Abstract
Background: Spinocerebellar ataxia types 2 (SCA2) and 3 (SCA3/MJD) are diseases due to dominant unstable expansions of CAG repeats (CAGexp). Age of onset of symptoms (AO) correlates with the CAGexp length. Repeat instability leads to increases in the expanded repeats, to important AO anticipations and to the eventual extinction of lineages. Because of that, compensatory forces are expected to act on the maintenance of expanded alleles, but they are poorly understood.
Objectives: we described the CAGexp dynamics, adapting a classical equation and aiming to estimate for how many generations will the descendants of a de novo expansion last.
Methods: A mathematical model was adapted to encompass anticipation, fitness, and allelic segregation; and empirical data fed the model. The arbitrated ancestral mutations included in the model had the lowest CAGexp and the highest AO described in the literature. One thousand generations were simulated until the alleles were eliminated, fixed, or 650 generations had passed.
Results: All SCA2 lineages were eliminated in a median of 10 generations. In SCA3/MJD lineages, 593 were eliminated in a median of 29 generations. The other ones were eliminated due to anticipation after the 650th generation or remained indefinitely with CAG repeats transitioning between expanded and unexpanded ranges.
Discussion: the model predicted outcomes compatible with empirical data - the very old ancestral SCA3/MJD haplotype, and the de novo SCA2 expansions -, which previously seemed to be contradictory. This model accommodates these data into understandable dynamics and might be useful for other CAGexp disorders.
1 Introduction
CAG repeat expansions (CAGexp) are a major genetic cause of neurological diseases. When they occur within a codon region, the corresponding expansion of the polyglutamine tract (polyQ) in the expressed protein is thought to be neurotoxic. Mechanisms include post-transcriptional and -translational modifications and autophagic disturbances (; ). Each CAGexp or mutant polyQ targets different populations of neurons, causing distinct diseases that are commonly referred to as polyQ diseases. They include Huntington disease (HD [MIM: 143100]), the spinocerebellar ataxia type 1 (SCA1, [MIM: 164400]), type 2 (SCA2, [MIM: 183090]), type 3 (also known as Machado-Joseph disease, SCA3/MJD, [MIM: 109150]), type 6 (SCA6, [MIM: 183086]), type 7 (SCA7, [MIM: 164500]), type 17 (SCA17, [MIM: 607136]), Dentatorubropallidoluysian atrophy (DRPLA, [MIM: 125370]), and spinobulbar muscular atrophy (SBMA, also known as Kennedy’s disease, [MIM: 313200]) (Figure 1) (; ; ; ; ; ; ; ; ; ).
FIGURE 1
PolyQ diseases are rare, progressive, and fatal, and share several clinical and genetic characteristics (
ATXN2 and ATXN3 are the genes related to SCA2 and SCA3/MJD, respectively. Both disorders are characterized by gait ataxia, pyramidal signs, a dystonic and/or rigid extrapyramidal syndrome, sensory losses, amyotrophy, and progressive external ophthalmoplegia (
There are some notable differences between both diseases. SCA3/MJD shows a relevant gap between the length of normal and expanded alleles (Figure 1), lacks de novo expansions, and preferentially segregates the expanded allele on meiosis. Evidence in favor of a few and old ancestral haplotypes have been obtained in several SCA3/MJD populations by robust studies (for instance, see
The similarities between SCA2 and SCA3/MJD clinical characteristics suggests that social and psychological impacts over their carriers should also be similar. This allows the presumption that the differences between SCA2 and SCA3/MJD transmissions to the offspring should not be attributed to distinct psychological or social pictures, but to the biological context of the expansions in ATXN2 and ATXN3. Due to that, SCA2 and SCA3/MJD are probably good prototypes to test the dynamics of the CAGexp in general and to answer the question: “for how many generations will the descendants of a de novo expansion last?” This was the aim of the present study. The specific aims were to adapt a classical equation on allele dynamics in population genetics to be used in the case of dominant alleles related to late onset neurodegenerative disorders; and then, to test if the results of the model match with the existing epidemiological evidence on ancestral lineages and anticipation, in SCA2 and SCA3/MJD.
