Randomised controlled trial.To determine the effectiveness of a sustainable community-based intervention designed to prevent serious complications and death 2 years after discharge in people with spinal cord injury in Bangladesh.Bangladesh.A pragmatic randomised controlled trial was undertaken. People who had sustained a spinal cord injury in the preceding 2 years, were wheelchair-dependent, and were about to be discharged from hospital in Bangladesh were recruited and randomised to an Intervention or Control group using a concealed allocation procedure stratified by level of lesion (tetraplegia/paraplegia). Participants in the Intervention group received 36 phone calls and three home visits over the first 2 years following discharge. All participants received usual post-discharge care. Survival status and date of death were determined by blinded assessors 2 years after randomisation.p from log rank test 0.85). There were no clinically important or statistically significant average causal effects of intervention on the incidence or severity of complications.Between July 2015 and March 2018, 410 participants were randomised . There was no loss to follow up. At 2 years, 15 (7.4%) participants in the Intervention group and 16 (7.8%) participants in the Control group had died (hazard ratio from unadjusted Cox model = 0.93 ; A program of community-based care for people with recent spinal cord injury in Bangladesh involving frequent phone contact and occasional in-person contact with a health professional after discharge from hospital is no better at preventing death at 2 years than usual care. In low- and middle-income countries, people who sustain spinal cord injuries are likely to experience serious complications after discharge from hospital. Common complications include pressure ulcers, respiratory and urinary tract infections, depression, faecal and urinary incontinence, and autonomic dysreflexia , 2. ThesMortality rates after discharge from hospital in low- and middle-income countries are much higher than in high-income countries , 3. ThatThe first-line strategies for prevention and management of common complications after spinal cord injury are neither expensive nor technically difficult to implement. For example, pressure ulcers can be prevented and managed by providing suitable cushions and mattresses and regular repositioning , 10. BlaIn an attempt to reduce the rates of secondary complications and death soon after discharge from hospital, we designed an affordable and potentially sustainable community-based program of care for people who had been discharged from hospital with spinal cord injury. A key feature of the intervention is frequent phone contact between health professionals and people with spinal cord injury in the 2 years after discharge from hospital. The health professionals help people with spinal cord injury identify complications and intervene early, before the complications become severe, and provide advice on simple strategies that people with spinal cord injury can implement themselves to prevent and manage complications.2 (95% CI -0.3 to 4.9). The second trial was a pilot trial of 30 people with spinal cord injury followed for 2 years [There is widespread acceptance of the need to provide programs of care for people living with spinal cord injury in low- and middle-income countries , but the 2 years . It confThe purpose of the CIVIC trial was to determine the effectiveness of a community-based program of care involving frequent phone and occasional in-person contact with a health professional after discharge from hospital with spinal cord injury in Bangladesh. We hypothesised that the intervention would prevent serious complications and death in the first 2 years after discharge in this population.The CIVIC trial was a pragmatic, assessor-blinded, two-arm, parallel, randomised, superiority trial. The trial protocol and statistical analysis plan have been published , 17. ThePatients admitted to the Centre for the Rehabilitation of the Paralysed (CRP) with a recent spinal cord injury were eligible to participate in the trial if they were at least 15 years of age, were wheelchair-dependent on discharge, had sustained a traumatic or non-traumatic spinal cord injury in the preceding 2 years and provided written consent. The CRP provides specialised inpatient rehabilitation for over 400 people with recent spinal cord injury each year. It accepts patients with recent traumatic and non-traumatic injuries from across Bangladesh irrespective of income. It is the only specialised spinal cord injury centre in Bangladesh and one of the largest rehabilitation centres of its kind. From 12th July 2015, trial staff screened all people with spinal cord injury prior to discharge from hospital.Participants were randomised in permuted blocks stratified by level of lesion (tetraplegia or paraplegia). The randomisation schedule was concealed from potential participants, trial staff and all investigators, except an Australia-based investigator (RDH) who generated the allocation schedule and two India-based trial staff who dispensed allocations by email. Participants were approached and enrolled by trial staff but allocation was requested by the site coordinator (MSI). Neither the investigator nor the two trial staff had any involvement in recruitment of trial participants. Each eligible participant was randomised to either an Intervention group or a Control group.The nature of the intervention precluded blinding of trial participants and the healthcare professionals who administered the intervention. However, the assessors were blinded. To reduce potential for unblinding, assessors were not permitted to share office space or correspond with other trial staff. Assessors were naïve to the nature of the trial intervention and were trained separately to other trial staff. Trial staff did not share information about the trial with CRP staff or patients.The Intervention group received community-based care in addition to usual care. The Control group received only usual care.To deliver community-based care, healthcare professionals provided phone-based support to participants fortnightly in the first year and monthly in the second year following discharge from hospital. In addition, a healthcare professional visited each participant and the participant’s family in the home on three occasions: twice in the first year and once in the second year. The health professionals had backgrounds in nursing and physiotherapy.At each contact , participants were screened for pressure ulcers, urinary tract infection, faecal or urinary incontinence, depression, autonomic dysreflexia, and respiratory complications. Where available and appropriate, the camera and video facilities of smartphones were used to help monitor complications. If there was any evidence of a complication, the healthcare professional provided advice to the participant and the participant’s family about management of the complication and then more closely monitored the participant until the complication had resolved. Where necessary and possible, the healthcare professionals referred participants to local service providers . The adParticipants in the Control group received only usual care , American Spinal Injury Association Impairment Scale (AIS), total motor score, marital status, employment status prior to injury, monthly and family income prior to injury, and anticipated primary care giver post discharge.All outcomes were measured by blinded assessors 2 years after randomisation (there was a ±1-month window for these to be conducted). Initially, the blinded assessor phoned the participant and then travelled to the participant’s home to conduct the assessment. If, however, the assessor was informed at the initial phone contact that the participant had died, the assessor interviewed family members over phone. Some of the secondary outcomes were also assessed at baseline .The primary outcome was time to death from any cause. The date of death was obtained by asking family members.Secondary outcomes were burden of complications, prevalence of pressure ulcers, severity of pressure ulcers, depression, participation, quality of life, and independence. The secondary outcomes reflected the prevalence rather than incidence of complications. By measuring prevalence of secondary outcomes at baseline and 2 years rather than monitoring incidence of secondary outcomes over the 2-year period we avoided the need to contact participants in the Control group during the 2-year period. That was desirable because any contact between trial staff and Control group participants during the 2-year period could have caused contamination of the intervention. All questionnaires used to obtain self-reported outcomes were administered in Bangla under the guidance of the assessor.The burden of complications was measured using the Spinal Cord Injury Secondary Conditions Scale (SCI-SCS) . This isPressure ulcers were assessed using the Pressure Ulcer Scale for Healing version 3 (PUSH) , 24. TheDepression was assessed using the Bangla version of the Centre for Epidemiologic Studies Depression Scale revised version (CESD-R) , 26. TheParticipation was assessed using the Bangla version of the eight participation items of the World Health Organization Disability Assessment Schedule version 2 (WHODAS 2.0) .The partHealth-Related Quality of Life was self-assessed, with assistance from the assessor if needed, using the Bangla version of the Short Form Health Survey-12 (SF12) questionnaire , 29. TheIndependence was assessed using the self-report version of the Spinal Cord Independence Measure III (SCIM-SR). This is a 17-item test covering key aspects of independence. It rates self-care (four items), respiration and sphincter management (four items), and mobility (nine items) . The iteParticipants were also asked if they had got out of bed, got out of their homes, and engaged in paid work over the last week. These questions were only asked at the 2-year assessment. The three questions were self-administered with assistance from the assessor if needed. In addition, participants in both groups were asked how often they had been in contact with CRP staff since discharge from the CRP. Detailed cost data were also collected. Participants were asked at the 2-year assessment to estimate spinal cord injury-related out-of-pocket costs they incurred over the preceding 2 years. Cost data will be reported elsewhere .The healthcare professionals providing the intervention were physiotherapists with clinical experience in the management of spinal cord injury. They were provided with a written study manual and trained by the principal investigators and other professionals with extensive experience in the management of spinal cord injury in low- and middle-income countries. Refresher training was provided as needed. Day-to-day support was provided by the trial investigators based in Australia and other countries. Experienced trial monitors from George Clinical, India visited the CRP on eight occasions to audit compliance with the trial protocol and with the International Conference of Harmonisation Harmonised Tripartite Guideline for Good Clinical Practice. There was only one change to the protocol: 13 months after the first participant was randomised, the minimum age for participation in the trial was lowered from 18 years to 15 years to increase the rate of recruitment.