What are the consequences to heredity from crossing-over?
Abstruse
Meiotic recombination is a genetic process that is critical for proper chromosome segregation in many organisms. Despite being central for organismal fettle, rates of crossing over vary profoundly betwixt taxa. Both genetic and ecology factors contribute to phenotypic variation in crossover frequency, as do genotype–environment interactions. Hither, we test the hypothesis that maternal age influences rates of crossing over in a genotypic-specific manner. Using classical genetic techniques, we estimated rates of crossing over for individual Drosophila melanogaster females from five strains over their lifetime from a unmarried mating outcome. We detect that both age and genetic background significantly contribute to observed variation in recombination frequency, as do genotype–age interactions. We further find differences in the effect of age on recombination frequency in the two genomic regions surveyed. Our results highlight the complexity of recombination rate variation and reveal a new role of genotype by maternal age interactions in mediating recombination rate.
Meiotic recombination is a critically of import biological process, equally chromosomal crossovers are required for proper chromosome segregation in many organisms (Roeder 1997). Defects in meiotic recombination can take detrimental consequences, including increasing the probability of nondisjunction ( Koehler et al. 1996; Hassold and Hunt 2001). The exchange of genetic cloth associated with crossing over tin have important evolutionary consequences by combining or separating beneficial or deleterious alleles. Given the central importance of recombination for organismal fitness, i might hypothesize that this process would be highly regulated, with footling to no variation present. Withal, a wealth of evidence in a variety of taxa points to the reverse. Variation in rates of recombination have been identified in yeast ( Mancera et al. 2008), worms ( Barnes et al. 1995; Rockman and Kruglyak 2009), fruit flies (Brooks and Marks 1986; Singh et al. 2009, 2013; Comeron et al. 2012), honey bees ( Ross et al. 2015), maize ( Bauer et al. 2013), chickens (Rahn and Solari 1986), mice ( Dumont et al. 2009), chimpanzees ( Ptak et al. 2005; Winckler et al. 2005), and humans ( Kong et al. 2002; Crawford et al. 2004; Myers et al. 2005).
Though at least some of this variation is due to differences amongst genotypes, information technology has long been known that recombination rates are phenotypically plastic. That is, a given genotype has the capability to exhibit dissimilar phenotypes in response to unlike environmental atmospheric condition. For case, various types of stress have been associated with plastic increases in recombination rate, such as mating ( Priest et al. 2007), nutrition (Neel 1941), parasitism ( Singh et al. 2015), social stress (Belyaev and Borodin 1982), and temperature (Plough 1917, 1921; Stern 1926; Smith 1936; Grushko et al. 1991).
The event of historic period on recombination rate has been investigated in some item. This is likely because aging is a ubiquitous procedure, and one with often detrimental consequences. Indeed, for many organisms, advancing age is accompanied by a subtract in overall fitness (Williams 1957; Partridge and Barton 1993) and also a subtract in overall reproductive output (Stearns 1992). Many studies take examined how recombination changes with advancing maternal age in Drosophila (Bridges 1915, 1927, 1929; Plough 1917, 1921; Stern 1926; Bergner 1928; Neel 1941; Hayman and Parsons 1960; Redfield 1966; Lake and Cederberg 1984; Parsons 1988; Chadov et al. 2000; Priest et al. 2007; Tedman-Aucoin and Agrawal 2011; Stevison 2012; Manzano-Winkler et al. 2013; Hunter and Singh 2014). This topic has been investigated in other species as well, such as worms (Rose and Baillie 1979), tomatoes (Griffing and Langridge 1963), mice and hamsters (Henderson and Edwards 1968; Sugawara and Mikamo 1983), and humans ( Kong et al. 2004; Coop et al. 2008; Hussin et al. 2011; Bleazard et al. 2013; Rowsey et al. 2014; Campbell et al. 2015; Martin et al. 2015).
In spite of the depth of research on this topic, a clear picture of how maternal age affects rates of recombination has all the same to emerge. In humans, for example, while some studies show fewer crossovers over time (i.e., Kong et al. 2004; Hussin et al. 2011), others show more crossovers over time (i.e., Tanzi et al. 1992; Bleazard et al. 2013; Martin et al. 2015). The Drosophila literature shows like discrepancies, with some studies showing clear increases in crossover frequency with increasing maternal historic period (i.e., Bridges 1915; Stern 1926; Bergner 1928; Lake and Cederberg 1984; Priest et al., 2007; Hunter and Singh 2014), others showing decreases (i.e., Bridges 1915; Hayman and Parsons 1960; Chadov et al., 2000), some revealing nonlinear furnishings (i.eastward., Plough, 1917, 1921; Bridges 1927; Neel 1941; Redfield 1966; Tedman-Aucoin and Agrawal 2011), and others yet finding no significant changes in recombination rates (i.e., Bridges 1915; Plough 1921; Stevison 2012; Manzano-Winkler et al., 2013).