2 Subjects and methods
2.1 Subjects
The following human populations were used in this work: EUROSTAT (European Statistical Office) 2019 data were used to establish fertility rates of normal women stratified by life year. Measures of fitness, segregation distortion, CAGexp instability and anticipation related to SCA2 or SCA3/MJD subjects were obtained from two meta-analyses published elsewhere (
2.2 Methods
To estimate the fate of CAGexp transmissions after a de novo expansion, one hypothetical expanded allele was assigned to a hypothetical founder of a lineage, and Monte Carlo methods were used to assign a random genotype to each descendant in several generations, based on segregation of the parental alleles, in a way similar to the gene dropping methodology (
2.2.1 Adaptation of classical method on natural selection effect
The classical equation from population genetics theory encompasses fitness and distortion in allelic segregation as two selective forces of interest:Where:
p' = Frequency of p allele in the subsequent generation.
p2 = Frequency of individuals with the p allele in homozygotes.
w11 = Fitness of the p allele in homozygotes.
k = segregation coefficient, where 0.5 represents Mendelian segregation.
p = frequency of the p allele.
q = frequency of the q allele.
w12 = fitness of heterozygotes.
W = average fitness.
Adaptations were made to match the original equation to the specific characteristics of polyQs diseases, as follows.
First, an anticipation coefficient (antcoeff) was included to account for the influence of anticipation on the allele frequency at each generation of a lineage. The antcoeff ranged from zero to one, where zero corresponds to symptoms starting before the beginning of the fertile life (proposed as being 12 years of age) and one is related to symptoms that begin after the end of the fertile life (proposed as being 50 years of age). The extreme values represent the worst and the best scenarios for reproductive life, respectively, and the anticipation coefficient values were the mathematical expression of the relation between AOfs and the fertility rate of the ages’ interval arising from the new cutoff in the reproductive period imposed by the AOfs, in a given generation, in the studied lineage.
The next adjustments aimed to simplify the model. As the expanded alleles have a very low frequency, homozygosity is so rare that is practically non-existent: as p2 ≈ 0, then it was removed from the equation. The expanded alleles have complete or near complete dominance, without any clear dosage effect when present in double dose (
The last adjustment was to directly use the relative w fitness into the equation instead of using the carriers’ W fitness divided by the general W fitness of the population, or in other words, replacing the expression with its results w
With this, we arrived at:Where:
p = frequency of the expanded allele.
p' = frequency of the expanded allele in the subsequent generation.
w = relative fitness.
antcoeff = anticipation coefficient.
k = segregation coefficient.
2.2.2 The anticipation coefficient antcoeff
To infer the impact of anticipation on carrier’s fitness, we developed the anticipation coefficient or antcoeff based on the premise that only a proportion of children is born after onset of symptoms, and on the premise that the reproductive period starts in adolescence. Therefore, it was important to impute what this proportion of births would be after the onset of symptoms, and what the fertility rates would be in the age groups still included before the AO of each new generation - data that will be described in the next paragraph. The antcoeff itself was the result of a five-step operation: first, the average CAGexp size of a given generation was estimated from the available data about CAGexp instability in the disease of interest; then, this new CAGexp length was related to its average AO; then, the new length of the reproductive period due to this new anticipation of the AO was imputed; measures of SCA2 and SCA3/MJD fitness were obtained from two meta-analyses published elsewhere (
We arbitrated that the antcoeff would be equal to the average reproduction rate of a given generation, in the lineage of a common ancestor. Each generation of carriers would be prone to show modifications in their reproduction rate, due to the change of their average reproductive period, provoked, in turn, by the average anticipation of that generation.
We assume that each new anticipation will be associated with an additional reduction in the fertile period. Although symptomatic people might continue to have children during the early years of their illness, at some point, their clinical state will interfere with the reproductive capacity - either because of the children’s threats related to motor incapacity of a parent, or because of the reduced opportunities of sexual relationships required for reproduction. The conceptual relationship between anticipation and reproduction reduction has been studied and discussed elsewhere (
To define how reductions in the reproductive period modify the antcoeff in each generation, fertility rates in different age groups of a general population were established, using EUROSTAT data on the fertility of European women in 2019. In order to know how much each age group contributed to the overall fertility rate of that population in a given year, the total area below fertility function was calculated (Supplementary Material S3, Supplementary Figure S1A) and normalized to the value of 1, equal to the best anticipation coefficient, that was also equal to 1 when the disease onset is after the end of the reproductive period and 0 when symptoms begin before the start of reproductive period (Supplementary Material S3, Supplementary Figure S1B). Each reduction in the reproductive period due to anticipation then reduced the area of the plot and reduced the value of the anticipation coefficient.