α = 0.05) to detect an increase in survival from 83% to 93% with a two-tailed log rank test allowing for a single interim analysis and a worst-case 15% loss to follow up.The sample size was informed by our earlier study, which investigated 2-year survival after discharge from CRP in a cohort of 350 people with recent spinal cord injury . A samplData were analysed by statisticians from the George Institute for Global Health (including QL) using SAS Enterprise Guide version 7.1 (SAS/Stat version 9.4) and replicated by one of the investigators (RDH) using Stata v16. The analyses were first conducted using dummy-randomised data and then, after discrepancies between the two analyses had been resolved, on the data as randomised. An independent Data Monitoring Committee monitored unblinded outcomes and adverse event data according to a written charter and conducted a formal interim analysis when the first 214 participants had been followed up. The protocol provided an option to terminate the trial early if there were safety concerns but not on the basis of futility.Data were analysed on an intention to treat basis. All tests were two-sided tests with a critical probability of 5%. The primary analysis compared all-cause mortality in the Intervention and Control groups using the log-rank test. Sensitivity analyses were conducted using a Cox model adjusted for level of lesion, combined tests of restricted mean survival times with and without adjustment for level of lesion (tetraplegia or paraplegia) , and tesThe effects of intervention on secondary outcomes were estimated using linear models adjusted for level of lesion. For continuous outcomes, baseline scores were included in the model to increase precision and provide adjusted estimates. For binary outcomes, log-binomial regression was used to estimate the relative risk.Cox models with interaction terms were used to examine whether the effect of the intervention on survival was moderated by level of lesion (tetraplegia or paraplegia) or age .n = 206) or Intervention group  of 39 (38 to 40) phone calls (the target was 38) and 3.0 (3.0 to 3.0) home visits (the target was 3). The median duration of phone calls was 10 (IQR 9 to 11) min. Participants in the Intervention group reported similar levels of usual care  to participants in the Control group; the group received, on average, 2 (0 to 5) phone calls, 1 (0 to 1) home visit from CRP staff, and 1 (0 to 5) contact with other healthcare professionals. More details of the intervention and usual care are provided elsewhere .p value from the log rank test 0.85). None of the sensitivity analyses demonstrated clinically important or statistically significant effects on survival (Table p = 0.51) or age (p = 0.44). There were no statistically significant or clinically important differences between groups for any of the continuous secondary outcomes  participants from the Intervention group and 16/206 (7.8%) participants from the Control group had died. Figure es Table  or binarTo minimise potential for contamination, Control participants were not monitored over the 2-year period. Therefore, there are no data on serious adverse events in the Control group. In contrast, participants in the Intervention group were closely monitored. In this group there were 30 serious adverse events in 25 participants. Six participants developed a serious adverse event deemed life threatening and 19 participants required hospitalisation for 24 serious adverse events. The most common serious adverse events were pressure ulcers (10 serious adverse events in 10 participants) and urinary complications (10 serious adverse events in 8 participants). Causes of death were adjudicated using unblinded data after completion of the trial by two physicians (FB-S and IC) using all available documentation. The most frequent causes of the 31 deaths were pressure ulcers , suicide or refusal to eat or drink , and respiratory-related illness .These data suggest that a community-based model of care for people with spinal cord injury in Bangladesh did not prevent secondary complications and death in the 2 years after discharge from hospital. We interpret the data in this way because the incidence of deaths was nearly identical in the two groups. The confidence intervals about the primary estimates of effect are quite wide (hazard ratios of 0.46 to 1.89), but sensitivity analyses suggest that if there was any effect of the intervention on survival the effect was small. In particular, the confidence intervals about the ratios of 2-year restricted mean survival (0.98–1.04) and the increase in 2-year restricted mean survival (−0.4 to 0.8 months) suggest clinically important effects are unlikely. Moreover, it would be expected that any effect of intervention would have been mediated by a reduction in the incidence or severity of secondary complications, but there was clearly very little effect of intervention on these outcomes (Table n = 24), or who self-discharged from hospital before trial staff had an opportunity to invite them into the trial (n = 9), were more likely to die than trial participants. We followed up these 33 people, after obtaining ethical approval and consent to do so, and found that 12 (36.4%) had died within 2 years of discharge. If these people had been included in the trial the mortality rate across all participants would still have been low (9.7%). In other words, selective recruitment had little effect on the mortality rate. It appears likely, therefore, that mortality rates after discharge from the CRP have decreased since 2011. This could be because better health care is now available to people after hospital discharge, or because there has been a change in the case mix of patients admitted to CRP. A comparison of the baseline characteristics of participants in the earlier cohort study and the current trial suggests that the two cohorts were similar with respect to socioeconomic backgrounds and level of lesion. Regardless of the explanation for the reduction in mortality rates after discharge, the findings of the current trial still hold: the intervention did not prevent secondary complications or death in the first 2 years following discharge.The 2-year mortality observed in this trial (7.6%) was substantially lower than the 2-year mortality observed in putatively the same population of patients discharged in 2011 (19%) . One expWe had hypothesised that many of the complications people with spinal cord injury commonly develop could be managed at home with appropriate advice and support, and that regular contact with participants, even if only over the phone, would provide an effective way of identifying complications early so that the complications could be managed before becoming insurmountable. The trial findings refute that hypothesis. Similarly, two recent large trials conducted in India and China failed to demonstrate the effectiveness of community-based programs for people with stroke , 33. ThiThe failure of the intervention to reduce secondary complications and prevent death 2 years after discharge might indicate that the prevention and management strategies recommended by the health professionals were not effective, or that the strategies were not implemented well. Alternatively, it could be that strategies that would otherwise have been effective were ineffective in the current context, even though they were implemented well, because they were administered to people living in poverty with few resources, poor nutrition, and limited access to health care. To the extent that is true, effective long-term intervention for this population may require strengthening of economic and health systems. It is possible that the intervention may have been more effective if delivered by nurses or doctors. We did not employ doctors because of the greater cost. We tried to employ nurses but only one appropriately qualified nurse applied for the position (this person was employed as an assessor). We do not believe that failure to recruit nurses was a major limitation because the physiotherapists were comprehensively trained and became skilled at providing the intervention.Interestingly, even though healthcare costs for participants in the Intervention group were subsidised (maximum AUD80), this did not improve health outcomes. The small amount of financial assistance may however have gone some way to alleviating the financial strain experienced by participants and their families . There mIt is possible that the education participants received prior to discharge rendered the post-discharge support unnecessary. During the period of hospitalisation at the CRP, people with spinal cord injury and their carers were educated about prevention and management of secondary complications. Post-discharge phone-based care may be more effective in other contexts where less education is provided while in hospital.A limitation of this trial was the failure to verify the exact date of death of participants. That occured because Bangladesh does not have a death registry. Participants in the Intervention group were carefully monitored and the dates of deaths were accurately recorded by trial staff. However, there were no equivalent data for the participants in the Control group. To avoid a systematic bias, only dates of death collected by the blinded assessors at 2 years were used for the analyses. The blinded assessors asked families and community members of both groups to report dates of death. These dates may not always have been accurate. However, because the assessors were blinded, it is unlikely that any inaccuracies would have biased the trial’s findings. Another limitation was that cause of death was determined using information reported by families. The data suggest that pressure ulcers were a common cause of death although often it was not certain whether participants died with pressure ulcers or because of pressure ulcers.The finding that the intervention did not produce clinically important reductions in secondary complications or death 2 years after discharge was disappointing but vindicates the trial. More generally, this finding confirms the importance of assessing effectiveness of health interventions with randomised trials even when there is a strong expectation that the intervention is effective. There remains an urgent need to identify sustainable ways to reduce morbidity and mortality after discharge from hospital with spinal cord injury in low- and middle-income countries.Deidentified individual participant data and the accompanying codebook are provided in the Supplementary files.CIVIC data dictionaryCIVIC data file


