Y-linked additive variation for quantitative traits in Drosophila simulans

Y-linked additive variation for quantitative traits in Drosophila simulans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Y-linked additive variation for quantitative traits in Drosophila simulans Kenneth Fedorka, Tobias Nielsen, Jaden Baldwin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1879119/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Drosophila Y-chromosome variation has been shown to influence numerous phenotypes and the expression of hundreds of genes throughout the genome. However, it is currently unknown if Y-chromosome variation can adaptively shape male traits. Previous theoretical work suggests additive variation necessary for adaptive evolution is difficult to maintain among Y-chromosomes within populations, and previous empirical work has revealed only Y-linked epistatic variation, which can impede adaptive evolution. To assess the impact this Y-linked variation may have on adaptive evolution, we established replicate populations in D. simulans containing either multiple Y-chromosome variants (YN populations) or a single Y-chromosome variant (Y1 populations) and estimated male and female heritabilities for sternopleural bristles number, abdominal bristles number, sex comb teeth number, and tibia length; traits previously shown to be influenced by Y-chromosomes. If Y-chromosome variation is additive, we expected YN populations to exhibit greater heritability than Y1. If variation is largely sign epistatic, we expected YN populations to exhibit reduced heritability. To minimize genetic dissimilarities between YN and Y1 populations, autosomal, X-linked, and mitochondrial allele frequencies were homogenized within YN/Y1 replicate pairs. Female heritability estimates served as controls and not expected to differ. We found that male YN populations exhibited greater heritability than Y1 for sternopleural bristle and sex comb tooth number, while female YN and Y1 population estimates showed no difference. These data suggest Y-chromosomes can adaptively shape male traits by contributing additive genetic variation. Further, Y-chromosomes may influence the evolution of sexual dimorphism by shaping male traits shared by both sexes. heritability Y-chromosome sexual dimorphism epistasis Figures Figure 1 Figure 2 Introduction At first glance, the potential for Y-chromosomes to influence the evolution of complex quantitative characters appears insignificant. Y-chromosomes are patrilineal, exhibit minimal recombination with X-chromosome homologs, tend to be highly heterochromatic, harbor few protein coding genes, decay over evolutionary time, and have been lost entirely in some species (Carvalho et al., 2009; Brown et al., 2020; Burgoyne, 1998; Castillo et al., 2010). In Drosophila , males are perfectly viable without the Y-chromosome, and its few invariant genes are essential only for male fertility (Carvalho et al., 2015). Despite this, numerous studies in Drosophila report Y-chromosome variants can differentially influence a broad range of behavioral, physiological, and morphological characters (Table 1). This effect is likely due to the Y-chromosome’s ability to modulate a genome’s chromatin landscape, influencing the regulation of hundreds of autosomal genes at euchromatin-heterochromatin boundaries and potentially impacting dozens of phenotypes (Lemos et al., 2010; Brown et al., 2020). Thus, contrary to the initial description above, Drosophila Y-chromosomes may have the potential to shape characters beyond male fertility. For natural selection to adaptively shape Y-linked variation, and hence the quantitative traits they affect, additive variation must exist among Y-chromosomes of a local gene pool (Figures 1A and 1B). However, theoretical work predicts that additive genetic variation on Y-chromosomes is difficult to maintain (Clark, 1987, 1990) and most empirical studies assessing Y-linked effects are unable to assess the potential for additive variation. That is because these studies generally sampled Ys from geographically distinct populations (Table 1) and placed them into isogenic and/or non-coevolved genetic backgrounds. This methodology creates several limitations when attempting to assess the evolutionary impact of Y-linked effects. First, Y-chromosomes sourced from distant populations provide little information regarding the variation segregating within local populations where selection operates. Second, non-coevolved backgrounds can induce artificial epistatic interactions. Epistasis occurs when an allele’s contribution to the phenotype is contingent upon alleles at other loci (Wolf et al., 2000). In other words, non-coevolved backgrounds can create Y-linked effects that do not occur in natural populations (for example, Figure 1B if genotype A1A1 represented a coevolved background and A2A2 a novel background; see also Stoltenberg and Hirsch 1997). Third, when Ys are assessed in an isogenic background, Y-chromosome by genetic background epistatic variation is eliminated. Removal of this variation can deceptively generate Y-linked additive effects in populations where none functionally exist (for example, Figure 1C if A1A1 was the only genetic background examined; see also Chippendale and Rice, 2001; Kutch and Fedorka, 2017). Only a handful of Drosophila studies have assessed the adaptive potential of Y-chromosome variants sampled from a single population and placed within coevolved, genetically variable backgrounds. Chippindale and Rice (2001) found Y-linked additive variation for male fitness in D. melanogaster when Ys were placed in unique backgrounds (i.e. backgrounds with minimal variation). However, this variation disappeared once multiple Ys and backgrounds were considered. Similarly, Kutch and Fedorka (2017) found significant Y-chromosome effects for immune function within coevolved isogenic backgrounds that vanished when multiple backgrounds were incorporated into the analysis. Polak and Starmer (2005) also showed a Y-linked effect for male sex-comb morphology when Ys were examined in their coevolved backgrounds. However, a limited number of Ys and backgrounds (only two of each) prohibited a meaningful assessment of their potential to adaptively shape sex comb morphology. The elimination of Y-linked additive variation once multiple genetic backgrounds are examined (Chippindale and Rice, 2001; Kutch and Fedorka, 2017) is indicative of sign epistasis, which occurs when alleles produce opposite phenotypic effects in different genetic backgrounds (Figure 1C). Therefore, the Y-linked effects reported for numerous traits over the years (Table 1) may represent sign epistatic variation circulating in natural populations and might not contribute to the adaptive evolution of the traits they affect. To address the hypothesis that Y-linked variation does not necessarily contribute to adaptive evolution, Kutch and Fedorka (2018) selected for improved male geotaxis in D. melanogaster populations possessing multiple Y-chromosomes (i.e. possessing Y-linked variation) or a single Y-chromosome variant (i.e. possessing no Y-linked variation). Geotaxis was chosen because it is a quantitative trait expressed by both sexes and previously shown to be affected by Y-chromosome variants (Stoltenberg and Hirsch, 1997). They found that after 20 generations, single-Y populations responded to selection while multi-Y populations did not. This pattern implies that Y-chromosome by background epistasis constrained the rate of adaptive evolution, most likely by reducing male trait heritability. It should be noted that apart from pure sign epistasis, epistasis in general can either increase or decrease the heritability of quantitative traits (Cheverud and Routman, 1995), which either improves or hinders a trait’s response to selection. However, Drosophila studies to date suggest Y-chromosome by genetic background interactions tends to minimize heritable variation and obscure Y-chromosome variants from selection’s view (Chippindale and Rice, 2011; Kutch and Fedorka, 2017). Thus, contemporary Y-chromosome variants within a population may generally act as evolutionary hinderances for male quantitative traits instead of as fuel for adaptive evolution. If a trait is expressed by both sexes (as with geotaxis), then a Y-linked reduction in male heritability could also slow the female selection response if they share a similar phenotypic optimum. This could have significant implications for how a population responds to a novel selective pressure like climate change or an invading pathogen or species. To better understand the evolutionary impact of Y-chromosome variation, more studies that assess Y-linked effects in their appropriate and variable genetic backgrounds are needed; especially considering that most studies reporting Y-linked effects also noted significant Y-by-background effects (Table 1). Here we address the potential for Y-chromosome variation in D. simulans to influence male trait heritability. Using similar methodology to Kutch and Fedorka (2018), we created replicate populations containing multiple Y-chromosomes and populations with a single Y-chromosome variant, with other genetics elements being homogenized within replicates. We then assessed the male and female heritabilities for sternopleural bristles, abdominal bristles, tibia length, and sex comb morphology; the former three traits being expressed by both sexes and the latter trait being male-only. If Y-chromosomes positively contribute to the additive genetic variation of a shared trait and hence its heritability, then Y-linked effects would be an effective way to shape sexual dimorphism. In contrast, if Y-chromosomes induce significant Y-by-background epistasis, then male trait heritability could be reduced, which would constrain further adaptive trait evolution. Methods Experimental Design Overview To examine how the Y-chromosome influences narrow-sense heritability, two types of populations were created. YN populations contained numerous Y-chromosome variants and Y1 populations contained a single Y-chromosome variant. Two replicate pair of YN and Y1 populations were created and population size, allele frequencies, and cytoplasmic elements were equalized within each pair (Figure 2). Heritabilities were calculated for left and right sternopleural bristle number, abdominal bristle number on sternites 4 and 5, left and right tibia length, and number of teeth on left and right sex comb (hereafter simply termed sex comb number). Trait heritabilities were calculated separately for each population type, replicate, and sex. If trait heritability is greater in the YN population, it would suggest Y-chromosomes contribute additive variation. However, if YN population exhibits lower heritability, it would suggest Y-chromosomes reduce heritable variation through epistasis, which would constrain the rate of trait evolution. For each trait, female heritabilities served as controls. This is because YN and Y1 female heritabilities are not expected to differ, as their allele frequencies were equalized and they do not contain Y-chromosomes. If female differences in YN and Y1 heritabilities were found for a given trait, differences in male YN and Y1 trait heritabilities would be difficult to interpret. YN and Y1 Population Creation All flies were maintained on a cornmeal medium at 25°C, under a 12-h:12-h light: dark photoperiod in Percival incubators. During winter 2017, 351 female D. simulans were collected within 6 miles of the University of Central Florida and isofemale lines were established for species identification. After species identification was confirmed via male offspring, the isofemale offspring were combined, mixed, and separated into two replicate base populations. Within each replicate base population a YN and a Y1 population was created (Figure 2). YN were maintained at 500 males and 500 females per generation in 12in 3 population cages. Y1 populations were established with a single YN male mated to several virgin YN females. In subsequent generations, newly established Y1 male offspring were mated to YN virgin females until Y1 populations reached 1000 individuals (Generation 5). While the diversity of Y-chromosome variants in YN was not confirmed, previous work using D. melanogaster showed a diversity of Y-chromosomes existed in the local Orlando population (Kutch & Fedorka, 2018). Therefore, it is reasonable to assume multiple Y-chromosomes also exist in the D. simulans Orlando population. At generation 5, YN and Y1 populations likely exhibited similar allele frequencies, though small differences may have persisted due to initial founder effects. Therefore, at generation 6 all newly eclosed virgin females from YN populations were swapped with Y1 females from the paired population. Males from each population were not exchanged and acted as allelic reservoirs