The next estimation to be made was that of the AOfs variation with each new generation, to extrapolate the corresponding reduction in the reproductive period.
All cohorts published to date were biased in favor of high anticipations (
A linear regression performed for SCA3/MJD carriers from the IPD mentioned before (
TABLE 1
| Allele | Fitness, w | Segregation distortion, k | Instability during meiosis | AO reduction due to each CAG repeat added in the expanded repeat |
|---|---|---|---|---|
| ATXN2, expanded allele | 1.50 (0.25)a | 0.404 (0.085)a | 2.42 (5.655) | 1.877 (1.86) |
| ATXN3, expanded allele | 1.45 (0.25)a | 0.640 (0.085) | 1.23 (5.126) | 1.652 (1.729) |
| ATXN2, normal allele | 1.00 (0.25)a | 0.596 (0.085)a | 0.23 (0.468) | |
| ATXN3, normal allele | 1.00 (0.25)a | 0.360 (0.085) | 0.00a (0.468)a |
Variables related to CAG repeats at ATXN2 and ATXN3, and used in the present adapted model. Data related to expanded repeats was obtained from heterozygous carriers; data related to normal repeats was obtained from non-carriers. Data is presented as means (standard deviation).
Imputed values. Reasons for each imputation were described in the text.
Contractions in the CAGexp repeat length can also occur, reducing the size of CAGexp, although very rarely documented (
Ultimately, we could express these relationships above in a way that the anticipation coefficient varies from 0 to 1, where 0 means that the carriers will not be able to reproduce, and 1 means their reproductive period will not be affected by the onset of symptoms. A simple causal chain can be defined as:
2.2.3 Other variables to be included in the model
In addition to anticipation, the model needed to include fitness and segregation distortion, as mechanisms potentially associated with the long-term maintenance of polyQ diseases. Data on these forces were collected from previous systematic reviews (
Fitness, segregation rates and unstable transmissions associated with non-pathogenic alleles - those originated from contractions as well as the wildtype alleles - also need to be considered in the model. The fitness of the normal alleles is an a priori concept and is equal to one; the same SD of 0.25 were arbitrarily imputed to them. Transmission of intermediate-sized unexpanded ATXN2 alleles was studied in the Cuban population (
Although the assumed variances seem reasonable, several of them were not obtained from observational data. The arbitrated SD of fitness, in particular, could add uncertainty to the inputs. Different SD values of fitness were then imputed in a second round to check if outputs would be distorted, in a sensitivity analysis of the model.
2.3 Computer simulations on the dynamics of the expanded alleles
Means and SD of the variables considered so far, were used in the simulations on what occurs in the successive generations of a lineage. The emphasis on the use of SD was decided, in order that results could capture any potential scenario of real life. Thus, at each generational step, the simulated values brought the component of randomness to our results.
Measures of central tendency of descriptive variables were eventually presented as means (range) or as medians (range), according to the pattern of their distributions.
The simulations were performed in the R Statistical Package. The hypothetical initial frequency of the expanded allele was proposed to be 0.000001 for both ATXN2 and ATXN3 - sufficiently low to be considered plausible. The original ancestral expanded allele was proposed to correspond to the smallest length of the symptom-associated CAG repetitive sequences found in the datasets described in section 2.1 Materials - 34 and 54 CAGexp for SCA2 and SCA3/MJD, respectively (
At least 1,000 different lineages with allele frequencies randomly generated in each generation, considering the values described above, were simulated per CAGexp ancestor, covering a maximum of 650 generations. One thousand runnings were done to warrant a sampling-based approach of the sensitive analysis of our outputs. The number of generations was chosen because it corresponds to circa 16,250 years, or the approximate age of the oldest SCA3/MJD SNP haplotype rs16999141, rs1048755, rs12895357, rs7158733 and rs3092822 known so far, the TTACAC or Joseph lineage (
3 Results
3.1 Frequencies of the expanded allele at ATXN2 across generations
From the 1,000 lineages simulated as descendants of the ancestral expansion of 34 repeats in ATXN2, 933 were eliminated in a median value of 10 generations, the extinction ranging between the 2nd and 121st generations. The frequency of alleles eliminated by generation and the number of generations that the allele remained in the population are represented in Supplementary Material S3, Supplementary Figure S2A, and Figure 2A.
FIGURE 2

Fate of 1,000 lineages simulated as descendants of one ancestral with a CAG expansion and with an initial population frequency of 0.000001. (A) Proportion of descents with expanded repeats, per generation, after the first ancestor with 34 repeats in ATXN2. (B) Proportion of descents with expanded repeats, per generation, after the first ancestor with 54 repeats in ATXN3.