It has proven difficult to compare these studies for a variety of reasons, fifty-fifty within a single arrangement such as Drosophila. First, many different strains accept been employed in the above experiments, and it is not even so articulate whether the effects of maternal age on recombination frequency are dependent on genetic background. Other factors, such every bit repeated mating, which may bear upon rates of crossing over in Drosophila ( Priest et al. 2007), have not been controlled for in all studies, further complicating the interpretation of previous data. Experimental pattern differs among studies besides, with some studies assaying recombination from single females while others analysis recombination from a pool of females; this too may contribute to the observed differences in the effects of maternal age on recombination among studies. Finally, different regions of the genome have been surveyed, and information technology is possible that the upshot of maternal age on recombination rate is not uniform across the genome.
The goal of this report is to test the hypothesis that the effects of maternal age on recombination rate are genotype and/or locus-specific. Demonstrating genotype-by-age interaction furnishings or genomic heterogeneity in the magnitude/direction of historic period-associated changes in recombination rate is a disquisitional first pace in quantifying the extent of such effects and determining their genetic basis. To exam for genotype–age interaction and locus-specific effects, we used multiple wild-type lines of Drosophila melanogaster and measured recombination rates of individual females for a period of 3 wk afterward a unmarried mating event. This study estimated crossover rates in two different genomic locations. Nosotros find an increase of recombination rates with increasing maternal age on the X chromosome, though no significant historic period-dependency in recombination frequency on chromosome 3R. Our written report confirms genotype-specific variation in recombination rate, and indicates that the effects of maternal age are indeed genotype-dependent. We also find a pregnant locus by maternal age effect, which suggests that age-related changes in recombination rate are likely to be variable across the genome. Our work establishes that it is important to control for genetic background effects when examining the effects of environmental factors on rates of crossing over. Nosotros predict that genotype–environment interaction furnishings on crossover rates are pervasive in other species as well.
Materials and Methods
Fly lines
V inbred wild-type strains of Drosophila were used in this study from the D. melanogaster Genetic Reference Panel (DGRP) ( Mackay et al. 2012; Huang et al. 2014). The five lines were RAL_21, RAL_59, RAL_73, RAL_75, and RAL_136. 4 of the lines are gratuitous of chromosomal inversions and have the standard karyotype, while one (RAL_136) is heterozygous for both the Mourad inversion on 3L and the Kodani inversion on 3R ( Huang et al. 2014). Information technology should exist noted that because of these inversions, RAL_136 was non used for estimating rates of recombination using markers on 3R. These lines were previously used in a study by the authors and were shown to exist significantly genetically variable for crossover rates (Hunter and Singh 2014).
To measure rates of recombination, we employed a classical genetic crossing scheme using recessive visible markers. The markers used to measure recombination on the X chromosome were yellow (y 1) and vermilion (v one) (Bloomington Drosophila Stock Middle #1509), which are 33 cM autonomously (Morgan and Bridges 1916), integrated into a wild-blazon isogenic Samarkand genetic groundwork ( Lyman et al. 1996); this line abbreviated time to come as 'y v.' The markers on the 3R chromosome were ebony (e iv) and rough (ro 1) (Bloomington Drosophila Stock Center #496), which are 20.iv cM apart (Bridges and Morgan 1923); this line is abbreviated hereafter every bit 'due east ro.' These markers were selected due to their genetic distance, ease of scoring, and lack of viability defects. To analysis rates of nondisjunction, we used a multiply marked fly strain. The total genotype of this strain is y cv v f / T(i:Y)BS ( Kohl et al. 2012).
Experimental crosses
All crosses were executed at 25° with a 12 hr:12 hr lite:dark bike on standard cornmeal-molasses media. To score crossover frequency, nosotros used a 2-step crossing scheme (Supplemental Material, Figure S1). For the commencement cross, xx virgin DGRP females were mated to 20 doubly-marked males for 5 d in eight ounce (oz.) bottles (doubly-marked males are denoted by thou 1 m two for simplicity and refer to either y five males or eastward ro males). Afterwards five d, parental flies were removed. Virgin F1 females (+ +/ chiliad 1 m 2) were collected within a 2 hr period between viii am and 10 am on the aforementioned day for all lines and held virgin for 24 60 minutes in groups of 20. Twenty virgin females were mass-mated with 20 males in eight oz. bottles for a flow of 24 hr (for flies mated to y v males) or for 48 hr (for flies mated to e ro males). Flies used for the e ro cross produced very few gravid females in a first trial of a 24 hr window, necessitating the longer mating window. Due to the credible effect of repeated mating on rates of recombination ( Priest et al. 2007), we limited females to mating attempts just in the short window of 24–48 hr. This short window allows for roughly 1 mating event since females become unresponsive to remating for roughly 1 d subsequently copulation (Manning 1962, 1967; Gromko et al. 1984). Drosophila females are able to shop sperm for periods greater than 2 wk (Kaufman and Demerec 1942; Lefevre and Jonsson 1962) and then all progeny nerveless are the event of mating within that original 24–48 hr window. After mating, individual females were placed into vials and transferred every 2 d at the same time of mean solar day for 22 d. We conducted this experiment twice; one time for the y 5 marker pair and in one case for the eastward ro marking pair. For y v, 150 replicate females were used for each line. For e ro, 175 replicate females were used for each line. The resulting progeny from each vial were scored for both sexual activity and the presence of morphological markers. Recombinant progeny were identified by the presence of but one visible mark (recombinant genotypes are m 1 + or + m two). Table S1 and Table S2 ontain progeny counts from individual females for each phenotype class from each day in each interval. Table S3 summarizes these data across lines for a given time point and interval.