for that population. To equalize the cytoplasmic elements, half of the females from each YN and Y1 paired populations were swapped in generation 7. Half of the females were swapped again in generation 8 to equalize X-chromosome frequencies, giving each X-chromosome an equal probability of originating from YN or Y1. To further minimize sampling error, half of newly eclosed virgin females were swapped among YN and Y1 populations within replicates an additional 3 times (Generations 9-11). In generation 12, virgin flies were collected and mating pairs established in vials for 24h. After 24h the flies were removed from the vials. The next generation was collected as 3-day-old flies, with all flies within a vial being full-siblings. For each vial, males and females were placed in separate, labeled microcentrifuge tubes which were placed in a -80⁰C freezer for subsequent trait measurement (Generation 13). The flies were frozen dry. Trait Measurement Morphological traits were measured rather than life-history, behavioral, or physiological traits because of their generally higher heritabilities, smaller associated standard errors (Roff & Mousseau, 1987), and ease of measurement at large sample sizes. The morphological traits chosen were assumed to be polygenic, which would increase the probability that the Y-chromosome affected their expression through autosomal chromatin modification. To this end, we measured left and right sternopleural bristles, sternite 4 and 5 abdominal bristles, left and right front leg tibia length, and left and right sex comb number (males only). Abdominal and sternopleural bristle numbers were counted under a dissecting microscope. Front legs were mounted on slides, from which tibia length and sex comb number were assessed using a compound microscope equipped with an ocular camera (Dino-Lite Dino-Eye model AM-423X). Bristle and sex comb tooth number were count measurements that have very high repeatability. Tibia length repeatability was 0.92, calculated via the intraclass correlation (Zar, 1984) using 240 tibias measured twice. Heritability Analysis A full-sibling design was used to calculate heritabilities and associated standard errors, which were derived from one-way ANOVA model parameters (Roff 1997, equations 2.27 and 2.28). The upper and lower 95% confidence limits were estimated by multiplying the upper and lower standard errors by 1.96. Genetic correlations were estimated using Pearson product moment correlations among family means (Via, 1984). Heritabilities were calculated for each of the 8 groups (male/female, YN/Y1, replicate 1/2). Full-sib family size ranged between 8 and 10 individuals per sex. For abdominal bristle number, tibia length, and sex comb number, if the area associated with the trait appeared damaged, then the measurement was excluded from analysis. For sternopleural bristle number, measurements were only included in the analysis if all three macro bristles were present or if two macro bristles were present and the other bristles appeared undisturbed; in which case one bristle was added to the count to account for the missing macro bristle. All analyses were done using R. Results In total, 3348 flies were analyzed: 1688 male and 1660 female. Summary statistics are provided for each of the 56 trait heritabilities that were calculated (Supplemental Table 1). The average number of families ( + SE) per heritability estimate was 32.7 + 0.5, with an average family size of 9.15 + 0.07 flies. Average, maximum, and minimum morphological trait values resemble values seen in previous studies (Capy et al., 1993; Macdonald and Goldstein, 1999). Accordingly, females were shown to have a higher number of sternopleural bristles (♂ average = 9.68 + 0.03; ♀ average = 10.51 + 0.03) and abdominal bristles (♂ average = 15.55 + 0.04; ♀ average = 19.75 + 0.05; calculated as average of 4 th and 5 th sternite), as well as longer tibias (♂ average = 0.453 + 0.001 mm; ♀ average = 0.464 + 0.001 mm). To assess the influence of Y-chromosome variation on male trait heritability, we first determined if YN and Y1 populations differed for female trait heritabilities. If differences existed, it would suggest that the YN and Y1 populations differed in their allele frequencies, which could cause divergent heritabilities not due to the Y-chromosome and weaken our assessment of male differences. However, no significant differences were detected for any female trait between YN and Y1 populations (Table 2). Regarding males, significant differences were found for 4 th and 5 th sternite abdominal bristle number, right tibia length and left sex comb (Table 2). All significant differences indicate that YN populations had higher heritabilities than Y1 populations. When the heritability differences between YN and Y1 were averaged across all traits, we found that males from the YN populations exhibited a 0.14 + 0.11 (mean + 95 %CI) greater heritability on average than the Y1 populations. In contrast, females only exhibited a 0.01 + 0.06 (mean + 95%CI) increase in heritability. This approach provides a conservative method for assessing an overall difference between the YN and Y1 heritabilities that included all traits. In short, these data suggest that Y-chromosomes contribute additive variation to the heritabilities of these traits. Our replicate populations consistently found YN-Y1 differences in abdominal bristle heritability, but only replicate two found YN-Y1 differences in tibia length and sex comb heritability. Such inconsistency may be due to the limited number of families used to estimate heritabilities. To alleviate this potential issue, we combined replicate populations and recalculated our estimates, with replicate population included as a covariate in our models. This recalculation increased the average number of families examined for each trait from 32.7 + 0.5 to 65.6 + 1.4. Again, we found no significant difference between YN and Y1 female estimates. Male YN heritability estimates were greater than Y1 estimates for abdominal bristle number on the 4 th and 5 th sternites, and for left side sex comb number (Table 3). Further, the YN populations exhibited on average a 0.15 + 0.08 (mean + 95% CI) greater heritability than the Y1 populations for males, but only a 0.01 + 0.05 greater heritability for females. Genetic correlations between traits were estimated by first averaging individual left and right sides for sternopleural bristle number, sex comb number, and tibia length, as well as averaging the 4 th and 5 th abdominal sternites. We found that male and female correlations were similar, and no difference in the female correlations between YN and Y1 was detected (Table 4). We found that YN populations exhibited higher male genetic correlations between tibia length and sternopleural bristles, as well as tibia and sex comb number. Further, the male correlation between sex comb and abdominal bristle was low, suggesting that they are largely independent traits controlled by different genes. Discussion Understanding how sexual dimorphism evolves despite a shared genome has been of great interest to evolutionary biologists (Dean and Mank, 2014). Traditionally, Y-chromosomes have been dismissed as unimportant contributors to dimorphism due to their heterochromatic and degenerate nature. However, this traditional view was questioned when Drosophila Y-chromosomes were shown to influence the expression of hundreds of autosomal and X-linked genes (Lemos et al., 2008). Nevertheless, the potential for Y-chromosomes to contribute to sexually dimorphic evolution via Y-linked additive variation has gone largely unstudied. If Y-chromosomes contribute to a trait’s additive variation, they can help shape dimorphism, permitting the sexes to reach separate phenotypic optima. Here we show that populations with multiple Y-chromosomes (YN) exhibited higher male heritabilities compared to populations with a single Y-chromosome variant (Y1). Further, female heritabilities did not significantly differ between these populations (females served as controls since they do not possess Y-chromosomes). In short, our data suggest that Y-chromosomes contribute additive variation to two of the four traits examined here. The contribution of Y-linked additive variation to complex quantitative traits is somewhat unexpected, as theoretical models predict such variation is hard to maintain (Clark, 1987). Furthermore, previous empirical work suggests that Y-chromosome variation within D. melanogaster populations may be largely sign epistatic (Chippindale and Rice, 2001; Kutch and Fedorka, 2017), which likely decreases trait heritability and can impair populations from quickly responding to selective pressures (Kutch & Fedorka, 2018). Interestingly, a recent study in seed beetles ( Callosobruchus maculatus ) supports our finding. Specifically, Kaufmann and colleagues (2021) showed that Y-linked variation significantly contributed to the evolution of sexually dimorphic body size under artificial selection, despite being gene poor and highly heterochromatic. Thus, our findings, coupled with the latter study, suggest that Ys may play a significant role in the evolution of sexual dimorphism for a variety of traits. Although we show evidence of Y-linked variation for complex traits, several minor caveats exist. Central to our design was the creation of populations with multiple Y-chromosomes (YN). However, it is unclear how many Y-chromosomes were maintained in the YN populations. Kutch and Fedorka (2017) collected 40 female D. melanogaster from Orlando and, by examining Y-linked phenotypes, were able to determine at least 3 differing Y-chromosomes (many more were likely). Our current design collected a total of 351 female D. simulans from Orlando, so it is reasonable to assume multiple Y-chromosomes are segregating in the YN populations. In addition, the tests for additive variation for each morphological character is not entirely independent, as non-zero genetic correlations exist between these traits. Tibia length, specifically, had male correlations as high as 0.44. However, abdominal bristle number and sex comb number, the two traits found to exhibit differences between YN-Y1 heritabilities, had low correlations suggesting they were relatively independent characters. In summary, we report significantly higher heritabilities in YN populations compared to Y1 populations for abdominal bristle number and sex comb number. This suggests that Y-chromosomes contribute additive genetic variance for these traits. For abdominal bristle number, a trait shared by both sexes, this means that the Y-chromosome can help shape sexual dimorphism, aiding each sex in reaching their phenotypic optimum. When examining this study in the context of similar studies such as Kutch and Fedorka (2017, 2018) and Kaufmann and colleagues (2021), it appears that the type of variation the Y-chromosome contributes depends on the species, population, and trait examined. For instance, Y-chromosome variation may be additive in one population or for one trait, but largely epistatic elsewhere. Future research should examine the type of variation induced by the Y-chromosome across these variables. This would make clearer the evolutionary consequences of the Y-chromosome’s influence on hundreds of autosomal genes. Declarations Acknowledgements We thank Megan Danis, Kevin Cedeno, Saara Rasool, Mariam Sleem, Karishma Santadasani, Connor McDonnald, and Naiomy Gonzalez Carrero for assistance with sample processing. Author Contribution The study was conceived and designed by TMN and KMF, data collection was overseen by TMN and JB, data analysis was conducted by TMN and KMF, and the manuscript was authored by TMN and KMF. Conflict of Interest The authors declare no competing financial interests. Data Archiving Data will be archived in the Dryad data repository upon acceptance of the manuscript. References Åslund, S. E., Holmgren, P., & Rasmuson, B. (1978). The effects of number of Y chromosomes on mating behaviour and bristle number in Drosophila melanogaster . Hereditas, 89(2), 249-254. Barigozzi, C. (1951). The influence of the Y-chromosome on quantitative characters of Drosophila melanogaster . Heredity, 5(3), 415-432. Brown, E. J., Nguyen, A. H., & Bachtrog, D. (2020). The Drosophila Y chromosome affects heterochromatin integrity genome-wide. Molecular biology and evolution, 37(10), 2808-2824. Burgoyne, P. S. (1998). The mammalian Y chromosome: a new perspective. Bioessays, 20(5), 363-366. Capy, P., Pla, E., & D avid, J. R. (1993). Phenotypic and genetic variability of morphometrical traits in natural populations of Drosophila melanogaster and D simulans . I. Geographic variations. Genetics Selection Evolution, 25(6), 517-536. Carvalho, A. B., Vaz, S. C., & Klaczko, L. B. (1997). Polymorphism for Y-linked suppressors of sex-ratio in two natural populations of Drosophila mediopunctata . Genetics, 146(3), 891-902. Carvalho, A. B., Koerich, L. B., & Clark, A. G. (2009). Origin and evolution of Y chromosomes: Drosophila tales. Trends in Genetics, 25(6), 270-277. Carvalho, A. B., Vicoso, B., Russo, C. A., Swenor, B., & Clark, A. G. (2015). Birth of a new gene on the Y chromosome of Drosophila melanogaster . Proceedings of the National Academy of Sciences, 112(40), 12450-12455. Castillo, E. R., Marti, D. A., & Bidau, C. J. (2010). Sex and neo-sex chromosomes in Orthoptera: a review. Journal of Orthoptera Research, 213-231. Chippindale, A. K., & Rice, W. R. (2001). Y chromosome polymorphism is a strong determinant of male fitness in Drosophila melanogaster . Proceedings of the National Academy of Sciences, 98(10), 5677-5682. Clark, A. G. (1987). Natural selection and Y-linked polymorphism. Genetics, 115(3), 569-577. Clark, A. G. (1987). Variation in Y chromosome segregation in natural populations of Drosophila melanogaster . Genetics, 115(1), 143-151. Clark, A. G. (1990). Two tests of Y chromosomal variation in male fertility of Drosophila melanogaster . Genetics, 125(3), 527-534. Clark, A. G., Szumski, F. M., & Lyckegaard, E. M. (1991). Population genetics of the Y chromosome of Drosophila melanogaster : rDNA variation and phenotypic correlates. Genetics Research, 58(1), 7-13. Dean, R., & Mank, J. E. (2014). The role of sex chromosomes in sexual dimorphism: discordance between molecular and phenotypic data. Journal of Evolutionary Biology, 27(7), 1443-1453. Fry, J. D., DeRonde, K. A., & Mackay, T. F. (1995). Polygenic mutation in Drosophila melanogaster : genetic analysis of selection lines. Genetics, 139(3), 1293-1307. Etges, W. J., Over, K. F., De Oliveira, C. C., & Ritchie, M. G. (2006). Inheritance of courtship song variation among geographically isolated populations of Drosophila mojavensis . Animal Behaviour, 71(5), 1205-1214. Huttunen, S., & Aspi, J. (2003). Complex inheritance of male courtship song characters in Drosophila virilis . Behavior genetics, 33(1), 17-24. Jaenike, J. (1999). Suppression of sex‐ratio meiotic drive and the maintenance of Y‐chromosome polymorphism in Drosophila . Evolution, 53(1), 164-174. Jiang, P. P., Hartl, D. L., & Lemos, B. (2010). Y not a dead end: epistatic interactions between Y-linked regulatory polymorphisms and genetic background affect global gene expression in Drosophila melanogaster . Genetics, 186(1), 109-118. Joly, D., Bazin, C., Zeng, L. W., & Singh, R. S. (1997). Genetic basis of sperm and testis length differences and epistatic effect on hybrid inviability and sperm motility between Drosophila simulans and D. sechellia . Heredity, 78(4), 354-362. Kaufmann, P., Wolak, M. E., Husby, A., & Immonen, E. (2021). Rapid evolution of sexual size dimorphism facilitated by Y-linked genetic variance. Nature Ecology & Evolution, 1-9. Kutch, I. C., & Fedorka, K. M. (2015). Y-linked variation for autosomal immune gene regulation has the potential to shape sexually dimorphic immunity. Proceedings of the Royal Society B: Biological Sciences, 282(1820), 20151301. Kutch, I. C., & Fedorka, K. M. (2017). A test for Y‐linked additive and epistatic effects on surviving bacterial infections in Drosophila melanogaster . Journal of evolutionary biology, 30(7), 1400-1408. Kutch, I. C., & Fedorka, K. M. (2018). Y-chromosomes can constrain adaptive evolution via epistatic interactions with other chromosomes. BMC evolutionary biology, 18(1), 1-8. Lemos, B., Araripe, L. O., & Hartl, D. L. (2008). Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. science, 319(5859), 91-93. Lemos, B., Branco, A. T., & Hartl, D. L. (2010). Epigenetic effects of polymorphic Y chromosomes modulate chromatin components, immune response, and sexual conflict. Proceedings of the National Academy of Sciences, 107(36), 15826-15831. Macdonald, S. J., & Goldstein, D. B. (1999). A quantitative genetic analysis of male sexual traits distinguishing the sibling species Drosophila simulans and D. sechellia . Genetics, 153(4), 1683-1699. Mather, K. (1944). The genetical activity of heterochromatin. Proceedings of the Royal Society of London. Series B-Biological Sciences, 132(868), 308-332. Miller, G. T., Starmer, W. T., & Pitnick, S. (2003). Quantitative genetic analysis of among-population variation in sperm and female sperm-storage organ length in Drosophila mojavensis . Genetics Research, 81(3), 213-220. Montchamp‐Moreau, C., Ginhoux, V., & Atlan, A. (2001). The Y chromosomes of Drosophila simulans are highly polymorphic for their ability to suppress sex‐ratio drive. Evolution, 55(4), 728-737. Polak, M., & Starmer, W. T. (2005). Environmental origins of sexually selected variation and a critique of the fluctuating asymmetry-sexual selection hypothesis. Evolution, 59(3), 577-585. Roff, D. A. (1997). Evolutionary Quantitative Genetics. Chapman and Hall. DOI:10.1007/978-1-4615-4080-9 Rohmer, C., David, J. R., Moreteau, B., & Joly, D. (2004). Heat induced male sterility in Drosophila melanogaster : adaptive genetic variations among geographic populations and role of the Y chromosome. Journal of Experimental Biology, 207(16), 2735-2743. Stalker, H. D. (1961). The genetic systems modifying meiotic drive in Drosophila paramelanica . Genetics, 46(2), 177. Stoltenberg, S. F., & Hirsch, J. (1997). Y-chromosome effects on Drosophila geotaxis interact with genetic or cytoplasmic background. Animal behaviour, 53(4), 853-864. Via, S. (1984). The quantitative genetics of polyphagy in an insect herbivore. II. Genetic correlations in larval performance within and among host plants. Evolution, 896-905. Wolf, J. B., Brodie, E. D., & Wade, M. J. (Eds.). (2000). Epistasis and the evolutionary process. Oxford University Press. Zar, J. H. Biostatistical Analysis 2nd edn, 324 (Prentice Hall, Englewood Cliffs, New Jersey, 1984) Tables Table 1. Y-linked effect studies in Drosophila . Definition of terms: (i) Y-chromosome and Background source - An isoline is an isogenic or nearly isogenic fly strain that contains fixed genetic elements such as a single Y or X chromosome. A stock is a laboratory population that likely contains genetic variation including multiple Ys, Xs, and autosomes. A population is a stock recently derived form a natural location for the purpose of characterizing the individuals in that location. (ii) Background variation - Considered isogenic if derived from an isofemale line or if 1 or more X / autosomes were isogenic. Considered heterozygous if comprised of 2 isolines. If the authors did not explicitly state its condition, the background was listed as “unknown”. (iii) Y x B Effect - NA refers to the Y by background interaction not being formally assessed by the authors or difficult to infer from the reported data. * Y-linked effects were statistically significant only within isolated backgrounds but were not significant when examined across backgrounds. Drosophila species are as follows: mel = D. melanogaster , par = D. paramelanica , med = D. mediopunctata , sim = D. simulans , qui = D. quinarian , moj = D. mojavensis , vir = D. virilis , bip = D. bipectinata . Trait Y-Chrom Source Background Source Background Variation Co-evolved Y and B Y-Linked Effect Y x B Effect Species Citation Sternopleural Bristle 12 Ys from 12 isolines 1 isoline Isogenic No Yes Yes mel Mather 1944 Wing hair; corneae size 4 Ys from 4 isolines 1 isoline Isogenic No Yes NA mel Barigozzi 1951 Sex-ratio suppressor 50 Ys from 50 isolines across 16 populations 11 isolines across 7 populations Heterozygous mixed Yes Yes par Stalker 1961 Mating behavior; Bristle number 3 Ys from 2 isolines to create 4 genotypes: X0, XY, XYY, XYYY 2 isolines isogenic No Yes NA mel Aslund et al. 1978 Sex-ratio suppressor 36 Ys from 36 isolines 2 isolines Isogenic No Yes No mel Clark 1987 Fertility; sperm competition 36 Ys from 36 isolines 2 isolines Isogenic No No No mel Clark 1990 Bristle length; development time 36 Ys from 36 isolines 2 isolines Isogenic No No Yes mel Clark 1991 Abdominal and sternopleural bristle 12 Ys from 12 selection lines Selected and isolines heterozygous No Yes NA mel Fry et al. 1995 Sex-ratio suppressor 25 Ys across 2 populations 1 isoline heterozygous No Yes NA med Carvalho et al. 1997 Geotaxis 2 Ys from 2 selection lines 2 selection lines and 2 stocks Unknown No Yes Yes mel Stoltenberg et al. 1997 Sperm length Unknown # Ys from 2 hybridizing species stocks 2 species stocks Unknown No Yes NA sim Joly et al. 1997 Sex-ratio suppressor 61 Ys across 3 populations 6 isolines isogenic No Yes Yes qui Jaenike 1999 Sex-ratio suppressor 107 Ys across 6 populations 1 isoline isogenic No Yes NA sim Montchamp-Moreau 2001 Male fitness 20 Ys from 1 stock 3 isolines lines, 1 stock variable across lines Yes No* Yes mel Chippindale et al. 2001 Sperm length Unknown # Ys across 2 populations 2 populations Unknown No Yes Yes moj Miller et al. 2003 Courtship song Unknown # of Ys from 2 stocks 2 stocks Unknown No Yes Yes vir Huttunen et al. 2003 Heat-induced sterility Unknown # of Ys from 2 stocks 2 stocks Unknown No Yes Yes mel Rohmer et al. 2004 Sex comb 2 Ys from 2 isolines 2 Isolines Isogenic and heterozygous Yes Yes NA bip Polak et al. 2005 Courtship song Unknown # Ys from 13 stocks 2 stocks Unknown No Yes Yes moj Etges et al. 2006 Genome-wide regulation 5 Ys from 5 isolines 1 stock Isogenic No Yes NA mel Lemos et al. 2008 PEV, gene regulation 16 Ys from 16 isolines 1 stock Isogenic No Yes NA mel Lemos et al. 2010 PEV, gene regulation Unknown # Ys from 2 populations 2 stocks and 1 isoline Unknown / isogenic No Yes Yes mel Jiang et al. 2010 Lifespan 33 Ys from 33 isolines 1 isoline Isogenic Yes Yes NA mel Griffen et al. 2015 Immune Function 30 Ys from 30 isolines across 1 population 1 stock Isogenic No Yes NA mel Kutch et al. 2015 Immune Function 4 Ys from 4 isolines across 1 population 4 isolines across 1 population variable across lines Yes No* Yes mel Kutch et al. 2017 Table 2. Sex-specific trait heritabilities for each treatment (YN and Y1) and replicate population (R1 and R2). 95% confidence intervals for each heritability estimate appears in parentheses. YN-Y1 represents the difference in treatment heritabilities. YN≠Y1 represents the statistical assessment of the treatment difference. Y1 YN YN-Y1 Y1≠YN Trait Sex Population h 2 (±95%CI) h 2 (±95%CI) Sternopleural Bristle - Left ♂ R1 0.34 (0.20) 0.54 (0.25) 0.20 ns R2 0.35 (0.22) 0.37 (0.21) 0.02 ns ♀ R1 0.22 (0.17) 0.31 (0.20) 0.09 ns R2 0.49 (0.24) 0.35 (0.23) -0.15 ns Sternopleural Bristle - Right ♂ R1 0.28 (0.19) 0.47 (0.24) 0.19 ns R2 0.38 (0.24) 0.47 (0.23) 0.10 ns ♀ R1 0.38 (0.21) 0.37 (0.22) -0.01 ns R2 0.52 (0.25) 0.39 (0.24) -0.13 ns Abdominal Bristle - 4th Sternite ♂ R1 0.25 (0.20) 0.62 (0.26) 0.37 ** R2 0.36 (0.22) 0.51 (0.25) 0.16 ns ♀ R1 0.37 (0.23) 0.37 (0.22) 0.00 ns R2 0.37 (0.22) 0.26 (0.19) -0.11 ns Abdominal Bristle - 5th Sternite ♂ R1 0.18 (0.17) 0.53 (0.25) 0.35 ** R2 0.31 (0.20) 0.57 (0.26) 0.26 * ♀ R1 0.31 (0.22) 0.26 (0.20) -0.05 ns R2 0.39 (0.23) 0.43 (0.23) 0.04 ns Tibia Length - Left ♂ R1 1.05 (0.29) 0.77 (0.29) -0.28 ns R2 0.66 (0.28) 0.91 (0.30) 0.25 ns ♀ R1 0.67 (0.28) 0.71 (0.29) 0.04 ns R2 0.48 (0.26) 0.72 (0.28) 0.24 ns Tibia Length - Right ♂ R1 0.97 (0.30) 0.71 (0.29) -0.26 ns R2 0.53 (0.27) 0.96 (0.29) 0.42 ** ♀ R1 0.67 (0.29) 0.66 (0.28) -0.01 ns R2 0.45 (0.25) 0.57 (0.27 0.12 ns Sex Comb - Left ♂ R1 0.28 (0.21) 0.32 (0.22) 0.04 ns R2 0.07 (0.14) 0.45 (0.25) 0.38 ** Sex Comb - Right ♂ R1 0.27 (0.21) 0.14 (0.16) -0.13 ns R2 0.29 (0.21) 0.41 (0.24) 0.12 ns Average of YN-Y1 Male 0.14 (0.11) ** Female 0.01 (0.06) ns * P<0.05, ** P<0.01 Table 3 . Male and female trait heritabilities for YN and Y1 with replicates combined into a single population. Y1 YN YN-Y1 Y1≠YN Trait Sex h 2 (±95%CI) h 2 (±95%CI) Sternopleural Bristle - Left ♂ 0.34 (0.15) 0.41 (0.16) 0.07 ns ♀ 0.34 (0.15) 0.34 (0.15) 0.00 ns Sternopleural Bristle - Right ♂ 0.30 (0.14) 0.45 (0.16) 0.15 ns ♀ 0.45 (0.16) 0.43 (0.17) -0.02 ns Abdominal Bristle - 4th Sternite ♂ 0.22 (0.13) 0.45 (0.17) 0.23 ** ♀ 0.39 (0.16) 0.32 (0.15) -0.07 ns Abdominal Bristle - 5th Sternite ♂ 0.21 (0.13) 0.54 (0.18) 0.33 ** ♀ 0.35 (0.16) 0.27 (0.14) -0.08 ns Tibia Length - Left ♂ 0.72 (0.20) 0.71 (0.20) -0.01 ns ♀ 0.51 (0.19) 0.69 (0.20) 0.18 ns Tibia Length - Right ♂ 0.64 (0.20) 0.77 (0.21) 0.13 ns ♀ 0.54 (0.19) 0.57 (0.19) 0.03 ns Sex Comb - Left ♂ 0.19 (0.14) 0.42 (0.18) 0.23 * Sex Comb - Right ♂ 0.31 (0.18) 0.38 (0.18) 0.07 ns Average of YN-Y1 Male 0.15 (0.08) ** Female 0.01 (0.05) ns * P<0.05, ** P<0.01 Table 4. Male and female genetic correlations between traits. SPB = sternopleural bristle, AB = abdominal bristle, TL = tibia length, SC = sex comb. Female Male Y1 YN YN-Y1 YN≠Y1 Y1 YN YN-Y1 YN≠Y1 SPB AB 0.15 (0.08) 0.18 (0.08) 0.03 ns 0.14 (0.08) 0.19 (0.07) 0.05 ns SPB TL 0.20 (0.08) 0.16 (0.08) -0.04 ns 0.15 (0.08) 0.24 (0.07) 0.09 * AB TL 0.46 (0.07) 0.53 (0.06) 0.07 ns 0.44 (0.08) 0.43 (0.07) -0.01 ns SPB SC 