.43, P = 0.022), but not when considering excisions alone (Welch t-test, t = 0.61, P = 0.543). We found significantly more variance in the number of TE insertions per MA line in the group that did not perform endogenous DSBs (Levene’s Test, P = 7.15 × 10 −3 ), but little evidence for a difference in terms of coefficient of variation (bootstrap P = 0.083). The group without endogenous DSBs therefore incurred more TE insertion events per generation, with increased among-line variance attributable to the increased mean. In both treatment groups, we observed a significantly higher proportion of TE insertions on the X chromosome than expected based on chromosome size (binomial tests; with endogenous DSBs: P = 1.41 × 10 −7 ; without endogenous DSBs: P = 1.07 × 10 −9 ). We detected TEs from numerous different “families” ( Fig. 2 ), including S elements (long inverted repeat DNA transposon), H elements (hAT superfamily DNA transposon), Doc elements (non-LTR LINE retrotransposon) and roo elements (LTR retrotransposon). The increased rate of transposition we observed in the lines lacking endogenous DSBs was evident in most of these TE families ( Fig. 2 ). Download figure Open in new tab Figure 2. Transposable element activity by family and treatment group. Only TE families with at least five novel insertions or excisions across both treatment groups and at least one insertion/excision in each group are shown here. A complete list is given in Supplementary Material. Structural variants Based on sequencing coverage, we identified one mei-P22 − MA line with apparent trisomy of chromosome 4 (Fig. S2). We also identified 11 large deletions, 13 large insertions, and 2 tandem duplications for a total of 26 structural variants (size 265-7799 bp). The presence of meiotic DSBs did not influence the rate or average length of SVs (Wilcoxon rank sum test, P = 0.34; Welch t-test, t = 0.11, P = 0.91). The distribution of structural variants across chromosome arms did not differ from the null expectation (χ 2 = 6.52, P = 0.16). Mutation spectrum The presence of meiotic DSBs did not significantly influence the SNV spectrum ( Fig. 3A ; χ 2 = 6.87, P = 0.23) or the transition-transversion ratio ( Fig. 3B , Fisher’s exact test, P = 0.19). In both treatment groups, SNVs were much more likely to occur at G/C sites than at A/T sites (binomial tests, all P < 3.85 × 10 −11 ). However, we found some evidence that the strength of this effect differed between treatments (χ 2 = 4.03, P = 0.045), with greater bias towards mutation at G/C sites in the group without meiotic DSBs (69.59% of SNVs versus 62.26%, Fig. 3C ). The INDEL mutations in our dataset showed a deletion bias ( Fig. 3D ; binomial test, P = 5.84 × 10 −4 ), with no difference in this bias between treatment groups (Fisher’s exact test, P = 0.53). The median deletion size was 6 bp and the median insertion size was 15 bp, unaffected by treatment (Wilcoxon rank sum tests, all P > 0.30). Download figure Open in new tab Figure 3. Point mutation spectrum in each treatment group. (a) The spectrum of single nucleotide variants. 95% confidence intervals reflect Wilson score intervals for multinomial proportions. (b) The ratio of transitions to transversions with bootstrap 95% confidence intervals. (c) The ratio of variants that occurred at A/T sites versus G/C sites, with bootstrap 95% confidence intervals. (d) Length distribution of short insertions and deletions. To ensure that selection did not bias our results, we examined the frequency of mutations in coding regions and their consequences for protein sequences. We found that 72.7% of SNVs and 74.4% of INDELs occurred within genes (including introns; Fig. 4A ), which does not significantly differ from a null expectation of 75.08% for our callable sites (binomial tests, P = 0.17 and P = 0.94, respectively). Similarly, 71.0% of genic SNVs were nonsynonymous, which does not differ significantly from the expected value of 74.4% (binomial test, P = 0.38; Fig. 4B ). Additionally, these metrics did not differ between treatment groups (Fisher’s exact tests of genic frequency; SNV: P = 0.07; INDEL: P = 0.87; Fisher’s exact test of nonsynonymous SNVs: P = 0.85). We therefore find no evidence that selection biased the accumulation of mutations overall, or affected the treatment groups differently. Download figure Open in new tab Figure 4. Genomic consequences of point mutations. (a) Proportion of variants found within genes (including introns). The grey dashed line is the proportion of callable sites in our dataset that were genic. (b) Proportion of coding SNVs that were nonsynonymous. The grey dashed line is the null value calculated in Sharp et al. (2016). Genomic context of mutations The presence of endogenous DSBs could affect disproportionately mutation patterns in specific regions of the genome. Considering models of point mutations and TE activity across 7657 genomic windows, we found no evidence that treatment group interacted with G/C content, crossover recombination rate, chromatin state or replication timing in determining the rate of mutation (likelihood ratio tests, all P > 0.15). Combining treatment groups, point mutations were associated with lower G/C content ( z = –2.19, P = 0.028) and higher crossover recombination rates ( z = 2.19, P = 0.028), with no significant effect of replication timing ( z = –0.89, P = 0.37) or chromatin state (likelihood ratio test, P = 0.80) ( Fig. 5 ). TE activity was associated with lower G/C content ( z = –2.76, P = 5.75 × 10 −3 ) and differed significantly among chromatin states (likelihood ratio test, P = 0.04), but did not vary with crossover recombination rate ( z = –1.52, P = 0.13) or replication timing ( z = –0.25, P = 0.80) ( Fig. 6 ). Download figure Open in new tab Figure 5. Relationships between point mutations and genomic context. Results are based on regression analysis of 7657 genomic windows, combining data across treatment groups, with 95% error bands. Point mutations were more likely to occur in regions of lower G/C content (A) and in regions of high crossover recombination (B). Point mutations were not significantly associated with replication