To assay rates of nondisjunction, we used a elementary crossing scheme (Figure S2). All crosses were executed at 25° with a 12 hr:12 hr light:dark bike on standard media using virgin females anile roughly 24 60 minutes. For the cross, 10 or xx (depending on how many virgins eclosed on a given day) virgin females from each line were crossed to the same number of y cv 5 f / T(1:Y)BSouthward males in viii oz. bottles. Males and females were transferred to fresh bottles every five d for a total of 25 d. All progeny were collected and scored for both sex activity and presence/absence of Bar (BS) eyes. Females displaying Bar eyes or males displaying wild-type eyes indicated a nondisjunction upshot. The total number of nondisjunction progeny observed was multiplied by two to business relationship for triplo-X and nullo-X progeny, which are lethal (and thus not observable). Table S4 summarizes these data across lines for a given time bespeak and interval.
Statistics
All statistics were conducted using JMPPro v11.0.0 and/or R v3.2.0 unless otherwise noted. We used a repeated measures ANOVA (Winer 1971) on arcsine square root transformed data and tested for the furnishings of maternal age, genetic groundwork, and the interaction betwixt these factors. The full model is every bit follows:
where R represents (transformed) crossover frequency, μ represents the mean of regression, ε represents the individual mistake, and ρ represents the residual error. Grand represents female genetic groundwork, A represents maternal historic period, and I(K×A) represents the interaction of the two. Each of these terms was modeled as a fixed issue. For the repeated measures ANOVA, nosotros restricted our analysis to days 1–12 for the interval on the Ten chromosome, considering the number of progeny produced markedly decreased later 24-hour interval 12 (over a threefold subtract comparing the average of days 1–12 to the average of days 14–22; Tabular array S3). Similarly, we limited our assay to days 1–10 for the interval on 3R for the same reason ( Table S3).
Additionally, we used a generalized linear model with a binomial distribution and logit link function on the proportion of progeny that are recombinant. Nosotros treated each offspring every bit a realization of a binomial procedure (either recombinant or nonrecombinant), summarized the information for a given vial by the number of recombinants and the number of trials (total number of progeny per vial), and tested for an consequence of age, genetic background, and the interaction of the 2. The full model was as follows:
where Y represents the proportion of progeny that is recombinant, μ represents the mean of regression, and ε represents the error. Over again, G represents female genetic background, A represents maternal age, and I(One thousand×A) represents the interaction of the two, all modeled every bit fixed effects.
To test for locus furnishings, we used the aforementioned generalized linear model every bit detailed higher up, (in one case once again, with a binomial distribution and logit link function) to test for an issue of age, genetic background, and besides locus, too as all possible interactions. The full model is as follows:
where Y represents the proportion of recombination progeny and μ represents the mean of regression. G represents female genetic background, A represents maternal historic period, and L represent locus assayed (either y v or east ro), all modeled as a fixed effects, along with all interaction terms. Data points included three maternal ages (days ii, four, and 6–ten) for both loci.
We used a generalized linear model with a binomial distribution and logit link function to test for an effect of historic period, genetic background, besides every bit the interaction of the 2 on the proportion of progeny that are aneuploid. We treated each offspring as a realization of a binomial process (euploid vs. aneuploid), and summarized the data for a given bottle by the number of aneuploid progeny (multiplied by two to business relationship for triplo-X and nullo-X progeny which are lethal) and the number of trials (full number of progeny per canteen plus unobservable lethal progeny). The total model was as follows:
where Y represents the proportion of aneuploid progeny, μ represents the mean of regression, and ε represents the error. G represents female genetic groundwork, modeled as a fixed effect, and A represents maternal historic period, besides modeled as a stock-still effect, along with the interaction of the two (I(Chiliad×A) ).
Data availability
The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article.