0.09 (0.07) 0.11 (0.08) 0.02 ns AB SC 0.10 (0.09) 0.13 (0.08) 0.03 ns TL SC 0.24 (0.08) 0.37 (0.07) 0.13 ** Additional Declarations There is no duality of interest Supplementary Files SupplementalTable1.docx Supplemental Table 1 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 02 Sep, 2022 Review # 2 received at journal 29 Aug, 2022 Review # 3 received at journal 15 Aug, 2022 Reviewer # 3 agreed at journal 15 Aug, 2022 Reviewer # 2 agreed at journal 11 Aug, 2022 Review # 1 received at journal 11 Aug, 2022 Reviewer # 1 agreed at journal 29 Jul, 2022 Reviewers invited by journal 28 Jul, 2022 First submitted to journal 20 Jul, 2022 Editor assigned by journal 20 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1879119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":124687648,"identity":"cbcdf06c-6832-4c90-bed2-14aac8d6cb58","order_by":0,"name":"Kenneth Fedorka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBAC9gYGAwaGAwxyDAyMDxgY2IjQwnMAosWYgYHZgDQtiQ3Ea2Fv3vi44oxd+objhxkYPpQdJkILz7FiwzM3knM3nElmYJxxjggt9hI5ZpINH5hzN9zgP8DM20aMLfJvQFrq0w1uMDMw/yVKiwQPUMuNwwlgLYxEaeFJKzZsOHPccCbQLwd7zqUToYX98MaHDceq5fmOH2Z88KPMmrAWFHCARPWjYBSMglEwCnABAC/mO84ZyWxEAAAAAElFTkSuQmCC","orcid":"","institution":"Universrity of Central Florida","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Fedorka","suffix":""},{"id":124687649,"identity":"47146a62-39c8-4f80-bd02-328596bac09f","order_by":1,"name":"Tobias Nielsen","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Nielsen","suffix":""},{"id":124687650,"identity":"49558bef-2ac8-496b-9dac-1547fd187947","order_by":2,"name":"Jaden Baldwin","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jaden","middleName":"","lastName":"Baldwin","suffix":""}],"badges":[],"createdAt":"2022-07-20 18:25:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1879119/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1879119/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24723063,"identity":"aa96fcb3-2493-44ef-95ea-7f34388cf276","added_by":"auto","created_at":"2022-08-03 14:29:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48119,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical examples of physiological epistasis among Y-chromosomes and their genetic background.\u003cstrong\u003e \u003c/strong\u003e(A) The background alleles and Y-chromosomes exhibit pure additivity, creating a phenotype heritability equal to 1. (B) Significant Y-chromosome by background epistasis exists, reducing phenotype heritability compared with figure 1A. (C) The background alleles and Y-chromosomes exhibit pure sign epistasis, reducing phenotype heritability to zero. In all three hypothetical examples, dominance and environmental deviations are ignored and allele frequencies are assumed to be equal.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1879119/v1/03d12ac6f1e398512f340dd4.png"},{"id":24723061,"identity":"67f81211-26ef-4b1a-807c-08baeac8e102","added_by":"auto","created_at":"2022-08-03 14:29:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59323,"visible":true,"origin":"","legend":"\u003cp\u003eInitial steps in the creation of YN and Y1 populations. By generation 5, all populations were comprised of 500 males and 500 females. In generations 6-11, females were swapped between replicates to homogenize autosomal allele, X-chromosome, and cytoplasmic element frequencies (see methods).\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1879119/v1/5d509ab1bedf1de68c77fb31.png"},{"id":24723065,"identity":"b9eedc36-3726-4e4d-95af-190e2a0c2a47","added_by":"auto","created_at":"2022-08-03 14:29:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":321627,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1879119/v1/e41eb1a6-bce6-40a9-b7c5-315c62b47886.pdf"},{"id":24723062,"identity":"f792df15-27d3-46c0-9ae8-38795ff1f5ab","added_by":"auto","created_at":"2022-08-03 14:29:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29297,"visible":true,"origin":"","legend":"Supplemental Table 1","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1879119/v1/f62f60b894cfb2d959161833.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Y-linked additive variation for quantitative traits in Drosophila simulans","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAt first glance, the potential for Y-chromosomes to influence the evolution of complex quantitative characters appears insignificant.\u0026nbsp;Y-chromosomes are patrilineal, exhibit minimal recombination with X-chromosome homologs, tend to be highly heterochromatic, harbor few protein coding genes, decay over evolutionary time, and have been lost entirely in some species (Carvalho et al., 2009; Brown et al., 2020; Burgoyne, 1998; Castillo et al., 2010). In \u003cem\u003eDrosophila\u003c/em\u003e, males are perfectly viable without the Y-chromosome, and its few invariant genes are essential only for male fertility (Carvalho et al., 2015).\u0026nbsp;Despite this, numerous studies in \u003cem\u003eDrosophila\u003c/em\u003e report Y-chromosome variants can differentially influence a broad range of behavioral, physiological, and morphological characters (Table 1). This effect is likely due to the Y-chromosome\u0026rsquo;s ability to modulate a genome\u0026rsquo;s chromatin landscape, influencing the regulation of hundreds of autosomal genes at euchromatin-heterochromatin boundaries and potentially impacting dozens of phenotypes (Lemos et al., 2010; Brown et al., 2020). \u0026nbsp;Thus, contrary to the initial description above, \u003cem\u003eDrosophila\u003c/em\u003e Y-chromosomes may have the potential to shape characters beyond male fertility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor natural selection to adaptively shape Y-linked variation, and hence the quantitative traits they affect, additive variation must exist among Y-chromosomes of a local gene pool (Figures 1A and 1B). However, theoretical work predicts that additive genetic variation on Y-chromosomes is difficult to maintain (Clark, 1987, 1990) and most empirical studies assessing Y-linked effects are unable to assess the potential for additive variation. That is because these studies generally sampled Ys from geographically distinct populations (Table 1) and placed them into isogenic and/or non-coevolved genetic backgrounds. This methodology creates several limitations when attempting to assess the evolutionary impact of Y-linked effects. First, Y-chromosomes sourced from distant populations provide little information regarding the variation segregating within local populations where selection operates. Second, non-coevolved backgrounds can induce artificial epistatic interactions. Epistasis occurs when an allele\u0026rsquo;s contribution to the phenotype is contingent upon alleles at other loci (Wolf et al., 2000). In other words, non-coevolved backgrounds can create Y-linked effects that do not occur in natural populations (for example, Figure 1B if genotype A1A1 represented a coevolved background and A2A2 a novel background; see also Stoltenberg and Hirsch 1997). Third, when Ys are assessed in an isogenic background, Y-chromosome by genetic background epistatic variation is eliminated. Removal of this variation can deceptively generate Y-linked additive effects in populations where none functionally exist (for example, Figure 1C if A1A1 was the only genetic background examined; see also Chippendale and Rice, 2001; Kutch and Fedorka, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly a handful of \u003cem\u003eDrosophila\u003c/em\u003e studies have assessed the adaptive potential of Y-chromosome variants sampled from a single population and placed within coevolved, genetically variable backgrounds. Chippindale and Rice (2001) found Y-linked additive variation for male fitness in \u003cem\u003eD. melanogaster\u003c/em\u003e when Ys were placed in unique backgrounds (i.e. backgrounds with minimal variation). However, this variation disappeared once multiple Ys and backgrounds were considered. Similarly, Kutch and Fedorka (2017) found significant Y-chromosome effects for immune function within coevolved isogenic backgrounds that vanished when multiple backgrounds were incorporated into the analysis. Polak and Starmer (2005) also showed a Y-linked effect for male sex-comb morphology when Ys were examined in their coevolved backgrounds. However, a limited number of Ys and backgrounds (only two of each) prohibited a meaningful assessment of their potential to adaptively shape sex comb morphology. The elimination of Y-linked additive variation once multiple genetic backgrounds are examined (Chippindale and Rice, 2001; Kutch and Fedorka, 2017) is indicative of sign epistasis, which occurs when alleles produce opposite phenotypic effects in different genetic backgrounds (Figure 1C). Therefore, the Y-linked effects reported for numerous traits over the years (Table 1) may represent sign epistatic variation circulating in natural populations and might not contribute to the adaptive evolution of the traits they affect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To address the hypothesis that Y-linked variation does not necessarily contribute to adaptive evolution, Kutch and Fedorka (2018)\u0026nbsp;selected for improved male geotaxis in \u003cem\u003eD. melanogaster\u003c/em\u003e populations possessing multiple Y-chromosomes (i.e. possessing Y-linked variation) or a single Y-chromosome variant (i.e. possessing no Y-linked variation). Geotaxis was chosen because it is a quantitative trait expressed by both sexes and previously shown to be affected by Y-chromosome variants (Stoltenberg and Hirsch, 1997). They found that after 20 generations, single-Y populations responded to selection while multi-Y populations did not. This pattern implies that Y-chromosome by background epistasis\u0026nbsp;constrained the rate of adaptive evolution, most likely by reducing male trait heritability.\u003c/p\u003e\n\u003cp\u003eIt should be noted that apart from pure sign epistasis, epistasis in general can either increase or decrease the heritability of quantitative traits (Cheverud and Routman, 1995), which either improves or hinders a trait\u0026rsquo;s response to selection. However, \u003cem\u003eDrosophila\u003c/em\u003e studies to date suggest Y-chromosome by genetic background interactions tends to minimize heritable variation and obscure Y-chromosome variants from selection\u0026rsquo;s view (Chippindale and Rice, 2011; Kutch and Fedorka, 2017). Thus, contemporary Y-chromosome variants within a population may generally act as evolutionary hinderances for male quantitative traits instead of as fuel for adaptive evolution. If a trait is expressed by both sexes (as with geotaxis), then a Y-linked reduction in male heritability could also slow the female selection response if they share a similar phenotypic optimum. This could have significant implications for how a population responds to a novel selective pressure like climate change or an invading pathogen or species. To better understand the evolutionary impact of Y-chromosome variation, more studies that assess Y-linked effects in their appropriate and variable genetic backgrounds are needed; especially considering that most studies reporting Y-linked effects also noted significant Y-by-background effects (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we address the potential for Y-chromosome variation in \u003cem\u003eD. simulans\u003c/em\u003e to influence male trait heritability. Using similar methodology to Kutch and Fedorka (2018), we created replicate populations containing multiple Y-chromosomes and populations with a single Y-chromosome variant, with other genetics elements being homogenized within replicates. We then assessed the male and female heritabilities for sternopleural bristles, abdominal bristles, tibia length, and sex comb morphology; the former three traits being expressed by both sexes and the latter trait being male-only. If Y-chromosomes positively contribute to the additive genetic variation of a shared trait and hence its heritability, then Y-linked effects would be an effective way to shape sexual dimorphism. In contrast, if Y-chromosomes induce significant Y-by-background epistasis, then male trait heritability could be reduced, which would constrain further adaptive trait evolution.