Bradysia (Sciara) coprophila (Diptera: Sciaridae), which has 2 large germline-restricted “L” chromosomes. We sequenced and assembled the genome of B. coprophila and used differences in sequence coverage and k-mer frequency between somatic and germline tissues to identify GRC sequence and compare it to the other chromosomes in the genome. We found that the GRCs in B. coprophila are large, gene rich, and have many genes with divergent homologs on other chromosomes in the genome. We also found that 2 divergent GRCs exist in the population we sequenced. GRC genes are more similar in sequence to genes from another Dipteran family (Cecidomyiidae) than to homologous genes from Sciaridae. This unexpected finding suggests that these chromosomes likely arose in Sciaridae through hybridization with a related lineage. These results provide a foundation from which to answer many questions about the evolution of GRCs in Sciaridae, such as how this hybridization event resulted in GRCs and what features on these chromosomes cause them to be restricted to the germline.Germline-restricted DNA has evolved in diverse animal taxa and is found in several vertebrate clades, nematodes, and flies. In these lineages, either portions of chromosomes or entire chromosomes are eliminated from somatic cells early in development, restricting portions of the genome to the germline. Little is known about why germline-restricted DNA has evolved, especially in flies, in which 3 diverse families, Chironomidae, Cecidomyiidae, and Sciaridae, carry germline-restricted chromosomes (GRCs). We conducted a genomic analysis of GRCs in the fungus gnat  Germ-line restricted chromosomes are eliminated from all somatic tissues while being retained in the germline. This study of the evolutionary origin of such chromosomes in fungus gnats reveals that they are most similar to a fly of a different family, suggesting an ancient allopolyploidization origin for these peculiar chromosomes. Ascaris nematodes that fragments of chromosomes were eliminated from somatic cells early in development, showing that, in some cases, germline/soma differentiation involves changes in the genomic composition of cells as well as regulatory changes. In addition to the loss of chromosomal fragments (referred to as “chromatin diminution”), another type of germline specialization involves the elimination of whole chromosomes from somatic cells, a phenomenon identified in the Dipteran gnat Bradysia (Sciara) coprophila nearly a century ago [An underlying tenet of heredity is that all cells within an organism have the same genomic sequence. However, there are a surprising number of exceptions to this rule. For instance, Boveri  noted intury ago . Both chtury ago . ProgramMany examples of programmed DNA elimination involve regulated DNA elimination from somatic cells so that portions of the genome are restricted to the germline . GermlinIn species with chromosome elimination, entire chromosomes are exclusively found in the germline: GRCs. One hypothesis for the evolution of GRCs is that they originate from B chromosomes , which aGRCs are found in 3 Dipteran families: the “K” chromosomes of nonbiting midges (Chironomidae), the “E” chromosomes of gall gnats (Cecidomyiidae), and the “L” chromosomes of black winged fungus gnats (Sciaridae) ,21,22. EB. coprophila, all GRCs present  are transmitted to offspring regardless of parental origin [Fig 1). Furthermore, in Sciaridae, sex is determined by the number of X chromosomes eliminated from somatic cells in embryogenesis [Fig 1; see S1 Text for additional information).Although both Sciaridae and Cecidomyiidae carry GRCs, the characteristics of these chromosomes differ between the 2 families, with Sciaridae carrying few (up to 4) large GRCs, and Cecidomyiidae carrying many (between 16 and 67) small GRCs . l origin , and theogenesis . Sex chrThe hypothesis for the evolution of this unusual genetic system suggests that paternal genome elimination and X chromosome elimination as a means of sex determination evolved at the base of the Sciaridae . FollowiNasonia vitripennis. This chromosome, which is a selfish chromosome that affects sex determination in its host, was found to arise through a hybridization event with a Trichomalopsis wasp [We conduct the first genomic analysis of GRCs in Diptera, with the goal of exploring the origin, evolution, and composition of these chromosomes in Sciaridae. GRCs in Sciaridae are historically referred to as L chromosomes; however, we refer to them as GRCs in this paper to more easily facilitate comparison with GRCs in other lineages. We can imagine a few different hypotheses for the origins of GRCs in Sciaridae, following what is known about their distribution and the genetic systems in Sciaridae and Cecidomyiidae. First, as suggested by Haig , GRC evosis wasp .B. coprophila and were able to unambiguously identify GRC scaffolds in a single genome assembly generated from both tissue types by comparing scaffold coverage and k-mer distributions between the 2 sequence types (with the idea that GRC sequences will be present in the germline but not in the soma). We also use the recently published reference genome for B. coprophila [B. coprophila. We find that the 2 GRCs are gene rich and carry many homologs to the core genome. Contrary to Haig’s hypothesis [B. coprophila are often more closely related to genes in the core genome of Cecidomyiids  than Sciarids. A possible explanation for this unexpected result is that the GRCs in B. coprophila arose through a hybridization event between an ancient Sciarid and Cecidomyiid, with the GRCs containing genes originating from the Cecidomyiid ancestor. Our study raises questions about how this allopolyploidization may have occurred, why the GRCs were retained over time in Sciaridae, how they became restricted to the germline, and whether the GRCs do form a homologous chromosome pair. This study provides a foundation for the study of GRCs in Sciaridae, an understudied lineage with regards to GRCs, with great potential given the rich body of molecular and cytological research in Sciaridae [To disentangle the origins of GRCs in Sciaridae, we sequenced germline and somatic tissue from prophila , which wpothesis , we do nciaridae ,44,45. Fciaridae –13.B. coprophila is that male somatic and germline cells have a different chromosome constitution. They differ in the presence of GRCs, but also in the frequency of the X chromosome  . We used these differences in chromosome constitution to identify the GRCs in B. coprophila and to distinguish the X chromosome from autosomes. We sequenced adult male germline and somatic tissue and generated a genome assembly from both the germline and somatic sequence libraries . The genome assembly is of a comparable size to microspectrophotometry estimates for the genome of B. coprophila [Table 1).One consequence of the unconventional genetic system in prophila  . A k-mer score for each scaffold was then calculated as the number of mapped k-mer from the chromosome type with the most k-mers mapping to that scaffold divided by the length of the scaffold. We identified scaffolds that have a much higher coverage in germline tissue than somatic tissue (log2 germline/soma coverage ratio >0.5)  and a high proportion (k-mer score >0.8) of GRC-specific 27-mers mapping to the scaffold . We used a conservative approach, assigning GRC scaffolds only if both methods agreed on the assignment. Through this method, we were also able to identify regions that belonged to the X chromosome or autosomes. However, due to somatic contamination of the germline tissue sequencing library during the tissue dissection procedure, the coverage difference and k-mer frequencies of X chromosome and autosomal sequences were less pronounced between the somatic and germline tissue libraries than expected. Through both the coverage and k-mer assignment of chromosomes, we identified 162.4 Mb of autosomal sequence, 52.9 Mb of X chromosome sequence, and 154.1 Mb of GRC sequence (Table 1). The 28.2 Mb of sequence that we were unable to classify (Table 1) represent cases when the 2 methods (coverage and k-mer-based) did not agree on chromosome assignment (10Mb) or cases where the k-mer identification score was not high enough to classify the scaffold using this method (18.2 Mb) (see S3 Table). Overall, the low level of conflicting assignments indicates high agreement of the 2 approaches. Our chromosome sizes are comparable to microspectrophotometry estimates for B. coprophila [In order to identify GRCs in our genome assembly, we utilized coverage differences and differences in k-mer profiles between the somatic and germline tissue sequencing libraries. We used the k-mer profiles to extract GRC, X-linked, and autosomal k-mers that we mapped to the assembly . The number of genes that we annotated on the autosomes and X chromosome are comparable to the recently published reference genome for B. coprophila [Table 1), which did not contain GRC sequence.We annotated 41,418 genes in our prophila  (Table 1S2 Text for methods). This allowed us to increase the continuity of our assembly and to anchor genes within our assembly to known chromosomes  in the reference genome. Overall, we wanted to gain a better understanding using the number, chromosomal location, and divergence level of homologs in the genome, of the events which led to the current genomic composition of the GRCs. Additionally, we wanted to determine whether GRC-GRC reciprocal blast hits are prevalent in the genome assembly, which would suggest that we sequenced 2 distinct GRCs and explain why the size of the GRCs was larger than expected.To better understand the origins of the GRCs in Sciaridae, we conducted a reciprocal blast search with the amino acid sequence of all annotated genes with the goal of understanding whether the GRCs have homologs primarily on one chromosome in the genome (indicating their likely origin) and whether the divergence level of homologs can give us an idea of whether gene traffic has occurred