Results and Discussion
Robustness of crossover frequency estimation
In full, we scored 105,378 progeny for both intervals combined (78,292 for the y v interval and 27,086 for the e ro interval). Nosotros performed G-tests for goodness of fit (Sokal and Rohlf 1994) on our combined information to validate that the correct proportions of females vs. males, wild-blazon vs. one thousand 1 m two, and thousand ane + vs. + thou 2 were being recovered. It is expected that each of these pairs will exist recovered in a ane:1 ratio due to Mendelian segregation. Comparing females vs. males for the y five interval, only i out of 613 replicates showed a pregnant deviation from the 1:1 ratio (Bonferroni-corrected P = 0.05, G-examination) while for the eastward ro interval, 0 out of 467 replicates showed a significant deviation from the 1:1 ratio (Bonferroni-corrected P > 0.05, all comparisons, G-examination). Comparing wild-type vs. m 1 g two (progeny with both markers) in the y v interval, 6 out of 613 replicates showed a meaning deviation from the expected one:1 ratio (Bonferroni-corrected P < 0.05, G-test), while for the eastward ro interval, none of the replicates showed a pregnant deviation from the 1:ane ratio (Bonferroni-corrected P > 0.05, all comparisons, Yard-test). Comparing the ratio of recombinant progeny (1000 1 + vs. + thousand 2), none of the replicates showed a significant deviation from the expected one:1 ratio for the either the y v or e ro interval (Bonferroni-corrected P > 0.05, all comparisons, G-test). These results signal that there is no viability defect associated with any of the mutations used in the current study and gives us confidence that our estimates of crossover are robust.
Interaction of genetic background and maternal age
The chief motivation for this study was to make up one's mind how crossover frequency varies in relation to genetic backgrounds, advancing maternal age, and the interaction of the two. Although work has shown that meiotic nondisjunction increases with maternal historic period in Drosophila (using oocytes aged ∼4 d; Jeffreys et al. 2003; Subramanian and Bickel 2008, 2009; Weng et al. 2014), the nature of the relationship between recombination rate and maternal age is less clear. As described earlier, increases, decreases, nonlinear, and no changes in rates of recombination with increasing maternal age have all been observed previously.
Nosotros used a repeated measures ANOVA to examination for significant furnishings of genetic background, maternal age, and the interaction of age and genotype on recombination frequency data from individual females. Repeated measures ANOVA are uniquely well-suited to the longitudinal construction of our information—recombination rate measurements from the aforementioned individuals at multiple timepoints. Although our residuals after model-plumbing fixtures show significant deviations from normality (P = 0.01, Kolmogorov–Smirnov test), ANOVAs are robust fifty-fifty when assumptions are of the model are violated ( Glass et al. 1972; Schmider et al. 2010). Thus, a repeated measures ANOVA is an appropriate framework in which to analyze these information, given our focus on the role of historic period on recombination charge per unit. Even so, we couple this approach with an additional blazon of assay (see below) to ensure that our findings are robust.
For the y v region data (up to 12 d; see Materials and Methods), the repeated measures ANOVA reveals that genetic background (F 4,302 = ten.86; P < 0.001; Table 1) significantly contributes to the recombination charge per unit observed in our study. This is consistent with previous work in Drosophila, which has also highlighted a role of genetic variation in mediating crossover frequency both within the DGRP lines specifically ( Comeron et al. 2012; Hunter and Singh 2014; Hunter et al. 2016) likewise as in Drosophila in general (Chinnici 1971a,b; Brooks and Marks 1986; Comeron et al. 2012). Moreover, the magnitude of variation in recombination rate that we observe across lines (∼i.6-fold in the current report; Figure 1) is consistent with the magnitude of interstrain variability in Drosophila (∼1.3-fold; Brooks and Marks 1986; Hunter and Singh 2014; Hunter et al. 2016) A role for genetic background in recombination rate variation is seen in other species likewise, including mice (e.g., Dumont et al. 2009; Dumont and Payseur 2011) and humans (e.1000., McVean et al. 2004; Fearnhead and Smith 2005; Graffelman et al. 2007; Kong et al. 2010).
Results from repeated measures ANOVA to test for pregnant furnishings of genetic groundwork (line), age, and their interaction on crossover frequency in the 2 intervals assayed
Table 1
Results from repeated measures ANOVA to test for significant effects of genetic groundwork (line), age, and their interaction on crossover frequency in the two intervals assayed
| Chromosome | Source | df | SS | MS | F-Value | Prob > F |
|---|---|---|---|---|---|---|
| X | Line | four | 1.34 | 0.34 | x.25 | < 0.001 |
| Residuals | 305 | 9.96 | 0.033 | |||
| Maternal age | 1 | one.32 | 1.32 | 54.19 | < 0.001 | |
| Line × maternal age | 4 | 0.66 | 0.17 | 6.78 | < 0.001 | |
| Residuals | 1855 | 45.xix | 0.024 | |||
| 3R | Line | ii | 0.0011 | 0.00059 | 0.033 | 0.97 |
| Residuals | 15 | 0.27 | 0.018 | |||
| Maternal age | 1 | 0.046 | 0.046 | 2.93 | 0.097 | |
| Line × maternal historic period | 2 | 0.069 | 0.0035 | 0.22 | 0.80 | |
| Residuals | 33 | 0.52 | 0.016 |
| Chromosome | Source | df | SS | MS | F-Value | Prob > F |
|---|---|---|---|---|---|---|
| X | Line | 4 | 1.34 | 0.34 | x.25 | < 0.001 |
| Residuals | 305 | 9.96 | 0.033 | |||
| Maternal age | 1 | i.32 | i.32 | 54.xix | < 0.001 | |
| Line × maternal age | 4 | 0.66 | 0.17 | 6.78 | < 0.001 | |
| Residuals | 1855 | 45.nineteen | 0.024 | |||
| 3R | Line | 2 | 0.0011 | 0.00059 | 0.033 | 0.97 |
| Residuals | xv | 0.27 | 0.018 | |||
| Maternal age | 1 | 0.046 | 0.046 | 2.93 | 0.097 | |
| Line × maternal historic period | 2 | 0.069 | 0.0035 | 0.22 | 0.eighty | |
| Residuals | 33 | 0.52 | 0.016 |
df, degrees of freedom; SS, sum of squares; MS, mean square.