\u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eExperimental Design Overview\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo examine how the Y-chromosome influences narrow-sense heritability, two types of populations were created. YN populations contained numerous Y-chromosome variants and Y1 populations contained a single Y-chromosome variant. Two replicate pair of YN and Y1 populations were created and population size, allele frequencies, and cytoplasmic elements were equalized within each pair (Figure 2). Heritabilities were calculated for left and right sternopleural bristle number, abdominal bristle number on sternites 4 and 5, left and right tibia length, and number of teeth on left and right sex comb (hereafter simply termed sex comb number). Trait heritabilities were calculated separately for each population type, replicate, and sex. If trait heritability is greater in the YN population, it would suggest Y-chromosomes contribute additive variation. However, if YN population exhibits lower heritability, it would suggest Y-chromosomes reduce heritable variation through epistasis, which would constrain the rate of trait evolution. For each trait, female heritabilities served as controls. This is because YN and Y1 female heritabilities are not expected to differ, as their allele frequencies were equalized and they do not contain Y-chromosomes. If female differences in YN and Y1 heritabilities were found for a given trait, differences in male YN and Y1 trait heritabilities would be difficult to interpret.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eYN and Y1 Population Creation\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll flies were maintained on a cornmeal medium at 25\u0026deg;C, under a 12-h:12-h light: dark photoperiod in Percival incubators. During winter 2017, 351 female \u003cem\u003eD. simulans\u003c/em\u003e were collected within 6 miles of the University of Central Florida and isofemale lines were established for species identification. \u0026nbsp;After species identification was confirmed via male offspring, the isofemale offspring were combined, mixed, and separated into two replicate base populations. Within each replicate base population a YN and a Y1 population was created (Figure 2). YN were maintained at 500 males and 500 females per generation in 12in\u003csup\u003e3\u003c/sup\u003e population cages. Y1 populations were established with a single YN male mated to several virgin YN females. In subsequent generations, newly established Y1 male offspring were mated to YN virgin females until Y1 populations reached 1000 individuals (Generation 5). While the diversity of Y-chromosome variants in YN was not confirmed, previous work using \u003cem\u003eD. melanogaster\u003c/em\u003e showed a diversity of Y-chromosomes existed in the local Orlando population (Kutch \u0026amp; Fedorka, 2018). Therefore, it is reasonable to assume multiple Y-chromosomes also exist in the \u003cem\u003eD. simulans\u003c/em\u003e Orlando population.\u003c/p\u003e\n\u003cp\u003eAt generation 5, YN and Y1 populations likely exhibited similar allele frequencies, though small differences may have persisted due to initial founder effects. Therefore, at generation 6 all newly eclosed virgin females from YN populations were swapped with Y1 females from the paired population. Males from each population were not exchanged and acted as allelic reservoirs for that population. To equalize the cytoplasmic elements, half of the females from each YN and Y1 paired populations were swapped in generation 7. Half of the females were swapped again in generation 8 to equalize X-chromosome frequencies, giving each X-chromosome an equal probability of originating from YN or Y1. To further minimize sampling error, half of newly eclosed virgin females were swapped among YN and Y1 populations within replicates an additional 3 times (Generations 9-11).\u003c/p\u003e\n\u003cp\u003eIn generation 12, virgin flies were collected and mating pairs established in vials for 24h. After 24h the flies were removed from the vials. The next generation was collected as 3-day-old flies, with all flies within a vial being full-siblings. For each vial, males and females were placed in separate, labeled microcentrifuge tubes which were placed in a -80⁰C freezer for subsequent trait measurement (Generation 13). The flies were frozen dry.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTrait Measurement\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eMorphological traits were measured rather than life-history, behavioral, or physiological traits because of their generally higher heritabilities, smaller associated standard errors (Roff \u0026amp; Mousseau, 1987), and ease of measurement at large sample sizes. The morphological traits chosen were assumed to be polygenic, which would increase the probability that the Y-chromosome affected their expression through autosomal chromatin modification. To this end, we measured left and right sternopleural bristles, sternite 4 and 5 abdominal bristles, left and right front leg tibia length, and left and right sex comb number (males only). Abdominal and sternopleural bristle numbers were counted under a dissecting microscope. Front legs were mounted on slides, from which tibia length and sex comb number were assessed using a compound microscope equipped with an ocular camera (Dino-Lite Dino-Eye model AM-423X). Bristle and sex comb tooth number were count measurements that have very high repeatability. Tibia length repeatability was 0.92, calculated via the intraclass correlation (Zar, 1984) using 240 tibias measured twice. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHeritability Analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eA full-sibling design was used to calculate heritabilities and associated standard errors, which were derived from one-way ANOVA model parameters (Roff 1997, equations 2.27 and 2.28). The upper and lower 95% confidence limits were estimated by multiplying the upper and lower standard errors by 1.96. Genetic correlations were estimated using Pearson product moment correlations among family means (Via, 1984). Heritabilities were calculated for each of the 8 groups (male/female, YN/Y1, replicate 1/2). Full-sib family size ranged between 8 and 10 individuals per sex. For abdominal bristle number, tibia length, and sex comb number, if the area associated with the trait appeared damaged, then the measurement was excluded from analysis. For sternopleural bristle number, measurements were only included in the analysis if all three macro bristles were present or if two macro bristles were present and the other bristles appeared undisturbed; in which case one bristle was added to the count to account for the missing macro bristle. All analyses were done using R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 3348 flies were analyzed: 1688 male and 1660 female. Summary statistics are provided for each of the 56 trait heritabilities that were calculated (Supplemental Table 1). The average number of families (\u003cu\u003e+\u003c/u\u003e SE) per heritability estimate was 32.7 \u003cu\u003e+\u003c/u\u003e 0.5, with an average family size of 9.15 \u003cu\u003e+\u003c/u\u003e 0.07 flies. Average, maximum, and minimum morphological trait values resemble values seen in previous studies (Capy et al., 1993; Macdonald and Goldstein, 1999). Accordingly, females were shown to have a higher number of sternopleural bristles (♂\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 9.68 \u003cu\u003e+\u003c/u\u003e 0.03; ♀\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 10.51 \u003cu\u003e+\u003c/u\u003e 0.03) and abdominal bristles (♂\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 15.55 \u003cu\u003e+\u003c/u\u003e 0.04; ♀\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 19.75 \u003cu\u003e+\u003c/u\u003e 0.05; calculated as average of 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e sternite), as well as longer tibias (♂\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 0.453 \u003cu\u003e+\u003c/u\u003e 0.001 mm; ♀\u003csub\u003eaverage\u0026nbsp;\u003c/sub\u003e= 0.464 \u003cu\u003e+\u003c/u\u003e 0.001 mm).\u003c/p\u003e\n\u003cp\u003eTo assess the influence of Y-chromosome variation on male trait heritability, we first determined if YN and Y1 populations differed for female trait heritabilities. If differences existed, it would suggest that the YN and Y1 populations differed in their allele frequencies, which could cause divergent heritabilities not due to the Y-chromosome and weaken our assessment of male differences. However, no significant differences were detected for any female trait between YN and Y1 populations (Table 2).\u003c/p\u003e\n\u003cp\u003eRegarding males, significant differences were found for 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e sternite abdominal bristle number, right tibia length and left sex comb (Table 2). All significant differences indicate that YN populations had higher heritabilities than Y1 populations. When the heritability differences between YN and Y1 were averaged across all traits, we found that males from the YN populations exhibited a 0.14 \u003cu\u003e+\u003c/u\u003e 0.11 (mean \u003cu\u003e+\u003c/u\u003e 95 %CI) greater heritability on average than the Y1 populations. In contrast, females only exhibited a 0.01 + 0.06 (mean \u003cu\u003e+\u003c/u\u003e 95%CI) increase in heritability. This approach provides a conservative method for assessing an overall difference between the YN and Y1 heritabilities that included all traits. In short, these data suggest that Y-chromosomes contribute additive variation to the heritabilities of these traits.\u003c/p\u003e\n\u003cp\u003eOur replicate populations consistently found YN-Y1 differences in abdominal bristle heritability, but only replicate two found YN-Y1 differences in tibia length and sex comb heritability. Such inconsistency may be due to the limited number of families used to estimate heritabilities. To alleviate this potential issue, we combined replicate populations and recalculated our estimates, with replicate population included as a covariate in our models. This recalculation increased the average number of families examined for each trait from 32.7 \u003cu\u003e+\u003c/u\u003e 0.5 to 65.6 \u003cu\u003e+\u003c/u\u003e 1.4. \u0026nbsp;Again, we found no significant difference between YN and Y1 female estimates. Male YN heritability estimates were greater than Y1 estimates for abdominal bristle number on the 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e sternites, and for left side sex comb number (Table 3). Further, the YN populations exhibited on average a 0.15 \u003cu\u003e+\u003c/u\u003e 0.08 (mean \u003cu\u003e+\u003c/u\u003e 95% CI) greater heritability than the Y1 populations for males, but only a 0.01 \u003cu\u003e+\u003c/u\u003e 0.05 greater heritability for females.\u003c/p\u003e\n\u003cp\u003eGenetic correlations between traits were estimated by first averaging individual left and right sides for sternopleural bristle number, sex comb number, and tibia length, as well as averaging the 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e abdominal sternites. We found that male and female correlations were similar, and no difference in the female correlations between YN and Y1 was detected (Table 4). We found that YN populations exhibited higher male genetic correlations between tibia length and sternopleural bristles, as well as tibia and sex comb number. Further, the male correlation between sex comb and abdominal bristle was low, suggesting that they are largely independent traits controlled by different genes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding how sexual dimorphism evolves despite a shared genome has been of great interest to evolutionary biologists (Dean and Mank, 2014). Traditionally, Y-chromosomes have been dismissed as unimportant contributors to dimorphism due to their heterochromatic and degenerate nature. However, this traditional view was questioned when \u003cem\u003eDrosophila\u003c/em\u003e Y-chromosomes were shown to influence the expression of hundreds of autosomal and X-linked genes (Lemos et al., 2008). Nevertheless, the potential for Y-chromosomes to contribute to sexually dimorphic evolution via Y-linked additive variation has gone largely unstudied. If Y-chromosomes contribute to a trait\u0026rsquo;s additive variation, they can help shape dimorphism, permitting the sexes to reach separate phenotypic optima. Here we show that populations with multiple Y-chromosomes (YN) exhibited higher male heritabilities compared to populations with a single Y-chromosome variant (Y1). Further, female heritabilities did not significantly differ between these populations (females served as controls since they do not possess Y-chromosomes). In short, our data suggest that Y-chromosomes contribute additive variation to two of the four traits examined here.