from the core chromosomes to the GRCs after their formation. We also conducted a collinearity analysis to identify larger homologous blocks in the genome, in which we identified collinear blocks of 5 or more genes anchored to the reference assembly   or an asFig 2A). We did not identify a homolog for the remaining 7,105 genes; however, the majority of homologs that we identified were unexpectedly divergent (approximately 45% amino acid similarity) ; therefore, there is a chance that some of these genes do have extremely divergent homologs within the genome assembly which we were unable to identify with the cutoff values used in our homology analysis (min 40% amino acid identity). Of the homolog pairs we identified , 67% contained at least 1 GRC gene. We also identified homologs on all 3 autosomes and the X chromosome. The number of GRC-X chromosome homologs were slightly enriched compared to GRC-autosomal homologs given the number of genes on each of these chromosome types . However, the large number of homologs we identified on autosomes, and the fact that the divergence of the X-GRC homologs is similar to the divergence of A-GRC homologs , suggests that the enrichment of X-GRC homologs does not likely reflect that the GRCs are derived from the X chromosome. We explore the origin of the GRCs in more detail below.We looked for pairwise reciprocal blast hits in our genome assembly as well as identifying larger networks of genes that all had reciprocal blast hits to each other. From this analysis, we found that the GRC genes had many homologs located throughout the genome. We identified homologs in the genome for 55% (8707) of the 15,812 genes located on the GRCs, with 38.8% of these having exactly one homolog and the majority having multiple homologs in the genome . Overall, our results suggest that the GRCs are not derived from the X chromosome, or from any other single chromosome, nor from a simple chromosomal rearrangement . Intriguingly, our results also suggest that the homologs identified between the GRCs and core genome are older than we might expect if the GRCs evolved in the common ancestor of Sciaridae. We investigate the timing of evolution of the GRCs in more detail below.A collinearity analysis revealed 76 collinear blocks between the GRCs and autosomes, 13 collinear blocks between the GRCs and X chromosome, 23 collinear blocks in which both blocks were located on GRC scaffolds, and 5 collinear blocks in which both blocks were located on an autosome or the X chromosome. GRC-X chromosome collinear blocks made up a small proportion of blocks between the GRCs and core genome (14.6%) further suggesting that the GRCs are not likely derived from the X chromosome. We also anchored 42 blocks to individual chromosomes in the reference assembly and found that the GRCs are homologous to all 3 autosomes of Fig 3A). As the third peak is approximately the sum of the mean coverages of the first 2 peaks, this likely indicates that we did indeed assemble 2 distinct GRCs, and the 2 lower coverage peaks represent scaffolds present only on 1 GRC . Scaffolds in the highest coverage peak (approximately 55× coverage) are likely scaffolds present on both GRCs, although these represent a much smaller proportion of GRC sequence compared to scaffolds only located on 1 GRC.We wanted to explore the reason behind 2 unexpected observations: (1) that the size of sequence in our assembly attributed to the GRCs was much larger than the expected size of one GRC; and (2) that we identified a multitude of GRC-GRC homologs and GRC-GRC collinear blocks in our assembly. Therefore, we looked at the coverage of GRC scaffolds, to determine whether there was any evidence that we assembled 2 distinct GRCs. We analyzed the coverage of scaffolds assigned to the GRCs, the scaffold coverage of homologs in which both copies are on the GRCs, and the coverage of collinear blocks where both blocks are located on the GRCs. We found that the GRC scaffold coverage histogram has 3 peaks, 1 at 24.7×, 1 at 31.0×, and 1 at 55× . Using this method, we assigned 43 Mb of the assembly to GRC1 and 63Mb to GRC2. We then determined whether GRC-GRC homologs had 1 copy on each GRC. We found that, although for the majority of GRC-GRC homologs we were able to assign (74/128), one gene was on GRC1 and the other on GRC2 , there were also a substantial number of GRC-GRC homologs where both genes were on GRC1 (20/128) or GRC2 (34/128) , such that the enrichment of GRC1-GRC2 homologs was not statistically different from the assumption that homologs are randomly distributed in these 3 categories ). The majority of GRC1-GRC2 homologs observed is suggestive that the 2 GRCs do share homology, even on the scaffolds that assembled separately, but there are also many nonhomologous regions as many GRC homologs were only found on one GRC. In support of this idea, we also looked at whether collinear blocks in which both blocks were assigned to the GRCs had one block assigned to GRC1 and the other to GRC2 . We found that 7/13 GRC-GRC collinear blocks which we were able to assign to GRC1 or GRC2 were of the GRC1-GRC2 type, however, we also found 5 blocks in which both blocks were on GRC2 and 1 block in which both blocks were on GRC1 . Therefore, the 2 GRCs share some homologous regions, but also have some unique regions. However, due to the fragmented nature of our GRC assembly, we were unable to entirely resolve how the GRC scaffolds are physically placed, and future work is needed to determine where GRC1-GRC2 homologs are located on their respective chromosomes and where the GRC scaffolds at a higher coverage (55×) are physically placed on the GRCs.Next, we wanted to explore homology between the 2 GRCs by exploring whether the multitude of GRC-GRC homologs are an indication of distant homology between the 2 GRCs. To do this we assigned scaffolds to GRC1 or GRC2 by taking all GRC scaffolds with a coverage between 18× and 35× and a length greater than 5,000 bp and fitting 2 curves to this region of the histogram. We then assigned scaffolds to GRC1 or GRC2 by comparing the likelihood that they belong to one peak of the histogram versus the other (Sylvicola fuscatus) [S6 Fig). We examined the phylogenetic placement of all BUSCO genes identified in B. coprophila which we were able to assign to a specific chromosome and for which we were able to identify the BUSCO gene in at least 80% of Sciaroidea species in the analysis  . We first reconstructed a gene tree for every BUSCO ID and summarized the placement of genes located in the core genome and GRC genes.In order to better understand how old the GRCs are, we reconstructed the phylogenetic placement of GRC genes in Sciaroidea . We used a set of universal single-copy orthologs (BUSCO) identified in recently published draft genomes for 13 species within Sciaroidea and outgroup species (uscatus)  Fig 4A). We hypothesized that GRC genes would most likely have an intraspecific origin, where GRC genes were placed as the outgroup of the Sciaridae subtree . Another scenario is that the GRCs in Sciaridae and Cecidomyiidae share a common origin. Although it is generally considered that the GRCs in Sciaridae and Cecidomyiidae are independent [For GRC genes, we were interested in which hypothesis of GRC origin they supported . Surprisingly, we found that 454 of the 627 GRC BUSCO gene trees (72.4%) support the origin of GRCs through introgression. All the genes supporting this origin are placed within the Cecidomyiidae family; specifically, the GRCs are most closely related to the hessian fly Mayetiola destructor . This placement is not caused by biases in amino acid composition  and was supported by both bootstrap values and SH-like approximate likelihood ratio test score [Fig 4B) and is therefore unlikely a methodological artifact.As expected, BUSCO genes located on either an autosome or the X chromosome were nearly always located in the Sciaridae clade in phylogenies  The majority of the remaining genes  were monophyletic with all other Sciaridae genes. Of these, 74 genes had sequence data for all 3 Sciaridae species and allowed us to classify whether the GRC copy is an outgroup to other Sciaridae species or on an internal branch. Only 4 GRC genes  supported the hypothesis that GRCs arose at the base of Sciaridae . The vast majority  of GRC genes in Sciaridae were placed on internal branches, suggestive of ongoing gene traffic onto the GRCs.S7 Fig). The phylogenetic position of GRC sequences in B. coprophila is unexpected, but suggestive that the GRCs in B. coprophila might have arisen through an ancient hybridization event of a Sciaridae ancestor with a derived member of the Cecidomyiidae family. The support for the hybrid origin is surprising as the 2 families are extremely divergent, calling into question how the 2 genomes could be compatible [p-value < 0.0001; S10 Fig).We reconstructed 2 concatenated phylogenies with the 340 BUSCO genes with 1 copy on the GRC and 1 copy on either an autosome or the X chromosome , separating BUSCO IDs based on whether the GRC gene copy fell within the Cecidomyiidae clade or Sciaridae clade (mpatible . HoweverS6 Table) .We also examined whether the phylogenetic position of GRC genes (either in the Cecidomyiidae or Sciaridae clade) is associated with whether the gene is located on GRC1 or GRC2. We found that both GRC chromosomes contained genes placed in Cecidomyiidae clade in phylogenies  and some genes located in Sciaridae clade in phylogenies , suggesting that both chromosomes likely originated through the same means , followed by postacquisition gene traffic from the core genome. However, we found that GRC1 had more genes in the Cecidomyiidae clade in phylogenies compared to GRC2 . Furthermore, similar to the phylogenetic analysis, we found that there was a smaller subset of genes that had greater similarity to the B. coprophila homolog and that these homologs overall had higher similarity values (likely representing recent gene traffic from the core genome).In further support of the hybrid origin hypothesis, we conducted a reciprocal blast search between the amino acid sequences of all GRC genes and the official gene set for B. coprophila are more closely related to genes in the core genome of Cecidomyiids. However, there are still many questions remaining about how GRCs in Sciarids evolved, among them whether the GRCs arose from chromosomes in the core genome of a Cecidomyiid or from the GRCs. In the second case, the GRCs in both families would have a common origin. Although investigating the answer to this question would undoubtedly provide important insight into GRC evolution in Dipterans, we currently do not have GRC sequence data