From these 1,000 simulated lineages, 67 were fixed after a median (range) of 60 (34–113) generations. In the generation in which the allele was fixed, the median (range) repeat length was of 32.00 (22.72–39.14) repeats - i.e., either non-pathogenic or borderline allele, in relation to SCA2 symptoms. The frequency of fixed alleles across generations are shown in Figure 3A. Figure 3B shows that all those 67 lineages in which the allele were fixed in the background population, turned out to be expanded, resulting in AOfs before the start of the reproductive period of life (Figure 3C). These lineages would then become extinct in up to the 170th generation (Table 2).
FIGURE 3

Data on the 67 lineages where fixed CAG expanded alleles at ATXN2 were obtained after 1,000 simulations (simulations stopped when lineages were fixed). (A) Frequency of descendant alleles across generations until they were fixed. (B) The repeat lengths in the lineages where the descendant allele was fixed, per generation. The bold area in the graphic represents the pathological range of CAG repeat lengths (or CAGexp). (C) The predicted mean age at onset of gait ataxia per generation of each fixed lineage. The bold area in the graphic represents ages of onset younger than 10 years old. The lines describing the AO in each lineage were interrupted at 46.55 years of age, at the top of the chart, since this was the AO predicted to be related to the 34 CAG repeats, the shortest expansion in the pathological range.
TABLE 2
| Lineages eliminated from the population | Fixed alleles | Lineages that remained in the population | ||
|---|---|---|---|---|
| Lineages extinct after fixation | Lineages held as fixed | |||
| ATXN2 | 933 | 67 | 0 | 0 |
| ATXN3 | 593 | 43 | 7 | 357 |
| p | <0.001 | ns | ns | <0.001 |
Comparisons between the fates of ATXN2 and ATXN3 lineages produced by computer simulations from their expanded ancestors, in the 650th generation.
Results obtained with different imputed SDs of ATXN2 fitness were described in Supplementary Material S4.
3.2 Frequencies of the expanded allele at ATXN3 across generations
From the 1,000 lineages simulated as descendants of the ancestral expansion with 54 repeats in ATXN3, 593 were eliminated in a median of 29 generations, the extinction ranging between 3 and 649 generations. The median size of the CAG repeats when the lineages were eliminated was 84, ranging from 39.45 to 88. The frequency of alleles eliminated by generation and the histogram of the number of generations where the allele was deleted are shown in Figure 2B and in Supplementary Figure S2B.
Of the same 1,000 simulated lineages, 50 were fixed and their frequencies across the generation are shown in Figure 4A. Fixation occurred at a median (range) of 19.50 (14–63) generations. Figure 4B shows the repeat lengths until the allele was fixed. In all the fixed lineages, the allele was expanded when it became fixed: they had a mean (range) of 64.64 (53.20–75.86) CAG repeats; the mean (range) AO of their carriers was 50.34 (14.98–70.41) years. After turning fixed, further instabilities continued to occur in the descendants (Figure 4B). Of the 50 fixed ATXN3 lineages, the 43 that continued to expand were eliminated due to severe anticipations in AO (Figure 4C); the seven lineages transmitted after the 650th generation presented a contraction, carrying a limitrophe allele between normal and pathogenic CAG repeat lengths (Figure 4B).
FIGURE 4

Data on the fifty lineages where fixed CAG expanded alleles at ATXN3 were obtained after 1,000 simulations (simulations stopped when lineages were fixed). (A) Frequency of descendant alleles across generations until they were fixed. (B) The repeat lengths in the lineages where the descendant allele was fixed, per generation. The bold area in the graphic represents the pathological range of CAG repeat lengths (CAGexp). (C) The predicted mean age at onset of gait ataxia per generation of each fixed lineage. The bold area in the graphic represents ages of onset younger than 10 years old. The lines describing the AO in each lineage were interrupted at 71.14 years of age, at the top of the chart. They were related to CAG repeat sizes that were outside the pathological range, according to the regression.