\u003c/p\u003e\n\u003cp\u003eThe contribution of Y-linked additive variation to complex quantitative traits is somewhat unexpected, as theoretical models predict such variation is hard to maintain (Clark, 1987). Furthermore, previous empirical work suggests that Y-chromosome variation within \u003cem\u003eD. melanogaster\u003c/em\u003e populations may be largely sign epistatic (Chippindale and Rice, 2001; Kutch and Fedorka, 2017), which likely decreases trait heritability and can impair populations from quickly responding to selective pressures (Kutch \u0026amp; Fedorka, 2018). Interestingly, a recent study in seed beetles (\u003cem\u003eCallosobruchus maculatus\u003c/em\u003e) supports our finding. Specifically, Kaufmann and colleagues (2021) showed that Y-linked variation significantly contributed to the evolution of sexually dimorphic body size under artificial selection, despite being gene poor and highly heterochromatic. Thus, our findings, coupled with the latter study, suggest that Ys may play a significant role in the evolution of sexual dimorphism for a variety of traits.\u003c/p\u003e\n\u003cp\u003eAlthough we show evidence of Y-linked variation for complex traits, several minor caveats exist. Central to our design was the creation of populations with multiple Y-chromosomes (YN). However, it is unclear how many Y-chromosomes were maintained in the YN populations. Kutch and Fedorka (2017) collected 40 female \u003cem\u003eD. melanogaster\u003c/em\u003e from Orlando and, by examining Y-linked phenotypes, were able to determine at least 3 differing Y-chromosomes (many more were likely). Our current design collected a total of 351 female \u003cem\u003eD. simulans\u0026nbsp;\u003c/em\u003efrom Orlando, so it is reasonable to assume multiple Y-chromosomes are segregating in the YN populations. In addition, the tests for additive variation for each morphological character is not entirely independent, as non-zero genetic correlations exist between these traits. Tibia length, specifically, had male correlations as high as 0.44. However, abdominal bristle number and sex comb number, the two traits found to exhibit differences between YN-Y1 heritabilities, had low correlations suggesting they were relatively independent characters.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In summary, we report significantly higher heritabilities in YN populations compared to Y1 populations for abdominal bristle number and sex comb number. This suggests that Y-chromosomes contribute additive genetic variance for these traits. For abdominal bristle number, a trait shared by both sexes, this means that the Y-chromosome can help shape sexual dimorphism, aiding each sex in reaching their phenotypic optimum. When examining this study in the context of similar studies such as Kutch and Fedorka (2017, 2018) and Kaufmann and colleagues (2021), it appears that the type of variation the Y-chromosome contributes depends on the species, population, and trait examined. For instance, Y-chromosome variation may be additive in one population or for one trait, but largely epistatic elsewhere. Future research should examine the type of variation induced by the Y-chromosome across these variables. This would make clearer the evolutionary consequences of the Y-chromosome\u0026rsquo;s influence on hundreds of autosomal genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Megan Danis, Kevin Cedeno, Saara Rasool, Mariam Sleem, Karishma Santadasani, Connor McDonnald, and Naiomy Gonzalez Carrero for assistance with sample processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceived and designed by TMN and KMF, data collection was overseen by TMN and JB, data analysis was conducted by TMN and KMF, and the manuscript was authored by TMN and KMF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Archiving\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be archived in the Dryad data repository upon acceptance of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u0026Aring;slund, S. E., Holmgren, P., \u0026amp; Rasmuson, B. (1978). The effects of number of Y chromosomes on mating behaviour and bristle number in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Hereditas, 89(2), 249-254.\u003c/li\u003e\n \u003cli\u003eBarigozzi, C. (1951). The influence of the Y-chromosome on quantitative characters of \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Heredity, 5(3), 415-432.\u003c/li\u003e\n \u003cli\u003eBrown, E. J., Nguyen, A. H., \u0026amp; Bachtrog, D. (2020). The \u003cem\u003eDrosophila\u003c/em\u003e Y chromosome affects heterochromatin integrity genome-wide. Molecular biology and evolution, 37(10), 2808-2824.\u003c/li\u003e\n \u003cli\u003eBurgoyne, P. S. (1998). The mammalian Y chromosome: a new perspective. Bioessays, 20(5), 363-366. Capy, P., Pla, E., \u0026amp; D avid, J. R. (1993). Phenotypic and genetic variability of morphometrical traits in natural populations of \u003cem\u003eDrosophila melanogaster\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;D simulans\u003c/em\u003e. I. Geographic variations. Genetics Selection Evolution, 25(6), 517-536.\u003c/li\u003e\n \u003cli\u003eCarvalho, A. B., Vaz, S. C., \u0026amp; Klaczko, L. B. (1997). Polymorphism for Y-linked suppressors of sex-ratio in two natural populations of \u003cem\u003eDrosophila mediopunctata\u003c/em\u003e. Genetics, 146(3), 891-902.\u003c/li\u003e\n \u003cli\u003eCarvalho, A. B., Koerich, L. B., \u0026amp; Clark, A. G. (2009). Origin and evolution of Y chromosomes: \u003cem\u003eDrosophila\u003c/em\u003e tales. Trends in Genetics, 25(6), 270-277.\u003c/li\u003e\n \u003cli\u003eCarvalho, A. B., Vicoso, B., Russo, C. A., Swenor, B., \u0026amp; Clark, A. G. (2015). Birth of a new gene on the Y chromosome of \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Proceedings of the National Academy of Sciences, 112(40), 12450-12455.\u003c/li\u003e\n \u003cli\u003eCastillo, E. R., Marti, D. A., \u0026amp; Bidau, C. J. (2010). Sex and neo-sex chromosomes in Orthoptera: a review. Journal of Orthoptera Research, 213-231.\u003c/li\u003e\n \u003cli\u003eChippindale, A. K., \u0026amp; Rice, W. R. (2001). Y chromosome polymorphism is a strong determinant of male fitness in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Proceedings of the National Academy of Sciences, 98(10), 5677-5682.\u003c/li\u003e\n \u003cli\u003eClark, A. G. (1987). Natural selection and Y-linked polymorphism. Genetics, 115(3), 569-577.\u003c/li\u003e\n \u003cli\u003eClark, A. G. (1987). Variation in Y chromosome segregation in natural populations of \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Genetics, 115(1), 143-151.\u003c/li\u003e\n \u003cli\u003eClark, A. G. (1990). Two tests of Y chromosomal variation in male fertility of \u003cem\u003eDrosophila\u0026nbsp;\u003c/em\u003e\u003cem\u003emelanogaster\u003c/em\u003e. Genetics, 125(3), 527-534.\u003c/li\u003e\n \u003cli\u003eClark, A. G., Szumski, F. M., \u0026amp; Lyckegaard, E. M. (1991). Population genetics of the Y chromosome of \u003cem\u003eDrosophila melanogaster\u003c/em\u003e: rDNA variation and phenotypic correlates. Genetics Research, 58(1), 7-13.\u003c/li\u003e\n \u003cli\u003eDean, R., \u0026amp; Mank, J. E. (2014). The role of sex chromosomes in sexual dimorphism: discordance between molecular and phenotypic data. Journal of Evolutionary Biology, 27(7), 1443-1453.\u003c/li\u003e\n \u003cli\u003eFry, J. D., DeRonde, K. A., \u0026amp; Mackay, T. F. (1995). Polygenic mutation in \u003cem\u003eDrosophila\u0026nbsp;\u003c/em\u003e\u003cem\u003emelanogaster\u003c/em\u003e: genetic analysis of selection lines. Genetics, 139(3), 1293-1307.\u003c/li\u003e\n \u003cli\u003eEtges, W. J., Over, K. F., De Oliveira, C. C., \u0026amp; Ritchie, M. G. (2006). Inheritance of courtship song variation among geographically isolated populations of\u0026nbsp;\u003cem\u003eDrosophila mojavensis\u003c/em\u003e. Animal Behaviour, 71(5), 1205-1214.\u003c/li\u003e\n \u003cli\u003eHuttunen, S., \u0026amp; Aspi, J. (2003). Complex inheritance of male courtship song characters in \u003cem\u003eDrosophila virilis\u003c/em\u003e. Behavior genetics, 33(1), 17-24.\u003c/li\u003e\n \u003cli\u003eJaenike, J. (1999). Suppression of sex‐ratio meiotic drive and the maintenance of Y‐chromosome polymorphism in \u003cem\u003eDrosophila\u003c/em\u003e. Evolution, 53(1), 164-174.\u003c/li\u003e\n \u003cli\u003eJiang, P. P., Hartl, D. L., \u0026amp; Lemos, B. (2010). Y not a dead end: epistatic interactions between Y-linked regulatory polymorphisms and genetic background affect global gene expression in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Genetics, 186(1), 109-118.\u003c/li\u003e\n \u003cli\u003eJoly, D., Bazin, C., Zeng, L. W., \u0026amp; Singh, R. S. (1997). Genetic basis of sperm and testis length differences and epistatic effect on hybrid inviability and sperm motility between \u003cem\u003eDrosophila simulans\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;D. sechellia\u003c/em\u003e. Heredity, 78(4), 354-362.\u003c/li\u003e\n \u003cli\u003eKaufmann, P., Wolak, M. E., Husby, A., \u0026amp; Immonen, E. (2021). Rapid evolution of sexual size dimorphism facilitated by Y-linked genetic variance. Nature Ecology \u0026amp; Evolution, 1-9.\u003c/li\u003e\n \u003cli\u003eKutch, I. C., \u0026amp; Fedorka, K. M. (2015). Y-linked variation for autosomal immune gene regulation has the potential to shape sexually dimorphic immunity. Proceedings of the Royal Society B: Biological Sciences, 282(1820), 20151301.\u003c/li\u003e\n \u003cli\u003eKutch, I. C., \u0026amp; Fedorka, K. M. (2017). A test for Y‐linked additive and epistatic effects on surviving bacterial infections in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e. Journal of evolutionary biology, 30(7), 1400-1408.\u003c/li\u003e\n \u003cli\u003eKutch, I. C., \u0026amp; Fedorka, K. M. (2018). Y-chromosomes can constrain adaptive evolution via epistatic interactions with other chromosomes. BMC evolutionary biology, 18(1), 1-8.\u003c/li\u003e\n \u003cli\u003eLemos, B., Araripe, L. O., \u0026amp; Hartl, D. L. (2008). Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. science, 319(5859), 91-93.\u003c/li\u003e\n \u003cli\u003eLemos, B., Branco, A. T., \u0026amp; Hartl, D. L. (2010). Epigenetic effects of polymorphic Y chromosomes modulate chromatin components, immune response, and sexual conflict. Proceedings of the National Academy of Sciences, 107(36), 15826-15831.\u003c/li\u003e\n \u003cli\u003eMacdonald, S. J., \u0026amp; Goldstein, D. B. (1999). A quantitative genetic analysis of male sexual traits distinguishing the sibling species \u003cem\u003eDrosophila simulans\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;D. sechellia\u003c/em\u003e. Genetics, 153(4), 1683-1699.\u003c/li\u003e\n \u003cli\u003eMather, K. (1944). The genetical activity of heterochromatin. Proceedings of the Royal Society of London. Series B-Biological Sciences, 132(868), 308-332.\u003c/li\u003e\n \u003cli\u003eMiller, G. T., Starmer, W. T., \u0026amp; Pitnick, S. (2003). Quantitative genetic analysis of among-population variation in sperm and female sperm-storage organ length in \u003cem\u003eDrosophila mojavensis\u003c/em\u003e. Genetics Research, 81(3), 213-220.\u003c/li\u003e\n \u003cli\u003eMontchamp‐Moreau, C., Ginhoux, V., \u0026amp; Atlan, A. (2001). The Y chromosomes of\u0026nbsp;\u003cem\u003eDrosophila simulans\u003c/em\u003e are highly polymorphic for their ability to suppress sex‐ratio drive. Evolution, 55(4), 728-737.\u003c/li\u003e\n \u003cli\u003ePolak, M., \u0026amp; Starmer, W. T. (2005). Environmental origins of sexually selected variation and a critique of the fluctuating asymmetry-sexual selection hypothesis. Evolution, 59(3), 577-585.\u003c/li\u003e\n \u003cli\u003eRoff, D. A. (1997). Evolutionary Quantitative Genetics. Chapman and Hall. DOI:10.1007/978-1-4615-4080-9\u003c/li\u003e\n \u003cli\u003eRohmer, C., David, J. R., Moreteau, B., \u0026amp; Joly, D. (2004). Heat induced male sterility in\u0026nbsp;\u003cem\u003eDrosophila melanogaster\u003c/em\u003e: adaptive genetic variations among geographic populations and role of the Y chromosome. Journal of Experimental Biology, 207(16), 2735-2743.\u003c/li\u003e\n \u003cli\u003eStalker, H. D. (1961). The genetic systems modifying meiotic drive in \u003cem\u003eDrosophila\u0026nbsp;\u003c/em\u003e\u003cem\u003eparamelanica\u003c/em\u003e. Genetics, 46(2), 177.\u003c/li\u003e\n \u003cli\u003eStoltenberg, S. F., \u0026amp; Hirsch, J. (1997). Y-chromosome effects on \u003cem\u003eDrosophila\u003c/em\u003e geotaxis interact with genetic or cytoplasmic background. Animal behaviour, 53(4), 853-864.\u003c/li\u003e\n \u003cli\u003eVia, S. (1984). The quantitative genetics of polyphagy in an insect herbivore. II. Genetic correlations in larval performance within and among host plants. Evolution, 896-905.\u003c/li\u003e\n \u003cli\u003eWolf, J. B., Brodie, E. D., \u0026amp; Wade, M. J. (Eds.). (2000). Epistasis and the evolutionary process. Oxford University Press.\u003c/li\u003e\n \u003cli\u003eZar, J. H. Biostatistical Analysis 2nd edn, 324 (Prentice Hall, Englewood Cliffs, New Jersey, 1984)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Y-linked effect studies in \u003cem\u003eDrosophila\u003c/em\u003e.