from any species within Cecidomyiidae, which would be needed to investigate this question. Such a dataset would be extremely useful to establish whether the GRCs in B. coprophila show greater similarity to the Cecidomyiid GRC genes or their autosomal counterparts.The results of this study raise many questions about how GRCs in Sciaroidea evolved (both in Cecidomyiidae and Sciaridae). Our study rejects the hypothesis that GRCs in Sciaridae arose from the X chromosome  and instNasonia [Trichomalopsis. However, this B chromosome is a lot smaller (9 Mbp) compared to GRCs and the 2 species that hybridized have a much more recent common ancestor approximately 2.6 mya [While our results suggest introgression of GRC genes between the 2 clades, exactly how this introgression occurred is rather puzzling. There are a few examples where interspecies crosses gave rise to additional chromosomes with non-mendelian inheritance, such as the paternal sex ratio (PSR) chromosome in the parasitic wasp Nasonia ,49. The  2.6 mya .S3 Text for details). This estimate gives us an approximate idea of when the GRCs introgressed into a Sciarid from a Cecidomyiid. We would like to note that this estimation is based on limited data and that there is also some debate as to the phylogenetic position of several of the Bibionomorpha families [Arabidopsis [We roughly calibrated our phylogenetic tree by dated amber fossils from Bibionomorpha and estimated that the introgression happened 114 to 50 mya and between 37 and 101 mya after split of the 2 ancestors of Sciaridae and Cecidomyiidae . Howevefamilies . Hybridifamilies  or Arabibidopsis ). None oM. destructor ; therefore, the hybridization event likely took place after differentiation of Cecidomyiidae. As GRCs and reproduction via paternal genome elimination is a common feature of the Cecidomyiidae family, the ancestor likely had both those features present during the hybridization event. The presence of the machinery for genome elimination in the M. destructor ancestor could have been the key for the allopolyploidization event to be successful. Perhaps, the Sciaridae subgenome retained its function as a core genome, while the Cecidomyiidae subgenome rearranged and became restricted to germline already in the first-generation hybrid, which might have facilitated overcoming reproductive barriers between the species. A previous phylogeny of 3 mitochondrial and 3 nuclear genome markers placed the 2 families far apart from each other with high confidence [B. coprophila through introgression from Cecidomyiidae, to our knowledge, the first example of cross-family allopolyploidization that led to sexually reproducing offspring.It is very unlikely the introgression would be a continuous hybridization process over such evolutionary distance. Therefore, the introgression probably took the form of a single hybridization event. The phylogenetic analysis placed nearly all the GRC genes in Cecidomyiidae closest to nfidence , indicatS6 Table), we can conjecture that both GRCs likely arose in the same hybridization event. However, it is unclear why 1 GRC contains a higher proportion of Sciaridae genes, which we hypothesize originated via post hybridization gene traffic from the core Sciaridae chromosomes. Perhaps larger blocks of genes translocated from the core chromosomes to GRC2 compared to GRC1. We were able to assign more sequence to GRC2 than GRC1, indicating that this chromosome is likely larger. Further work with long-read/linked-read GRCs data may provide clues as to why this is the case, by elucidating the physical location of genes with different origins.We also found that phylogenetic position of GRC genes was associated with which GRC  genes were located on, with GRC2 containing a larger number of genes that were located within the Sciaridae clade in phylogenies. Given that both GRCs had a substantial number of genes located in the Cecidomyiidae clade in phylogenies  the GRCs have become established in the germline by manipulating the mechanism of sex determination or (2) that the system of sex determination in Sciaridae arose through manipulating the mechanism by which GRCs are eliminated from somatic cells. However, we need to learn much more about the genetic underpinnings of sex determination in these clades, and to establish the timing of the evolution of different parts of the chromosome system in these families to establish how or whether GRC evolution and the evolution of the unusual sex determination mechanism in Sciaridae and Cecidomyiidae are related.B. coprophila [B. coprophila germline cells [It is interesting to consider how our results fit in with the larger body of cytological work on GRCs in Sciaridae. One unexpected finding is that there are 2 distinct GRCs in the pooled male germline tissue we sequenced. There are 2 possible reasons for this finding. This library was sequenced from a pooled sample of 95 male testes and as stated previously, all GRCs  present in male germline cells are transmitted to sperm in prophila . Therefone cells ; therefoB. coprophila, the GRCs form a bivalent in female meiosis, suggesting that the GRCs are homologous [B. coprophila, and how these chromosomes are transmitted, will help us understand more about whether the 2 GRCs are present in all males and in females, whether the 2 GRCs ever pair during meiosis, and if so which portions of the GRCs undergo recombination, as we would not expect that level of divergence between the GRCs if a significant portion of these chromosomes recombined.In mologous . AlthougWe do know that all GRCs in Sciaridae share one feature: restriction to the germline by elimination from somatic cells early in development. As we might expect that this feature is governed by a similar molecular mechanism in all Sciarids , sequencing GRCs from a larger diversity of Sciarids can help us answer questions about how this occurs, by looking at what genes/genomic features are common between different GRCs . In answering this question, the fact that the 2 GRCs we sequenced in this study were so divergent may come in handy, as there seems to be very few regions these chromosomes share. One other feature that Sciarid GRCs share is their unusual transmission patterns. In Sciaridae, all GRCs present in male germline cells are transmitted to offspring through sperm. Although this has only been established from a handful of species, these species are taxonomically distributed throughout the clade, suggesting that this may be a feature of all Sciarids ,26,31,57B. coprophila, it is less clear whether all of these genes are functional. The GRCs in Sciaridae seem to have evolved in an unusual manner, and we would hypothesize that the when the GRCs originated through allopolyploidization, many genes on the GRCs (formerly genes in Cecidomyiids) would initially have been under relaxed selection. It seems possible that due to the unique origins of these chromosomes, some of the genes located on them may not be functional, and further work characterizing the role of GRC genes in development and reproduction of B. coprophila would be extremely useful. This work, however, was beyond the scope of our study. There was historically some debate about whether GRCs in Sciarids provided any sort of function, as these chromosomes are predominantly heterochromatic, except during a few stages during development and reproduction [B. coprophila, and future work can now elucidate when and where their transcription occurs and determine whether these chromosomes are necessary in B. coprophila.An important caveat in this study is that although we have identified a multitude of genes located on the GRCs in oduction ,58,59. Soduction . HoweverBradysia impatiens, which lost the GRCs, also lost the ability to produce female offspring and thus later died out as a line completely [Several recent studies have sequenced germline-restricted DNA in different species that represent independent origins of this phenomenon. These studies, like ours, have generally found that germline-restricted DNA is more gene rich than expected ,12,13,60mpletely . AlthougB. coprophila, in which it seems that most of the GRC genes originated in one time period (when the hybridization occurred), followed by some gene transfer to the GRCs more recently . However, in both lineages it appears that there is some transfer of genes onto the GRCs over time from the core genome, perhaps reflecting that these chromosomes are acquiring genes related to germline function/reproduction over time [Songbirds are the only other lineage with GRCs in which the content of these chromosomes have been studied in detail ,13,20,61ver time . For SciB. coprophila has a fascinating chromosome inheritance system, which displays several examples of non-mendelian transmission and contains 2 GRCs. Understanding more about how this system evolved can tell us about the evolution of alternative non-mendelian reproduction systems as well as about the evolution of GRCs and germline soma differentiation. Through sequencing the GRCs in the Sciarid B. coprophila, we have determined there are 2 distinct GRCs in this species that contain many protein coding genes. Perhaps the most intriguing finding from this study is that the GRCs in B. coprophila seem to have evolved through an ancient hybridization between a Cecidomyiid and a Sciarid. Future research is needed to establish exactly how this occurred, and whether it involved the core genome chromosomes of a Cecidomyiid, or the GRCs in this lineage, but this finding is the first example, to our knowledge, of a hybridization event between animals so divergent from each other that has had lasting evolutionary consequences. We speculate that the fact that the GRCs in B. coprophila are restricted to the germline (and these were the chromosomes which have a Cecidomyiid origin) is likely an important component to the longevity of this system, as otherwise we might expect negative effects due to chromosome pleiotropy in somatic cells.B. coprophila evolved not as a means to resolve germline/soma conflict, but instead through allopolyploidization. The GRC in zebra finches, as well, may not have evolved as a means to resolve germline/soma conflict, but instead from a selfish B chromosome [B. coprophila, and how and whether they have a germline related function, will provide additional insight into how different types of germline-restricted DNA are related, and whether GRCs in B. coprophila provide a similar function to other lineages with GRCs.Finally, our results add additional insight into the evolution of germline-restricted DNA. Studies on germline-restricted DNA in taxa with chromatin diminution  often suggest that this system