Table 1
Results from repeated measures ANOVA to test for significant effects of genetic background (line), age, and their interaction on crossover frequency in the 2 intervals assayed
| Chromosome | Source | df | SS | MS | F-Value | Prob > F |
|---|---|---|---|---|---|---|
| X | Line | four | 1.34 | 0.34 | 10.25 | < 0.001 |
| Residuals | 305 | nine.96 | 0.033 | |||
| Maternal age | 1 | one.32 | i.32 | 54.xix | < 0.001 | |
| Line × maternal age | four | 0.66 | 0.17 | 6.78 | < 0.001 | |
| Residuals | 1855 | 45.xix | 0.024 | |||
| 3R | Line | 2 | 0.0011 | 0.00059 | 0.033 | 0.97 |
| Residuals | 15 | 0.27 | 0.018 | |||
| Maternal age | 1 | 0.046 | 0.046 | ii.93 | 0.097 | |
| Line × maternal age | 2 | 0.069 | 0.0035 | 0.22 | 0.80 | |
| Residuals | 33 | 0.52 | 0.016 |
| Chromosome | Source | df | SS | MS | F-Value | Prob > F |
|---|---|---|---|---|---|---|
| 10 | Line | 4 | 1.34 | 0.34 | 10.25 | < 0.001 |
| Residuals | 305 | nine.96 | 0.033 | |||
| Maternal age | 1 | 1.32 | i.32 | 54.19 | < 0.001 | |
| Line × maternal age | 4 | 0.66 | 0.17 | half dozen.78 | < 0.001 | |
| Residuals | 1855 | 45.19 | 0.024 | |||
| 3R | Line | two | 0.0011 | 0.00059 | 0.033 | 0.97 |
| Residuals | 15 | 0.27 | 0.018 | |||
| Maternal historic period | 1 | 0.046 | 0.046 | 2.93 | 0.097 | |
| Line × maternal age | 2 | 0.069 | 0.0035 | 0.22 | 0.eighty | |
| Residuals | 33 | 0.52 | 0.016 |
df, degrees of freedom; SS, sum of squares; MS, mean foursquare.
Effigy 1
Crossover frequency summed across an individual female's lifetime for the (A) y v interval or (B) e ro interval. Boxplots testify first to third quartiles with median denoted by line inside the box with whiskers extending to the smallest and largest nonoutliers, while the grey line indicates the grand hateful.
Effigy 1
Crossover frequency summed across an individual female person's lifetime for the (A) y v interval or (B) e ro interval. Boxplots prove get-go to tertiary quartiles with median denoted by line inside the box with whiskers extending to the smallest and largest nonoutliers, while the gray line indicates the k mean.
Our results point that maternal age also contributes to variation in recombination charge per unit observed in the current written report (Fi,1837 = 56.09; P < 0.001). Our information further signal that rates of crossing over increase with maternal age within the y 5 genomic region (Figure ii), although these increases appear to not exist strictly linear. The increment in recombination frequency with increasing maternal age is consequent with several previous studies in Drosophila (Bridges 1915; Stern 1926; Bergner 1928; Lake and Cederberg 1984; Priest et al. 2007; Hunter and Singh 2014) and other species such equally humans ( Kong et al. 2004; Coop et al. 2008; Martin et al. 2015).
Figure 2
Average crossover frequency separated by mean solar day for RAL_21 (black line, ● data points), RAL_59 (nighttime gray line, ▪ data points), RAL_73 (long-dashed black line, ▴ data points), RAL_75 (short-dashed black line, Ten data points), and RAL_136 (low-cal gray line, ♦ information points). Upper lines represent crossover frequency in the y v interval while lower lines correspond crossover frequency in the e ro interval. Error bars denote standard error.