\u003c/strong\u003e Definition of terms: (i) Y-chromosome and Background source - An isoline is an isogenic or nearly isogenic fly strain that contains fixed genetic elements such as a single Y or X chromosome. A stock is a laboratory population that likely contains genetic variation including multiple Ys, Xs, and autosomes. A population is a stock recently derived form a natural location for the purpose of characterizing the individuals in that location. (ii) Background variation - Considered isogenic if derived from an isofemale line or if 1 or more X / autosomes were isogenic. Considered heterozygous if comprised of 2 isolines. If the authors did not explicitly state its condition, the background was listed as \u0026ldquo;unknown\u0026rdquo;. (iii) Y x B Effect - NA refers to the Y by background interaction not being formally assessed by the authors or difficult to infer from the reported data. * Y-linked effects were statistically significant only within isolated backgrounds but were not significant when examined across backgrounds. \u003cem\u003eDrosophila\u003c/em\u003e species are as follows: mel = \u003cem\u003eD. melanogaster\u003c/em\u003e, par = \u003cem\u003eD. paramelanica\u003c/em\u003e, med = \u003cem\u003eD. mediopunctata\u003c/em\u003e, sim = \u003cem\u003eD. simulans\u003c/em\u003e, qui = \u003cem\u003eD. quinarian\u003c/em\u003e, moj = \u003cem\u003eD. mojavensis\u003c/em\u003e, vir = \u003cem\u003eD. virilis\u003c/em\u003e, bip = \u003cem\u003eD. bipectinata\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eY-Chrom Source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003eBackground Source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eBackground Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eCo-evolved Y and B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eY-Linked Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eY x B Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSternopleural Bristle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e12 Ys from 12 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eMather 1944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eWing hair; corneae size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e4 Ys from 4 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eBarigozzi 1951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex-ratio suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e50 Ys from 50 isolines across 16 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e11 isolines across 7 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eHeterozygous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003emixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003epar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eStalker 1961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eMating behavior; Bristle number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e3 Ys from 2 isolines to create 4 genotypes: X0, XY, XYY, XYYY\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eisogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eAslund et al. 1978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex-ratio suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e36 Ys from 36 isolines\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eClark 1987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eFertility; sperm competition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e36 Ys from 36 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eClark 1990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eBristle length; development time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e36 Ys from 36 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eClark 1991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eAbdominal and sternopleural bristle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e12 Ys from 12 selection lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003eSelected and isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eheterozygous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eFry et al. 1995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex-ratio suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e25 Ys across 2 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eheterozygous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eCarvalho et al. 1997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eGeotaxis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e2 Ys from 2 selection lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 selection lines and 2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eStoltenberg et al. 1997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSperm length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # Ys from 2 hybridizing species stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 species stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003esim\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eJoly et al. 1997\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex-ratio suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e61 Ys across 3 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e6 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eisogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003equi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eJaenike 1999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex-ratio suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e107 Ys across 6 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eisogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003esim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eMontchamp-Moreau 2001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eMale fitness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e20 Ys from 1 stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e3 isolines lines, 1 stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003evariable across lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eNo*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eChippindale et al. 2001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSperm length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # Ys across 2 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emoj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eMiller et al. 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eCourtship song\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # of Ys from 2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003evir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eHuttunen et al. 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eHeat-induced sterility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # of Ys from 2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eRohmer et al. 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eSex comb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e2 Ys from 2 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 Isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic and heterozygous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003ebip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003ePolak et al. 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eCourtship song\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # Ys from 13 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 stocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emoj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eEtges et al. 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eGenome-wide regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e5 Ys from 5 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eLemos et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003ePEV, gene regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e16 Ys from 16 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eLemos et al. 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003ePEV, gene regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003eUnknown # Ys from 2 populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e2 stocks and 1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eUnknown / isogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eJiang et al. 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eLifespan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e33 Ys from 33 isolines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 isoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eGriffen et al. 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eImmune Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e30 Ys from 30 isolines across 1 population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e1 stock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003eIsogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eKutch et al. 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.21656050955414%\"\u003e\n \u003cp\u003eImmune Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.101910828025478%\"\u003e\n \u003cp\u003e4 Ys from 4 isolines across 1 population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\"\u003e\n \u003cp\u003e4 isolines across 1 population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.535031847133759%\"\u003e\n \u003cp\u003evariable across lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.713375796178344%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.07643312101911%\"\u003e\n \u003cp\u003eNo*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.802547770700637%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.394904458598726%\"\u003e\n \u003cp\u003emel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\"\u003e\n \u003cp\u003eKutch et al. 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Sex-specific trait heritabilities for each treatment (YN and Y1) and replicate population (R1 and R2). 95% confidence intervals for each heritability estimate appears in parentheses. YN-Y1 represents the difference in treatment heritabilities. YN\u0026ne;Y1 represents the statistical assessment of the treatment difference.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003eY1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003eYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003eYN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003eY1\u0026ne;YN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.428571428571427%\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.571428571428571%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.80952380952381%\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.19047619047619%\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn;95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20%\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn;95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eSternopleural Bristle - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.34 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.54 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.35 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.37 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.22 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.31 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.49 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.35 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eSternopleural Bristle - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.28 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.47 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.38 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.47 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.38 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.37 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.52 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.39 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eAbdominal Bristle - 4th Sternite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.25 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.62 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.36 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.51 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.37 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.37 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.37 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.26 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eAbdominal Bristle - 5th Sternite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.18 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.53 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.31 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.57 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.31 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.26 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.39 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.43 