evolves to resolve germline/soma conflict over gene expression ,62. Howeromosome . Investiromosome ,12,13. UB. coprophila lines used in this study have been maintained in the laboratory since the 1920s [Sciara coprophila, although the genus name was changed from Sciara to Bradysia some decades ago [B. coprophila, but Sciara tilicola , Sciara amoena  Sciara coprophila , and Bradysia tilicola are all synonyms. Our B. coprophila cultures were obtained from the Sciara stock center at Brown University and kept at the University of Edinburgh since October 2017. We maintain colonies by transferring 1 female and 2 males to a glass vial (25 mm diameter × 95 mm) with bacteriological agar and allowing the offspring of the female to develop. During development, we add a mixture of mushroom powder, spinach powder, wheat straw powder and yeast to the vials 2 to 3 times a week until the larvae pupate.he 1920s . Most ofades ago . We refeWe sequenced genomic DNA from somatic (heads) and germline (testes and sperm) tissue of 1- to 2-day-old adult males. We dissected males which had been put on ice in a vial  on a clean slide in a dish of ice under a dissecting scope. For the dissections, we used jewelers forceps to separate the head from the body and then placed the head in a 1.5-ml microcentrifuge vial on dry ice. We then placed a drop of sterile 1X PBS on the body of the male and used forceps and insect pins to slowly pull the claspers away from the body until the claspers and male reproductive tissue separated from the body. We severed the ejaculatory duct and placed the testes in a separate microcentrifuge tube. We collected males over several days and stored the samples at −80°C until DNA extractions, sequencing a pooled sample from the tissue from 95 males.S2 Text for full protocol). We quantified DNA on a qubit fluorometer (v3) and sequenced the samples on the Illumina Novaseq S1 platform, generating PE data with 150 bp reads and 350 bp inserts through Edinburgh Genomics .The DNA extraction protocol we used was a modified version of the DNeasy Blood and Tissue Kit  extraction procedure (see S2 Table). We also generated a genome assembly from long-read (PacBio Sequel 3.0) sequence data from germline tissue, but the short-read assembly produced a more complete genome assembly according to BUSCO gene assessments, so this assembly was used for gene annotation. We used the long-read assembly for the collinearity analysis to increase the continuity of GRC scaffolds (see S2 Text for details).We generated a genome assembly with both the somatic and germline tissue short-read libraries (https://www.bioinformatics.babraham.ac.uk/projects/fastqc). We generated an initial assembly with CLC assembly cell using default settings (Qiagen- v 5.0.0), then used blobtools [S1 Fig for blobplot), using bamfilter to retain reads which had a GC content between 0.14 and 0.51 and a coverage higher than 7 (which excluded Prokaryotic sequences identified as contaminants). We generated an assembly with spades [S2 Text for details) to the genome using Hisat2  [For the short-read libraries, we trimmed the raw reads with fastp with parameters —cut_by_quality5 —cut_by_quality3 —cut_window_size 4 —cut_mean_quality 20  and usedlobtools ,66 to inh spades  using thh spades  using thh spades , alignin v2.1.0) , and usi v2.1.0)  and Repe v2.1.0)  with theFig 1B), we expect autosomal scaffolds to have a log2 coverage ratio (germline/soma) of approximately −1 , X-linked scaffolds to have a coverage difference of approximately 1, and GRC scaffolds to have very few reads mapping to them from the somatic library but a diploid coverage level in the germline tissue library . We mapped the germline and somatic reads to the genome assembly with bwa mem (v0.7.17) using default settings, counted the number of reads from each library mapping to each scaffold, and computed the log2 ratio of germline read counts to soma read counts (+1 to ensure a noninfinite coverage ratio) [Fig 1C).We used a combination of 2 techniques to identify scaffolds belonging to the GRCs in our assembly. One technique employs coverage differences between the germline and somatic tissues to identify which chromosome a scaffold belongs to. Since the number and type of chromosomes differs between the somatic and germline tissue (e ratio) . Due to Fig 1D). We manually inspected the 2D histogram and used the expected differences in the chromosome frequency in each tissue to assign k-mers with a frequency between 125 and 175 in the somatic library and between 80 and 140 in the germline library as autosomal, k-mers with a frequency between 50 and 100 in the somatic library and 60 and 100 in the germline library as X-linked, and k-mers with a frequency <5 in the somatic library and >10 in the germline library as belonging to the GRCs. We used a custom script to extract 27-mers and their coverages using kmc dump [S2 Fig for plots showing k-mer identification cores for each chromosome type). Scaffolds with a majority of autosomal k-mers mapping to them and a k-mer identification score greater than 0.4 were assigned as autosomal. Similarly X-linked scaffolds with a k-mer identification score greater than 0.4 were assigned as X-linked, and GRC scaffolds with a k-mer identification score greater than 0.8 were assigned as belonging to the GRCs . We then compared the scaffolds assigned using the k-mer and coverage techniques. Only scaffolds that were assigned as the same chromosome type with both techniques were included in downstream analyses.The second technique we used to assign scaffolds to chromosomes utilizes differences in the frequency of k-mers in the trimmed sequencing reads of each library. We used the kat comp command (kat v 2.4.1)  to generkmc dump . We had Fig 2C). First, we extracted transcripts for each gene with gffread (v0.11.7) [S2 Text), and autosomal and X linked genes to the reference assembly (NCBI accession: GCA_014529535.1 [S4 Table) [Fig 2C using SynVisio   and used529535.1 ) using b529535.1 . Note th4 Table) . The syn4a4361f, ).In order to explore why the size of the GRCs in our assembly was larger than we expected and why we observed so many GRC-GRC homologs, we explored the coverage of GRC scaffolds in the assembly. To do this, we used samtools to calculate the depth of each scaffold in the assembly  and extrB. coprophila genome assembly (v2.26.0) [S5 Fig).We then looked at whether the GRC-GRC homologs and collinear blocks had one half on GRC1 and the other on GRC2, as we would expect if the 2 GRCs are largely homologous (but divergent enough to assemble separately). As we only assigned scaffolds to GRC1 or GRC2 if they met certain scaffold length/coverage criteria (see above), we assigned 128 GRC-GRC homologs and 13 GRC-GRC collinear blocks to GRC1 and GRC2. For the collinear blocks, we only made GRC1/GRC2 assignments on blocks in which we were able to assign more than 3 genes in the block to GRC1 or GRC2. We also examined the coverage of genes along each GRC-GRC collinear blocks using BEDtools coverage with settings -mean -a to compute the mean coverage across each annotated gene within the v2.26.0)  M. destructor, which we obtained from NCBI (accession: GCA_000149195.1). We conducted a BUSCO analysis (version 4.0.2) [B. coprophila assembly, to identify universal single-copy orthologs in each genome. We excluded the Exechia fusca genome from further analyses as this genome had a low proportion of complete BUSCO genes identified, indicating that the genome was likely of poor quality. We identified the chromosomal locations of each BUSCO gene identified in the B. coprophila assembly (S5 Table). We took the amino acid sequence of the BUSCO genes for B. coprophila  and the longest amino acid sequence for each BUSCO ID per species as the gene sequence in the genome assemblies from all other species . We only retained BUSCO IDs in the analysis in which 80% of the species of interest had complete versions of the gene and for which we were able to determine the chromosomal location of all B. coprophila genes. This left 1,184 BUSCO IDs in our phylogenomic analysis.We utilized draft genome assemblies for 14 Sciaroidea species and 2 species outside the Sciaroidea, most of which we obtained from Anderson and colleagues  with then 4.0.2)  using thS. fuscatus or if that species was absent Penthetria funebris as the outgroup, according to recent phylogenetic studies [B. coprophila genes on different chromosomes depending on the ID) allowing IQtree to select the most appropriate substitution model and computing ultrafast bootstrap and SH-aLRT statistics for each node with 1,000 replicates [B. coprophila genes in all phylogenies using a custom script which summarized whether B. coprophila gene copies fell within the Sciaridae clade or Cecidomyiidae clade, as these were the 2 most common topologies. We also extracted information about the bootstrap value of the nearest node and branch length for B. coprophila genes in each phylogeny. For GRC genes that were placed in the Sciaridae clade, we also analyzed first whether gene sequences for all Sciaridae species were present in the gene tree, and second whether the GRC gene formed an outgroup to all other Sciaridae species  or whether the GRC gene was placed within the Sciaridae clade (indicating that it likely evolved through translocation from a core chromosome). As the placement of GRC genes in the Cecidomyiidae clade was unexpected given the evolutionary distance between these 2 clades, we also examined whether amino acid composition bias may have led to the GRC genes being placed in the Cecidomyiidae clade . Therefore, we also generated a heatmap of the amino acid composition of all BUSCO genes that went into the phylogenetic analysis for all species.We aligned the amino acid sequences with MAFFT using the L-INS-i method , setting studies ,37. We rplicates –84. We mB. coprophila, one on the GRCs and one in the core genome . We separated the BUSCO IDs based on the phylogenetic placement of the GRC gene , then reconstructed maximum likelihood phylogenies for both sets of genes separately in IQtree with 1,000 ultrafast bootstrap and SH-aLRT replicates [We constructed concatenated phylogenies summarizing the most common position of GRC genes in phylogenetic analyses for the 340 BUSCO IDs for which we identified 2 genes in plicates –84. For M. destructor homologs or homologs within the B. coprophila genome but on an autosome or the X chromosome. We therefore downloaded the amino acid sequences of the M. destructor official gene set from i5K (OGS1.0) and appended the amino acid sequences of the annotated GRC genes to this file. We then conducted a reciprocal blast search with the same parameters as we used for the homolog identification within the B. coprophila genome, filtering the reciprocal blast hits in the same way. We used a custom R script to extract GRC genes that had reciprocal blast hits in both the M. destructor and B. coprophila core genome. As some of these genes had more than one hit in one or the other genome, we took the reciprocal hit with the greatest identity within the genome to be the best blast hit for that genome. We then compared the similarity of reciprocal blast hit in the M. destructor genome to the similarity of the reciprocal blast hit for the same GRC gene in the B. coprophila core genome.Given the phylogenetic position of many GRC genes, we wanted to explore whether GRC genes share greater similarity with S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S1 Tablehttps://github.com/RossLab/Bradysia-GRCs and at 10.5281/zenodo.5884857. All data to generate the figures within this manuscript are located at https://github.com/RossLab/Bradysia-GRCs/blob/master/tables/figure_data.tar.gz. The name of the data used to generate each figure is listed in this table.Scripts and analysis documentation describing how we generated figures/tables is available at (PDF)Click here for additional data file.S2 TableThe short-read assembly was used for gene prediction as it was closer to the expected genome size compared to the long-read assembly and also was more complete according to BUSCO assessment.