Effigy ii
Boilerplate crossover frequency separated by day for RAL_21 (black line, ● information points), RAL_59 (dark gray line, ▪ information points), RAL_73 (long-dashed black line, ▴ data points), RAL_75 (brusque-dashed blackness line, X data points), and RAL_136 (light gray line, ♦ data points). Upper lines represent crossover frequency in the y v interval while lower lines represent crossover frequency in the e ro interval. Error bars denote standard mistake.
In humans, increased recombination with increasing age is associated with a reduced incidence of aneuploidy ( Ottolini et al. 2015). Estimating levels of nondisjunction of these aforementioned five DGRP lines over a 25 d period ( Table S4), nosotros observe no meaning effect of age (P = one), yet nosotros do observe a significant event of genetic background and the interaction of genetic background and age (P < 0.001, both factors; Table S5). These results propose that, like rates of recombination, different genetic backgrounds besides vary in their corporeality of nondisjunction. Thus, it appears that although both Drosophila and humans can bear witness increases in recombination with increasing maternal age, rates of aneuploidy are less dependent on historic period per se and more than dependent on genetic background in Drosophila.
Central to our motivating hypothesis, the interaction of genetic background and maternal historic period likewise significantly contributes to phenotypic variation in recombination rate (F 4,1837 = half dozen.45; P < 0.001; Tabular array 1). This indicates that the furnishings of maternal age on recombination charge per unit are genotype-dependent. While previous work showed that unlike strains of D. melanogaster containing dissimilar ascendant deleterious mutations differed in the magnitude and extent of age-dependent changes in recombination (Tedman-Aucoin and Agrawal 2011), here nosotros report that natural genetic variation tin can also drive changes in the effects of maternal historic period on recombination rate.
To appraise the robustness of our findings, we tested for effects of maternal historic period, genetic background, and genotype–age interactions using a generalized linear model. While this statistical arroyo does non crave that residuals are usually-distributed equally the ANOVA framework does, it does not capture the repeated measurement structure of our data when partition variance. Analysis of the full information complement for the y v interval using a generalized linear model reveals significant effects of line and maternal age (P < 0.001 for both factors), and a marginally pregnant effect of genotype-past-age interaction on recombination rate variation (Table 2). The marginal significance revealed past this logistic regression, coupled with the loftier significance revealed by the repeated measures ANOVA, point that our results are largely robust to statistical approach and, moreover, are consistent with a statistically meaning line by age interaction result. As a further test of robustness, nosotros repeated both the repeated measures ANOVA and the logistic regression after removing RAL_136 (which contains segregating inversions on arms 3L and 3R (see Materials and Methods)); these analyses produce the same results in both cases ( Tabular array S6), indicating that this line is not driving the effect.
Results from generalized linear model to exam for effects of genetic background (line), age, and their interaction on crossover frequency in the ii intervals assayed
Table 2
Results from generalized linear model to test for furnishings of genetic background (line), age, and their interaction on crossover frequency in the two intervals assayed
| Chromosome | Source | df | χ 2 | Prob > χ 2 |
|---|---|---|---|---|
| X | Line | iv | 46.41 | < 0.001 |
| Maternal historic period | 9 | 126.10 | < 0.001 | |
| Line × maternal age | 36 | 48.80 | 0.075 | |
| 3R | Line | 3 | vii.84 | 0.0495 |
| Maternal age | two | 0.039 | 0.98 | |
| Line × maternal age | 6 | 4.22 | 0.65 |
| Chromosome | Source | df | χ ii | Prob > χ 2 |
|---|---|---|---|---|
| X | Line | four | 46.41 | < 0.001 |
| Maternal age | ix | 126.10 | < 0.001 | |
| Line × maternal age | 36 | 48.lxxx | 0.075 | |
| 3R | Line | 3 | 7.84 | 0.0495 |
| Maternal age | 2 | 0.039 | 0.98 | |
| Line × maternal age | half dozen | 4.22 | 0.65 |
df, degrees of liberty, χ 2, chi-square value.
Tabular array 2
Results from generalized linear model to exam for effects of genetic background (line), age, and their interaction on crossover frequency in the 2 intervals assayed
| Chromosome | Source | df | χ ii | Prob > χ ii |
|---|---|---|---|---|
| X | Line | four | 46.41 | < 0.001 |
| Maternal historic period | 9 | 126.10 | < 0.001 | |
| Line × maternal age | 36 | 48.80 | 0.075 | |
| 3R | Line | 3 | 7.84 | 0.0495 |
| Maternal age | 2 | 0.039 | 0.98 | |
| Line × maternal age | six | 4.22 | 0.65 |
| Chromosome | Source | df | χ 2 | Prob > χ 2 |
|---|---|---|---|---|
| X | Line | 4 | 46.41 | < 0.001 |
| Maternal age | 9 | 126.ten | < 0.001 | |
| Line × maternal age | 36 | 48.eighty | 0.075 | |
| 3R | Line | 3 | 7.84 | 0.0495 |
| Maternal age | ii | 0.039 | 0.98 | |
| Line × maternal age | 6 | 4.22 | 0.65 |
df, degrees of freedom, χ two, chi-square value.