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eTibia Length - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e1.05 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.77 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.66 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.91 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.67 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.71 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.48 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.72 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eTibia Length - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.97 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.71 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.53 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.96 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.470588235294118%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.58823529411765%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.941176470588236%\"\u003e\n \u003cp\u003e0.67 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e0.66 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.764705882352942%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.470588235294118%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.45 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.57 (0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eSex Comb - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.28 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.32 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.07 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.45 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"23.6983842010772%\"\u003e\n \u003cp\u003eSex Comb - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"6.4631956912028725%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003e0.27 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.14 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.308483290488432%\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.87917737789203%\"\u003e\n \u003cp\u003e0.29 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.41 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"23.6983842010772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.4631956912028725%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"15.978456014362656%\"\u003e\n \u003cp\u003eAverage of YN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e0.14 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.205128205128204%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6923076923076925%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.88034188034188%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.94871794871795%\"\u003e\n \u003cp\u003e0.01 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.324786324786325%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"23.6983842010772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.4631956912028725%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"14.183123877917415%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.978456014362656%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e* P\u0026lt;0.05, ** P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.515260323159785%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable \u003cem\u003e3\u003c/em\u003e.\u003c/strong\u003e Male and female trait heritabilities for YN and Y1 with replicates combined into a single population.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003eY1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003eYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003eYN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003eY1\u0026ne;YN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"39.285714285714285%\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.714285714285714%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25%\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn;95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25%\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e (\u0026plusmn;95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eSternopleural Bristle - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.34 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.41 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.34 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.34 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eSternopleural Bristle - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.30 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.45 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.45 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.43 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eAbdominal Bristle - 4th Sternite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.22 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.45 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.39 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.32 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eAbdominal Bristle - 5th Sternite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.21 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.54 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.35 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.27 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eTibia Length - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.72 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.71 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.51 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.69 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"27.906976744186046%\"\u003e\n \u003cp\u003eTibia Length - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.64 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.77 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.557184750733137%\"\u003e\n \u003cp\u003e♀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.54 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.57 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.633431085043988%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.542521994134898%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\"\u003e\n \u003cp\u003eSex Comb - Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.19 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.42 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.906976744186046%\"\u003e\n \u003cp\u003eSex Comb - Right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6109936575052854%\"\u003e\n \u003cp\u003e♂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.31 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.38 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.906976744186046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6109936575052854%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003eAverage of YN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e0.15 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"33.933161953727506%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.254498714652957%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.59383033419023%\"\u003e\n \u003cp\u003e0.01 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.624678663239074%\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.906976744186046%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.6109936575052854%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.758985200845665%\"\u003e\n \u003cp\u003e* P\u0026lt;0.05, ** P\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.20507399577167%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Male and female genetic correlations between traits. SPB = sternopleural bristle, AB = abdominal bristle, TL = tibia length, SC = sex comb.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"9.696969696969697%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.696969696969697%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"39.84848484848485%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" width=\"40.75757575757576%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eY1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN\u0026ne;Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eY1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN-Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYN\u0026ne;Y1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.15 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.18 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.14 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.19 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.20 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.16 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.15 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.24 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.46 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.53 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.44 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.43 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.09 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.11 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.13 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.24 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.37 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"heritability, Y-chromosome, sexual dimorphism, epistasis\t","lastPublishedDoi":"10.21203/rs.3.rs-1879119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1879119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eDrosophila\u003c/em\u003e Y-chromosome variation has been shown to influence numerous phenotypes and the expression of hundreds of genes throughout the genome. However, it is currently unknown if Y-chromosome variation can adaptively shape male traits. Previous theoretical work suggests additive variation necessary for adaptive evolution is difficult to maintain among Y-chromosomes within populations, and previous empirical work has revealed only Y-linked epistatic variation, which can impede adaptive evolution. To assess the impact this Y-linked variation may have on adaptive evolution, we established replicate populations in \u003cem\u003eD. simulans\u003c/em\u003e containing either multiple Y-chromosome variants (YN populations) or a single Y-chromosome variant (Y1 populations) and estimated male and female heritabilities for sternopleural bristles number, abdominal bristles number, sex comb teeth number, and tibia length; traits previously shown to be influenced by Y-chromosomes. If Y-chromosome variation is additive, we expected YN populations to exhibit greater heritability than Y1. If variation is largely sign epistatic, we expected YN populations to exhibit reduced heritability. To minimize genetic dissimilarities between YN and Y1 populations, autosomal, X-linked, and mitochondrial allele frequencies were homogenized within YN/Y1 replicate pairs. Female heritability estimates served as controls and not expected to differ. We found that male YN populations exhibited greater heritability than Y1 for sternopleural bristle and sex comb tooth number, while female YN and Y1 population estimates showed no difference. These data suggest Y-chromosomes can adaptively shape male traits by contributing additive genetic variation. Further, Y-chromosomes may influence the evolution of sexual dimorphism by shaping male traits shared by both sexes.\u003c/p\u003e","manuscriptTitle":"Y-linked additive variation for quantitative traits in Drosophila simulans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-03 14:29:52","doi":"10.21203/rs.3.rs-1879119/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2022-09-02T11:06:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-08-29T20:53:58+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-08-15T22:05:04+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-08-15T19:56:35+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-08-11T15:06:16+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-08-11T07:29:55+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-07-29T12:37:16+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2022-07-28T13:30:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heredity","date":"2022-07-20T18:21:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-20T18:21:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a79a5300-dc56-4a00-a6bd-d211b12965dd","owner":[],"postedDate":"August 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-01-30T11:12:01+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-03 14:29:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1879119","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1879119","identity":"rs-1879119","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}