timing (C) or chromatin state (D). Download figure Open in new tab Figure 6. Relationships between transposable element activity and genomic context. Results are based on regression analysis of 7657 genomic windows, combining data across treatment groups, with 95% error bands. TE insertions were associated with lower G/C content (A) and chromatin state (D). TE insertions were not significantly associated with crossover recombination rate (B) or replication timing (C). Discussion Eliminating endogenous meiotic DSBs appeared to have little effect on the overall rate of point mutations, but a substantial effect on the rate of transposition ( Fig. 1 ). Our experimental design focused on genome-wide mutation patterns in the presence or absence of meiotic DSBs, rather than on mutations occurring at known or controlled sites of meiotic exchange. This approach permitted detection of ‘global’ effects–– transposition throughout the genome––but likely limited our power to detect mutation patterns associated with specific DSB locations. While not significant, we do find that the rate of single-nucleotide changes was greater in flies with endogenous DSBs, as expected if DSB repair is mutagenic. If we assume 24 meiotic DSBs per genome per generation ( Carpenter, 1987 ; Mehrotra & McKim, 2006 ), and that each repair tract involves the synthesis of 5000 bp ( Adams et al., 2003 ), our point estimates would be consistent with an increased mutation rate within such tracts of 6.7-fold. We considered whether the presence of endogenous DSBs might disproportionately affect point mutation patterns in specific regions of the genome, particularly euchromatic regions where DSBs are more likely to occur, or areas of the genome showing higher levels of crossover recombination; we did not detect significant interactions with treatment for either of these factors. Instead, we found that point mutations were associated with regions of higher crossing over regardless of the presence of meiotic DSBs ( Fig. 5 ), suggesting that some regions of the genome are more likely to experience both meiotic exchange and mutation, although the underlying mechanism for this is unclear. Such a pattern would contribute to the association between nucleotide diversity and crossover rate, independently of any mutagenic effect of DSBs. Importantly, we did not attempt to call point mutations in repetitive regions, which likely have distinct mutation patterns ( Flynn et al., 2018 ; López-Cortegano et al., 2025 ; Nishant et al., 2009 ). An additional caveat to this analysis is that it incorporates data collected in separate experiments using cell lines and inbred strains that may or may not reflect the genomic features of the flies we studied. An increased risk of mutation during the resolution of DSBs could be mitigated if the proteins that create DSBs also facilitate DNA repair, including the repair of exogenous, non-programmed DSBs. The suppression of endogenous DSBs in mei-P22 − flies could limit the activity of DSB-dependent repair mechanisms such as the GATOR complex, potentially dampening responses to exogenous DNA damage ( Wei et al., 2019 ), and counteracting any anti-mutagenic effect of eliminating endogenous DSBs. In contrast to point mutations, we found a clear effect of our treatment on TE activity, with the rate of novel TE insertions increasing by 41% in mei-P22 − flies ( Fig. 1B ). This increase was not restricted to a particular TE family ( Fig. 2 ) or genomic context; TE events were associated with lower G/C content and certain chromatin states regardless of treatment ( Fig. 6 ). One possible interpretation of this pattern is that mei-P22 − flies were “stressed”, inducing TE activity, but only specific forms of stress appear to have such an effect in Drosophila ( de Oliveira et al., 2021 ; Merenciano et al., 2025 ; Milyaeva et al., 2023 ; Mombach et al., 2022 ; Sharp & Agrawal, 2016 ). Instead, our results are consistent with previous observations linking TE activity with meiotic DSBs. Alleles disrupting the GATOR complex, which is involved in DSB repair, cause increased levels of TE expression ( Wei et al., 2019 ), and alleles preventing proper synaptonemal complex formation result in fewer meiotic DSBs and increased TE insertion genome-wide ( Miller, 2020 ). There is also a trend towards increased transposition in males, where endogenous DSBs are absent ( Wang et al., 2023 ). TE repression may therefore be connected with the generation and repair of meiotic DSBs. We found that TE insertions were more likely to occur on the X chromosome, in G/C poor regions, and in certain chromatin states ( Fig. 6 ), matching previous findings ( Adrion et al., 2017 ). However, the insertion bias towards certain chromatin states does not seem to be based simply on accessibility; insertion rates in the two types of euchromatin, RE and YE, both differed significantly from the highly repressed BK chromatin, but did so in opposite directions ( Fig. 6 ). The continual improvement of bioinformatic tools for detecting TE movement will aid further investigation into complex insertion site preferences and distinguishing genomic patterns of TE mutation versus selection ( Sultana et al., 2017 ; Zhang et al., 2020 ). In conclusion, our results indicate that point mutations arising due to meiotic DSB repair represent a small fraction of the genome-wide mutational burden in D. melanogaster , and that spatial correlations between mutation and recombination may occur even in the absence of meiotic DSB repair. Our study also adds to the growing evidence that meiotic recombination in this organism is tightly associated with the suppression of TE activity. Data availability Whole genome sequence data is available from the NCBI SRA under bioproject accession number PRJNA1346113. The R script used for analyses and its subsequent R workspace, our point mutation callset, TE callset, SV callset, the genomic windows used in analyses, and supplemental figures are available through Dryad DOI: 10.5061/dryad.b2rbnzstx. Study funding Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM154954 to NPS. 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