(PDF)Click here for additional data file.S3 TableFig 2A) was too low to support a confident assignment with this method.Chromosome assignment using the k-mer method is shown in the first column and the assignment with the coverage method is shown in the second column with the total size of unclassified sequence belonging to that category shown in the third column. In the k-mer assignment column, a “c” indicates cases where the majority k-mers from one chromosome mapped to that scaffold but the k-mer identification score (see (PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 TableS7 Fig) and concatenated phylogenies were generated for categories indicated with * . BUSCO categories including unassigned genes (NA assignment) are excluded from this table.BUSCO assessment was conducted with the insecta_odb10 database. The genomic location of genes was identified with both coverage and k-mer identification techniques. Individual gene trees were examined for all categories of BUSCOs listed below ((PDF)Click here for additional data file.S6 Tablep < 0.0001). However, both GRC chromosomes contain some genes that are within the Cecidomyiidae clade in phylogenies. Numbers in parenthesis indicate genes that are likely present on that chromosome, but which we were unable to unambiguously classify to a specific GRC . GRC, germline-restricted chromosome.Only GRC genes that fall within the Cecidomyiidae or Sciaridae clade are shown and only genes that have a scaffold coverage ranging from 18× to 35×. More genes on GRC1 fall within the Cecidomyiidae clade compared to genes on GRC2 Click here for additional data file.S1 FigS1 Table.Reads mapping to scaffolds with a GC content between 0.14 and 0.51 and a coverage higher than 7 were retained for the final assembly. Location of data used to generate this figure is specified in (PDF)Click here for additional data file.S2 Fig(A) Histogram of k-mer assignment scores for scaffolds of each chromosome type in the short-read assembly used throughout the manuscript. The score is defined as the number of k-mers with an exact match to the scaffold from the chromosome type with the majority of k-mers matching the scaffold, divided by the scaffold length. For GRC scaffolds (orange), we only assigned scaffolds with a score higher than 0.8 as GRC scaffolds, while for autosomal and X chromosome scaffolds  we assigned scaffolds with a score higher than 0.4, as the GRC scaffolds had a more distinct k-mer profile  than autosomes and the X chromosome. (B) Histogram of k-mer assignment scores in the long-read assembly (see S2 Text). The scores are substantially lower, especially for differentiating autosomes and the X chromosome. This assembly was used only for anchoring GRC genes in longer blocks for the collinearity analysis. Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.(PDF)Click here for additional data file.S3 Fig(A) showing autosomal-autosomal homologs, (B) showing GRC-autosomal homologs, (C) showing GRC-GRC homologs, (D) showing GRC-X chromosome homologs, (E) showing autosomal-X chromosome homologs, and (F) showing X chromosome-X chromosome homologs. We set a threshold of 40% identity and genes covering at least 60% of each other to assign reciprocal blast hits, so we would to capture only hits that spanned most of the length of each gene. Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.Plots are separated by the location of genes involved in the blast hit, with (PDF)Click here for additional data file.S4 FigS1 Table. GRC, germline-restricted chromosome.Scaffolds longer than 5,000 bp, between 18× and 35× coverage and assigned to the GRCs were used to generate a density plot from which 2 normal curves were drawn . We assigned each scaffold to GRC1 or GRC2 by taking the scaffold coverage and determining whether it was more likely to belong to GRC1 or GRC2. Location of data used to generate this figure is specified in (PDF)Click here for additional data file.S5 Fig(A) Barplot showing the number of the GRC-GRC collinear blocks we were able to assign to GRC1/GRC2. Blocks in which all but one gene in the same block were assigned to the same GRC are denoted with a c . We were unable to assign several blocks either because one block did not have a consistent GRC assignment or because one block did not have enough genes on it which were assigned to one of the GRCs . (B–D) Examples of gene coverages along a GRC1-GRC2 collinear block (B), a GRC1-GRC1 collinear block (C) and a GRC2-GRC2 collinear block (D). Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.(PDF)Click here for additional data file.S6 FigExechia fusca was excluded from analyses as the proportion of complete BUSCOs was low (54%). In B. coprophila, 39.2% of the insect BUSCO genes were duplicated. Location of data used to generate this figure is specified in S1 Table.(PDF)Click here for additional data file.S7 FigFig 4B and 4C shows a summary of all GRC BUSCO genes and all core genome BUSCO genes, respectively. Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.For each BUSCO category, the barplot summarizes how many genes fall within the Sciaridae clade , Cecidomyiidae clade (purple), or other (gray), the upper right boxplot shows the bootstrap values of the closest node for each gene within that category, and the bottom right boxplot shows the branch lengths for each gene within that category. Some categories are shown twice if they contain both GRC and core genes as these gene types were plotted separately. (PDF)Click here for additional data file.S8 FigSciara_coprophilla), clustered together with other Sciaridae genomes. The only Sciara genes with deviated aa compositions were those we were unable to classify (NA-Sciara_coprophila), which might be related to the same problems we experienced when we attempted to assign chromosomes (difficult structure). Location of data used to generate this figure is specified in S1 Table. Note: Sciara_coprophila is a synonym for B. coprophila. GRC, germline-restricted chromosome.Amino acid composition analysis did not show any potential bias that could cause long-branch attraction. For the genes used for the phylogenetic analysis we calculated the amino acid composition and used a heatmap to visualize the relative frequencies of individual amino acids. Overall, there is not much variation among the analyzed species. Furthermore, the composition of GRC genes (L-(PDF)Click here for additional data file.S9 Fig(A) With 1 GRC copy rooted in Cecidomyiidae, (B) with 1 GRC copy rooted in Sciaridae, (C) with 2 GRC copies both in Cecidomyiidae, (E) with 2 GRC copies, 1 in Cecidomyiidae and the other in Sciaridae, (D) with 2 GRC copies both in Sciaridae. (F) GRCs unplaced (without significant nodes) or branching with a species from any other family. Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.(PDF)Click here for additional data file.S10 Figp-value < 0.0001) suggesting the genes on GRCs found within Sciaridae might be due to gene duplications and translocations within Sciaridae after the GRCs were acquired. Location of data used to generate figure is specified in S1 Table. GRC, germline-restricted chromosome.Branch length distribution of GRC copies of BUSCO genes plotted with respect to the phylogenetic position (at family level), means shown by dashed lines. Branch lengths of BUSCO genes on GRCs within Cecidomyiidae (violet) are significantly longer than branches found within Sciaridae Click here for additional data file.S11 Fig(A) Scatterplot of the amino acid identity for GRC genes that had a reciprocal blast hit to both the B. coprophila core genome (x-axis) and the M. destructor core genome (y-axis). The majority of genes had a greater similarity to the M. destructor genome. (B) For genes that had a greater similarity to the B. coprophila genome , a histogram of the homolog identity to the B. coprophila core genome gene, with (C) showing a histogram of the homolog identity to the M. destructor core genome gene for genes that had a greater similarity to the M. destructor genome (purple background in A). (D) Histogram showing the number of reciprocal blast hits for GRC genes to the B. coprophila core genome, the number of unique hits , the number of reciprocal blast hits for GRC genes to the M. destructor core genome, the number of unique hits, and the number of GRC genes which had a reciprocal blast hit in both the M. destructor and B. coprophila genome. The genes in the last category were used for plots A,B, and C. Note that comparisons of reciprocal blast hits between these 2 genomes should be taken with a grain of salt, as the M. destructor genome was not annotated in the exact same way as we annotated the B. coprophila genome. However, we would not expect this to substantially affect the patterns of homology shown above. Location of data used to generate this figure is specified in S1 Table. GRC, germline-restricted chromosome.(PDF)Click here for additional data file.