Finally, and more importantly, the 357 lineages where the simulated alleles were not eliminated nor fixed, had their frequencies across generations shown in Figure 5A and Figure 5B. These 357 lineages that remained in the population without fixed alleles showed CAG repeats transitioning between the expanded (equal or larger than 51 repeats) and non-expanded ranges (Figure 5C), suggesting that this phenomenon might make a lineage to survive for many centuries. Of note, the allele frequencies of the expanded repeats (51 repeats or more) remained very low and reached a median (IQR) 2.7e-107 (1.960994e-108) of in the 650th generation. When the 13 lineages that presented expanded alleles in generation 650 are presented separately, this alternation between the expanded and non-expanded allele can be more clearly observed (Figure 5D). Table 2 summarizes these different fates of SCA3/MJD lineages and compares them to those of SCA2 lineages.
FIGURE 5

The 351 lineages of the CAG expanded alleles at ATXN3 that remained non-fixed in the 1,000 simulations. (A) Frequency of descendant alleles until the 650th generation. Most have residual frequencies, very close to zero, and are not discernible on the graph. (B) The repeat lengths in these lineages, per generation. Note the transitions between the pathogenic and non-pathogenic CAG repeat lengths. The bold area in the graphic represents the pathological range of CAG repeat lengths (CAGexp). (C) The repeat lengths in the 13 lineages that presented expanded alleles in generation 650. The bold area in the graphic represents the pathological range of CAG repeat lengths (CAGexp). The repeat lengths alternate between the expanded and unexpanded ranges in generations that antecede the 650th one.
Results obtained with different imputed SDs of ATXN3 fitness were described in Supplementary Material S4.
4 Discussion
Data on prevalence and anticipation have been difficult to put together in a unified biological explanation for polyQ diseases, as they are in contradiction with each other. Phenomena such as increased fitness and preferential segregation of the mutant alleles were then proposed to balance anticipation. But the empirical results, either because they were sparse or heterogeneous, kept the explanatory hypotheses in abeyance. The present model on the dynamics of expanded CAG alleles obtained compatible scenarios with current epidemiology, precisely using the empirical data on anticipation available now. Our model, like other population genetics models, was intended to capture some approximations of reality. Thus, our results, as they are closer to empirical evidence, can help to develop an acceptable and understandable explanation about why polyQ diseases can be present in populations for long periods of time.
The term “dynamics of the CAGexp” means the intrinsic, mutation-driven pattern of change in time of CAGexp which should be, by its nature, a multifactorial event. These dynamics might depend on the repeat motif itself (length, interruptions, etc.), on the surrounding sequence, and on other factors that interplay with this surrounding context (sex and parental age, for instance) (
Available empirical data on the evolutionary mechanisms at work on polyQs are certainly incomplete. Despite this, with data already available, the model predicted intergenerational dynamics that ended up being compatible with both apparently incoherent empirical data from SCA3/MJD - the few ancestral lineages with a long survival - and also the relatively more coherent facts associated with SCA2 - the serious anticipations due to dramatic expansions described in the literature and the multiple ancestral lineages (
In common, the CAGexp dynamics first went through an increase followed by a decrease in the frequencies of expanded alleles in successive generations. But the CAGexp alleles at ATXN2 and ATXN3 followed quite different trajectories (Table 2). The ATXN3 allele might remain longer in the population, a fate due to the bias in favor of the expanded allele in the segregation of gametes, to favorable fitness, and to the less intense instability and anticipation than in relation to the expanded allele in ATXN2 (Supplementary Figure S3).
In contrast, the rise and fall of frequencies were quite sharp in SCA2. The model predicted that any real expansion in ATXN2 would have a chance close to 98.6% of becoming extinct approximately 10 generations after its appearance. In this scenario, SCA2 recurrence in human populations distant as those of India, Cuba, and others, would depend upon de novo expansions.
The normal (CAG)22 allele in ATXN2 is the most prevalent in the population (
Given the positive selection of the (CAG)22 allele and the tendency of the expanded alleles to be rapidly withdrawn after they appear, SCA2 lineages should quickly disappear, and de novo expansions should be the most likely reason for the maintenance of SCA2 in populations. Our finding of at least eleven different ancestral SCA2 haplotypes among South American families are in line with this interpretation (
There is a continuum in the distribution of CAG alleles in ATXN2 found in controls and in SCA2 carriers. The lack of a gap between normal and SCA2-associated alleles and the occurrence of de novo cases are peculiar characteristics that occur not only in SCA2 but also in other polyQ diseases, such as SCA6, SCA7, and HD (Figure 1). It is possible that the dynamics of the expanded allele of these other polyQs might be like that described here for SCA2.