Information technology bears mentioning that our surveyed window does non fully capture the potential effects of age on recombination. Indeed, Drosophila can have lifespans of ∼fourscore d and beyond ( Grönke et al. 2010; Mockett et al. 2012; Ivanov et al. 2015). However, the average lifespan is ∼45–60 d under optimal conditions (see Ivanov et al., 2015), and unremarkably less under normal conditions ( Ashburner et al. 2005). Additionally, the human activity of mating tin can significantly reduce the average lifespan of a female as compared to her nonmated counterpart (Fowler and Partridge 1989). The average (unmated) lifespan for the five lines used in this report is ∼56 d ( Arya et al. 2010; Ivanov et al. 2015). Therefore, our measurements spanning 22 d comprehend a big proportion of the developed lives of these flies. While information technology is possible that were we able to survey recombination rates over a longer menstruation of time we would see more than dramatic effects of age on recombination, that we notice a significant effect of maternal historic period on recombination rates in the y v region indicates that the furnishings of age, even within the first 22 d, are biologically significant.
Locus effects
Previous research has indicated that rates of crossing vary along the genome, both on wide and fine scales ( Lindsley et al. 1977; McVean et al. 2004; Cirulli et al. 2007; Paigen et al. 2008; Singh et al. 2009, 2013; Comeron et al. 2012). We hypothesized that changes in crossover frequency due to age might likewise exist variable across the genome, and another goal of this work was to test the whether the furnishings of maternal age on recombination frequency are locus-dependent. Past using markers on both the Ten and 3R chromosomes, nosotros can compare the effect of maternal age and genetic background at two different genomic locations. For the recombination rate estimation on chromosome 3R, we limited our analysis to only the first x d, combining progeny from days six–10. This maximized the useable data, as we recovered fewer progeny overall from this crossing scheme as compared with the crossing scheme used to survey recombination on the Ten chromosome. In addition, we did not include RAL_136 in this experiment due to the aforementioned segregating inversions.
A repeated measures ANOVA of the due east ro region data suggests no factors are significant (Table ane). Using a generalized linear model (meet Materials and Methods), we find that genetic background significantly contributes to the observed variation in recombination rate (P = 0.05), but neither maternal age (P = 0.98) nor the interaction term (P = 0.65) are significant. Once once again, the lifetime measure of recombination (every bit calculated from all progeny from an individual female over her lifetime) varies ∼2.5-fold (Effigy 1B), which is on the aforementioned scale as the y v region too as previous work (Brooks and Marks 1986; Hunter and Singh 2014; Hunter et al. 2016). Given the sensitivity of these results to the method of analysis, information technology is difficult to interpret the results. All the same, it is worth noting that reducing the X chromosome dataset to the showtime 10 d only and combining days 6–x confirms significant furnishings of genetic groundwork (P < 0.001), maternal age (P < 0.001), and the interaction of the two (P = 0.02) on recombination frequency in this X chromosome interval using a repeated measures ANOVA, both with and without DGRP_136 ( Table S7). This indicates that the lack of a detectable effect of maternal age on crossover frequency on 3R is not due to the sampling structure of the experiment. That we detect no consistent event of age on recombination frequency in the tertiary chromosome region surveyed is suggestive that crossover frequency at this locus is differentially sensitive to environmental variation.
To test explicitly for a locus upshot, nosotros used a generalized linear model with a binomial distribution and logit link part using data up to day 10 from both loci (run across Materials and Methods) to exam for significant effects of genetic background, maternal age, and locus, and their interactions. We observe a significant result of genetic background, maternal age, locus, and maternal age × locus (P < 0.02 for all factors) and a marginally significant outcome genetic background × locus of (P = 0.08) (Table 3). The significant consequence of maternal age × locus suggests that the furnishings of age on recombination frequency are significantly variable across the genome.
Results from generalized linear model to test for meaning furnishings of genetic background (line), historic period, locus, and their interactions on crossover frequency using a combined model to test for locus and locus interaction furnishings
Table 3
Results from generalized linear model to test for significant furnishings of genetic background (line), historic period, locus, and their interactions on crossover frequency using a combined model to test for locus and locus interaction furnishings
| Source | df | χ 2 | Prob > χ 2 |
|---|---|---|---|
| Line | 3 | 23.98 | < 0.001 |
| Locus | 1 | 705.42 | < 0.001 |
| Maternal age | ii | 7.08 | 0.029 |
| Line × locus | iii | 6.63 | 0.084 |
| Locus × maternal age | 2 | 7.69 | 0.021 |
| Line × maternal historic period | six | 4.38 | 0.63 |
| Line × maternal historic period × locus | 6 | 3.59 | 0.73 |
| Source | df | χ two | Prob > χ ii |
|---|---|---|---|
| Line | 3 | 23.98 | < 0.001 |
| Locus | 1 | 705.42 | < 0.001 |
| Maternal age | 2 | seven.08 | 0.029 |
| Line × locus | 3 | 6.63 | 0.084 |
| Locus × maternal historic period | 2 | seven.69 | 0.021 |
| Line × maternal age | half-dozen | four.38 | 0.63 |
| Line × maternal age × locus | vi | 3.59 | 0.73 |
df, degrees of liberty; χ ii, chi-foursquare value.