Proponents argue that polygenic risk score (PRS) testing, based on panels of risk SNPs, will revolutionize the prevention and early detection of cancer through individualised risk management strategies and streamlining of the current ‘one-size-fits all’ population screening programs4. Such a model is highly seductive for the rationalisation of healthcare provision. UK government enthusiasm for PRSs is well demonstrated within the recent Genome UK report and 2020 update to the Life Sciences Strategy5. Indeed, reflecting governmental endorsement of predictive genomics, the UK government’s Secretary of State for Health and Social Care, Matthew Hancock, rather questionably enthused that his recent PRS-derived lifetime prostate cancer risk estimate of 15% (compared to a prior of 13%) “may have saved his life”7. To establish the requisite governance and data infrastructure for population-level genomic profiling, national projects such as the 100,000 Genomes Project  and the Accelerating Detection of Disease programme (up to 5 million volunteers) were initiated9. Following initial discontinuation by the U.S. Food and Drug Administration of the 23andMe PRS service11, there has been a resurgence within the direct-to-consumer genomics market of PRS predictions for many diseases. While the value of additional biomarkers to improve the targeting of measures for cancer prevention and early detection is indisputable, for PRS to be clinically useful, two assertions must be proven correct. The first assertion is that PRSs provides sufficient risk discrimination. The second is that this risk discrimination is meaningful in the context of absolute risk of that cancer and applicable in the context of respective tools available for prevention and early detection.Genome-wide association studies (GWAS) have identified associations between common genetic variants, single nucleotide polymorphisms (SNPs) and the risk of developing different cancers13. Two commonly presented measures of discrimination derived from these distributions are: (i) comparison of the RR for those at the top and bottom tails of the PRS distribution (ii) comparison for specified PRS cut-offs of the proportion of affected individuals with ‘positive’ PRS (‘detection rate’) versus the proportion of unaffected individuals falling within the same PRS score range )  to the mean. Up- or down-modification by these PRSs against baseline cancer risk results in minimal absolute difference in risk. Acknowledgement that the PRS for a chosen disease provides useful information for only a few percent of people is often countered by the argument that PRSs can be generated for dozens of cancer types, such that each individual is likely to be in an ‘extreme’ tail of PRS for at least one cancer type. However, in practice, clinical decision-making is driven by absolute risk, rather than relative risk per se. Hence, for all but the most common cancers, PRS reveals absolute increase in cancer risk against baseline that is miniscule even at the extreme upper tail of PRS. For example, for those in the respective top 5% of PRS, lifetime risk for breast cancer is elevated from 11.8 to 19.0% (1.6-fold), lifetime risk of prostate cancer is elevated from 12.7 to 22.2% (1.75-fold) and lifetime risk of colorectal cancer is elevated from 4.6 to 6.9% (1.5-fold). For less common cancers, the absolute risk increase is much lower: for women in the respective top 5% of ovarian cancer PRS lifetime risk is elevated 1.3-fold (from 1.6 to 2.1%)16. As an example, digital mammography for breast cancer screening has an AUC of 0.7817. PRSs constructed from cancer SNP sets derived from GWAS yield only AUCs ranging from 0.53  to 0.67 (prostate cancer)  curve, with the probability that a randomly selected case having a higher PRS than a randomly selected control being quantified by the area under the ROC curve (AUC). An AUC of 0.5 reflects a testing tool with no discrimination. While the threshold for clinical discrimination clearly depends on the benefit-risk profile of the proposed intervention, broadly speaking AUC values of 0.7–0.8 are considered as acceptable and >0.8 as affording good discriminationer) Fig. .Fig. 2Re−8 for declaring a SNP association guards against type 1 error but inevitably has limited the number of SNPs included in PRS testing panels. Methodologies such as LDpred apply a Bayesian genome-wide genetic risk prediction to incorporate SNP-correlations “tagged” by current arrays irrespective of association P-value18. However, to the extent that such methods have been explored, the discriminatory power for these ‘expanded’ PRSs is at best only very modestly improved20. The (likely) final generation of GWASs for common cancers such as breast and colorectal cancers is currently underway. Despite aggregating the’world-wide’ resource of available samples, power remains prohibitive and these experiments are unlikely to harvest more than 80% of the heritable risk for these cancers3. Even disregarding this and calculating PRS based on the full disease heritability, i.e. the hypothetical situation of identifying the full complement of risk SNPs, the AUCs for the common cancers remain disappointingly modest (0.64–0.73)3.The GWAS so far performed have only identified SNPs contributing 10–30% of common variant heritability for their respective cancers. It is widely asserted that discriminatory value of PRS will improve dramatically ‘in the future’, once additional rafts of new disease-associated SNPs are delivered. Imposing a threshold of 5 × 1023; some authors erroneously inflate their AUC by combining the two as if orthogonal25. For most cancer types, the AUC for non-genetic factors is modest27. When modelled, the well-established modifiable risk factors have additive effects with SNP associations and therefore only modestly improve the AUC28. Breast cancer has the best characterised set of non-genetic risk factors: the AUC for these risk factors is 0.637 while the AUC of the current PRS is 0.631; and in combination the AUC is 0.68329. Again, while aetiological epidemiological research continues to be a dynamic field, it would seem naïve to predict imminent discovery of robustly associated new non-genetic risk factors of sizeable effect.Another argument pertains to the boost in predictive value of PRS when applied in combination with non-genetic risk factors. Family history can be an important risk factor for cancer but is correlated with PRS31. From modelled adaptation of the UK breast cancer screening programme such that screening of women age 35–79 would be offered on the basis of PRS rather than age alone, it has been predicted that screening could be reduced by 24% at the cost of reduction in screen-detectable cases of 14%32. Although this indicates potential for more efficient targeting of screening, there is concern that: (i) genomic profiling will do little to improve and could even reduce uptake of existing cancer screening programmes, which for breast cancer in the UK is currently only 69%33, and (ii) the incidence of breast cancers in those from whom breast screening has been ‘withheld’ (or in fact ‘withdrawn’) would be perceived by the public as too high. Another proposal is that PRSs could be combined as a Bayesian Prior to a population screening test, such that the more expensive and/or invasive confirmatory test is only triggered when a positive screening test  arises in individuals with high PRS. In practice, where the screening test itself has good performance characteristics, for example Faecal Immunochemical Test (FIT) for detection of colorectal cancer, the negative predictive value of PRSs will be insufficient to exclude disease in those with a positive screening test. Conversely, where the performance of the screening test is poorer (e.g. prostate specific antigen (PSA) for prostate cancer), the limited positive predictive value of the PRS adds little, resulting in a large group of PRS-false-positives receiving an invasive, confirmatory investigation. Hence, where screening is effective, inexpensive and safe, the limited additional boost in detection added by PRS stratification is arguably likely to be outweighed by the cost, complexity of delivery of population risk-profiling with the potential for reduced participation. Well-designed trials are therefore essential to avoid inadvertent disruption of existing screening programmes with documented evidence of benefit34. Where screening is not effective, inexpensive, or safe, the addition of a PRS is unlikely to make it so.There are many who acknowledge PRS to be a weak predictor of individual cancer risk but who suggest that it could still be useful to individualise population screening programmes, for example by excluding ‘low risk’ individuals from screening35. While an attractive proposition, few agents are currently licenced for this purpose and clinical studies of new agents for chemoprevention are challenging. The risk-benefit profile of an agent dictates the level of disease risk at which administration is justified; thus the value of PRS stratification in determining administration would be very much contingent on the performance of PRS in discriminating those at sufficiently elevated risk. Administration of chemopreventative agents based on PRS-stratified groups has not yet been trialled for any agents and would require careful consideration37. While communicating DNA-based disease risk assessments may have a role in promoting risk-reducing behaviour38, evidence for this is currently lacking and there is a potential to introduce harm, particularly in the absence of counselling and clinical utility39. Furthermore, if PRS is to be used, it has to be universally applicable to all in the population regardless of ancestry to ensure equity in provision of healthcare resource. Presently the majority of PRSs are based on studies of European ancestry40 and their performance is poor in non-European populations41. This consideration is frequently offered as a minor technical ‘footnote’ to the value proposition of PRSs: given the limited predictive capabilities of existing PRS generated from decades of massive studies of available samples, it is unclear how this deficit will actually be addressed in the foreseeable future.PRS testing has been suggested as a method of identifying individuals who may benefit from cancer chemopreventative agentsBRCA1 and MLH1 is not under dispute: such mutations provide effective risk discrimination and there are established clinical pathways for those in whom mutations are identified. However, the notion that PRSs will offer equivalent utility population-wide by providing informative risk stratification across multiple diseases is misleading. Raising unrealistic expectations and implementing programmes without careful evaluation risks compromising the application of PRSs for specific niches, and indeed, of genomic medicine as a whole.The clinical utility of identification of high-impact mutations in genes such as Supplementary Information