In contrast, and as said before, SCA3/MJD is a polyQ disorder somehow different from most others, combining few ancestral haplotypes with a long-term permanence across generations (
Our simulations have also revived an old hypothesis to explain the antiquity of the ancestral generations of SCA3/MJD: the existence of a haplotype that predisposes to expansions (
The lineages modeled here refer to the descendants of a first expansion carrier. It is interesting to consider the population environment as well, to understand the uncommon occurrence or even the lack of intermediate alleles in ATXN3. We have seen that short ATXN3 alleles are transmitted preferentially in meiosis in the general population (
In any case, the fact that CAG tracts in ATXN2, ATXN7 and HTT, among others, are prone to de novo expansion, while CAG tracts in ATXN3 do not appear to be, needs to be further elucidated by observational and/or experimental studies. Discovering the reason for this discrepancy can have an impact even for future therapeutic or preventive management. One might suspect, for instance, that the CAG repeat of ATXN3 has structural features in wild type alleles that confers a strong protection against instabilities. Or that the preferential segregation of the shortest allele in the presence of two normal alleles is a force to prevent a novel expansion of a normal allele originated from a contraction of an originally expanded allele.
To date, there is no indication whether the dynamics of the expanded allele in ATXN3 finds similes among other polyQs. The best candidates to share these dynamics would be the polyQs for which no intermediate alleles were found, such as in DRPLA and in SBMA (Figure 1). In the case of SBMA, X-linked inheritance adds complexity to the description of intermediate alleles. DRPLA has additional similarities with SCA3/MJD: the gap between the normal range and the pathogenic range of CAGn is large (Figure 1); CAGexp on ATN1 is favorably segregated (
Fixation of some descendant CAGexp alleles was a counterintuitive result of our model, but occurred in a minority of simulations, i.e., in 6.7% and 5% of ATXN2 and ATXN3, respectively. The average CAG lengths of fixed lineages were 32 and 64 CAGexp for ATXN2 and ATXN3, which are related to the late onset (or non-penetrant range, in ATXN2 case) of the disease; part of them did not expand during fixation, thus not reducing the reproductive period of their carriers. When the simulations were continued, all fixed ATXN2 lineages were eliminated due to the severe anticipation (Figure 3C). The 14 fixed ATXN3 lineages at generation 650 had an unexpanded CAG repeat size; in some situations, their descents transited to the expanded range later (Figure 4B). Indeed, these scenarios seem highly hypothetical.
It is important to point out some weaknesses due to the lack of concrete data to include in the model. By running the model a large number of times, we tried to reduce the uncertainty of the outputs. But some inputs can still raise concerns. First, although we set a cutoff of 650 generations for the simulations, our fertility parameters were based on available data on the contemporary European populations. It is almost certain that in the past people had their children at an earlier age than today. The precocity of the general reproductive period would have meant a relaxation of the selective forces that acted to eliminate CAGexp from the population. As a result, perhaps the lineages of any CAGexp lasted longer in the deep past than we calculate here. This bias, however, would have occurred equivalently in the ATXN2 and ATXN3 lineages.
Second, some arbitrary values were included in the model (Table 1). Of those, the SDs of the fitness for the four categories of alleles seemed to be potentially problematic. However, after running the model with other imputed variances of the fitness, the results presented very similar fates to those obtained with the initial SD values, resulting in the extinction of all SCA2 lineages and in the survival of several SCA3/MJD lineages until the 650th generation (Supplementary Material S4).
One can also speculate whether, in the past, other CAG repeats loci would have undergone pathogenic expansions causing maladaptive phenotypes. And that these alleles could have become extinct, so that the phenomenon would go unrecorded and be lost in human history.
Finally, as a theoretical work, the present study raised not direct evidence, but probabilities from existing empirical data on selective forces that converged with current epidemiology. It is worth emphasizing that we were interested in clarifying the effects of past history on present and not on future prevalences. Even so, further studies on prevalence and on ancestral haplotypes are still required to amplify these generalizations. Prevalence of SCA3/MJD in the Azores archipelago increased between 1981 and 2015 (
In conclusion, the general dynamics of the CAGexp alleles seems to follow an increase in frequency for a few generations, followed by a decrease in frequency. Expanded ATXN2 alleles showed a clear and rapid tendency to be eliminated from the population. Their maintenance in human populations must be explained by de novo expansions. To the contrary, expanded ATXN3 alleles showed a tendency to remain longer in the population, a phenomenon explained at least by the favorable fitness, by the distortion in favor of the expanded allele or by a less intense instability and anticipation when compared to the expanded allele in ATXN2. These results contribute to the understanding of the survival of ancient origins for the ATXN3 expansions. Finally, we think that the present mathematical model, combined with evidence of specific selective forces, can be used to simulate the dynamics of expanded alleles in other polyQ diseases.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://ec.europa.eu/eurostat/data/database doi: 10.1111/cge.13978 doi: 10.1111/cge.13888 doi: 10.1136/jnnp-2018-319200.