Table 3
Results from generalized linear model to test for significant effects of genetic background (line), historic period, locus, and their interactions on crossover frequency using a combined model to test for locus and locus interaction effects
| Source | df | χ 2 | Prob > χ 2 |
|---|---|---|---|
| Line | iii | 23.98 | < 0.001 |
| Locus | 1 | 705.42 | < 0.001 |
| Maternal age | 2 | 7.08 | 0.029 |
| Line × locus | three | six.63 | 0.084 |
| Locus × maternal age | 2 | seven.69 | 0.021 |
| Line × maternal age | 6 | iv.38 | 0.63 |
| Line × maternal age × locus | six | 3.59 | 0.73 |
| Source | df | χ 2 | Prob > χ ii |
|---|---|---|---|
| Line | 3 | 23.98 | < 0.001 |
| Locus | one | 705.42 | < 0.001 |
| Maternal age | 2 | 7.08 | 0.029 |
| Line × locus | 3 | half dozen.63 | 0.084 |
| Locus × maternal age | ii | 7.69 | 0.021 |
| Line × maternal age | 6 | 4.38 | 0.63 |
| Line × maternal age × locus | 6 | 3.59 | 0.73 |
df, degrees of freedom; χ 2, chi-square value.
Integrating our findings with previous work also points to genomic heterogeneity in the recombinational response to maternal age. For instance, data in humans are similarly suggestive of chromosome-level variability in the effect of maternal historic period on crossover frequency ( Hussin et al. 2011). Moreover, Bridges (1915) found differences in the frequency of crossing over in two dissimilar broods from the same D. melanogaster females for markers on the tertiary chromosome (pinkish and kidney), but no significant differences in crossover frequency in broods between markers on the X chromosome (vermilion and fused). Interestingly, our results show the opposite: meaning increases in recombination on the 10 chromosome but no pregnant changes in recombination rate on chromosome 3. These information hint at the possibility that not merely does the effect of maternal age on recombination vary as a function of genomic position, simply that information technology may also vary depending on the genetic groundwork of the strain surveyed.
We uncover neither a significant line by locus by age interaction issue nor a meaning line by maternal age interaction effect on recombination frequency in the current study (Table iii). However, nosotros are likely underpowered to exercise so. By increasing both the number of genomic intervals and the number of genetic backgrounds analyzed, one might exist amend able to detect these interaction effects, which appear to exist weaker than the furnishings of factors such every bit genetic background and maternal age. Additionally, increasing the sample size past assuasive repeated mating would increase the number of progeny produced past private females, adding ability to the analyses. Surveying additional females could also add power and could facilitate uncovering such interaction furnishings.
It should too be pointed out that the markers used in this written report are both distal in location, so it is somewhat surprising that they bear witness different trends. Information technology is possible that the use of markers more proximal to the centromere or in other chromosomal locations could prove different results, as distribution of recombination is non uniform along the length of chromosomes (Charlesworth and Campos 2014). Future studies will exist aimed at analyzing how rates of recombination respond to advancing maternal age across the entirety of the genome, allowing for tests of differences between distal and proximal regions of chromosomes.
Conclusions
Our results indicate that crossover frequency is mediated by genetic background and maternal age. The novel contribution of our piece of work is the finding of natural genetic variation for age-dependent changes in recombination rate in Drosophila. Future work will be aimed at quantifying the magnitude of genotype–age interaction effects in natural populations. Moreover, the DGRP provides a community resource that could potentially be used to uncover the genetic footing of these interaction furnishings, another area of time to come work. Our data are also indicative of genomic variability in the effects of maternal age on recombination frequency, opening the possibility that ecology stressors may influence dissimilar parts of the genome in different ways. Future work volition also be aimed testing for heterogeneity in the recombinational response to environmental stimuli at a genomic scale.
Acknowledgments
The authors would like to thank Aki Yamamoto for generating a marker strain used in this study, Jeff Sekelsky and Kathryn Kohl for supplying the marker strain used for the nondisjunction assay, and Amy Kelly for assistance with scoring. The authors give thanks Kevin Gillespie for assistance with plumbing fixtures their statistical model in R. The authors gratefully acknowledge members of the Singh lab for feedback on this projection and manuscript. The thoughtful and insightful comments by the associate editor and two anonymous reviewers profoundly improved this manuscript. This piece of work was supported by National Science Foundation grant MCB-1412813 to NDS.
Footnotes
Communicating editor: Thou. S. McKim
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Source: https://academic.oup.com/g3journal/article/6/5/1409/6027599
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