Ethics statement
Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.
Author contributions
LS: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Writing–original draft. RL: Formal Analysis, Methodology, Supervision, Writing–review and editing. GF: Data curation, Investigation, Writing–review and editing. MS-P: Data curation, Supervision, Writing–review and editing. LJ: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Resources, Supervision, Writing–original draft, Writing–review and editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Financiamento e Incentivo à Pesquisa do Hospital de Clínicas de Porto Alegre (FIPE-HCPA) (grant numbers 2019-0254 and 2019-0169). LS, MS-P, and LJ were supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.
Acknowledgments
The present work was posted as a preprint on bioRxiv on 9 September 2023 with the doi doi.org/10.1101/2023.09.07.556735.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1296614/full#supplementary-material
References
1
AdegbuyiroaA.SedighiaF.PilkingtonA. W.GrooveraS.LegleiteraJ. (2017). Proteins containing expanded polyglutamine tracts and neurodegenerative disease. Biochemistry56 (9), 1199–1217. 10.1021/acs.biochem.6b00936
2
Almaguer-MederosL. E.MesaJ. M. L.González-ZaldívarY.Almaguer-GotayD.Cuello-AlmaralesD.Aguilera-RodríguezR.et al (2018). Factors associated with ATXN2 CAG/CAA repeat intergenerational instability in Spinocerebellar ataxia type 2. Clin. Genet.94, 346–350. 10.1111/cge.13380
3
AndrésA. M.LaoO.SoldevilaM.CalafellF.BertranpetitJ. (2003). Dynamics of CAG repeat loci revealed by the analysis of their variability. Hum. Mutat.21, 61–70. 10.1002/humu.10151
4
BechS.PetersenT.NørremølleA.GjeddeA.EhlersL.EibergH.et al (2010). Huntington's disease-like and ataxia syndromes: identification of a family with a de novo SCA17/TBP mutation. Park. Relat. Disord.16, 12–15. 10.1016/j.parkreldis.2009.06.006
5
BirdT. D. (1998). “Hereditary ataxia overview,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
6
BuntingE. L.HamiltonJ.TabriziS. J. (2021). Polyglutamine diseases. Curr. Opin. Neurobiol.72, 39–47. 10.1016/j.conb.2021.07.001
7
CaronN. S.WrightG. E. B.HaydenM. R. (1998). “Huntington disease,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
8
CarrollL. S.MasseyT. H.WardleM.PeallK. J. (2018). Dentatorubral-pallidoluysian atrophy: an update. Tremor Other Hyperkinet Mov.8, 577. 10.7916/D81N9HST
9
CaseyH. L.GomezC. M. (1998). “Spinocerebellar ataxia type 6,” in GeneReviews. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
10
ChenX. C.SunH.ZhangC. J.ZhangY.LinK. Q.YuL.et al (2013). Positive selection of CAG repeats of the ATXN2 gene in Chinese ethnic groups. J. Genet. Genomics40, 543–548. 10.1016/j.jgg.2013.08.003
11
ChoudhryS.MukerjiM.SrivastavaA. K.JainS.BrahmachariS. K. (2001). CAG repeat instability at SCA2 locus: anchoring CAA interruptions and linked single nucleotide polymorphisms. Hum. Mol. Genet.10, 2437–2446. 10.1093/hmg/10.21.2437
12
Cruz-MariñoT.Laffita-MesaJ. M.Gonzalez-ZaldivarY.Velazquez-SantosM.Aguilera-RodriguezR.Estupinan-RodriguezA.et al (2014). Large normal and intermediate alleles in the context of SCA2 prenatal diagnosis. J. Genet. Couns.23, 89–96. 10.1007/s10897-013-9615-1
13
CuboE.Martinez-HortaS. I.SantaloF. S.DescallsA. M.CalvoS.Gil-PoloC.et al (2019). Clinical manifestations of homozygote allele carriers in Huntington disease. Neurology92 (18), e2101–e2108. 10.1212/WNL.0000000000007147
14
DavidG.AbbasN.StevaninG.DurrA.YvertG.CancelG.et al (1997). Cloning of the SCA7 gene reveals a highly unstable CAG repeat expansion. Nat. Genet.17, 65–70. 10.1038/ng0997-65
15
de AraújoM. A.RaposoM.KazachkovaN.VasconcelosJ.KayT.et al (2016). Trends in the epidemiology of spinocerebellar ataxia type 3/machado-joseph disease in the Azores islands, Portugal. JSM Brain Sci.1 (1), 1001.
16
de CastilhosR. M.FurtadoG. V.GhenoT. C.SchaefferP.RussoA.BarsottiniO.et al (2014). Spinocerebellar ataxias in Brazil‐frequencies and modulating effects of related genes. Cerebellum13, 17–28. 10.1007/s12311-013-0510-y
17
de MattosE. P.Kolbe MusskopfM.Bielefeldt LeottiV.Saraiva-PereiraM. L.JardimL. B. (2019). Genetic risk factors for modulation of age at onset in Machado-Joseph disease/spinocerebellar ataxia type 3: a systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry90, 203–210. 10.1136/jnnp-2018-319200
18
DialloA.JacobiH.CookA.LabrumR.DurrA.BriceA.et al (2018). Survival in patients with spinocerebellar ataxia types 1, 2, 3, and 6 (EUROSCA): a longitudinal cohort study. Lancet Neurol.17 (4), 327–334. 10.1016/S1474-4422(18)30042-5
19
Europa (2023). Fertility indicators. https://ec.europa.eu/eurostat/databrowser/view/demo_find/default/table?lang=en.
20
FutamuraN.MatsumuraR.FujimotoY.HorikawaH.SuzumuraA.TakayanagiT. (1998). CAG repeat expansions in patients with sporadic cerebellar ataxia. Acta Neurol. Scand.98, 55–59. 10.1111/j.1600-0404.1998.tb07378.x
21
GardinerS. L.BoogaardM. W.TrompetS.de MutsertR.RosendaalF. R.GusseklooJ.et al (2019). Prevalence of carriers of intermediate and pathological polyglutamine disease-associated alleles among large population-based cohorts. JAMA Neurol.76, 650–656. 10.1001/jamaneurol.2019.0423
22
GuW.MaH.WangK.JinM.ZhouY.LiuX.et al (2004). The shortest expanded allele of the MJD1 gene in a Chinese MJD kindred with autonomic dysfunction. Eur. Neurol.52, 107–111. 10.1159/000080221
23
GusellaJ. F.LeeJ. M.MacDonaldM. E. (2021). Huntington’s disease: nearly four decades of human molecular genetics. Mol. Genet.30, 254–263. 10.1093/hmg/ddab170
24
HobanS.BertorelleG.GaggiottiO. (2012). Computer simulations: tools for population and evolutionary genetics. Nat. Rev. Genet.13, 110–122. 10.1038/nrg3130
25
IkeuchiT.IgarashiS.TakiyamaY.OnoderaO.OyakeM.TakanoH.et al (1996). Non-Mendelian transmission in dentatorubral-pallidoluysian atrophy and Machado-Joseph disease: the mutant allele is preferentially transmitted in male meiosis. Am. J. Hum. Genet.58, 730–733.
26
KawaguchiY.OkamotoT.TaniwakiM.AizawaM.InoueM.KatayamaS.et al (1994). CAG expansions in a novel gene for Machado-Joseph disease at chromosome 14Q32.1. Nat. Genet.8, 221–228. 10.1038/ng1194-221
27
KayC.CollinsJ. A.WrightG. E. B.BaineF.MiedzybrodzkaZ.AminkengF.et al (2018). The molecular epidemiology of Huntington disease is related to intermediate allele frequency and haplotype in the general population. Am. J. Med. Genet. B Neuropsychiatr. Genet.177 (3), 346–357. 10.1002/ajmg.b.32618
28
KoideR.KobayashiS.ShimohataT.IkeuchiT.MaruyamaM.SaitoM.et al (1999). A neurological disease caused by an expanded CAG trinucleotide repeat in the TATA-binding protein gene: a new polyglutamine disease?Hum. Mol. Genet.8, 2047–2053. 10.1093/hmg/8.11.2047
29
KomureO.SanoA.NishinoN.YamauchiN.UenoS.KondohK.et al (1995). DNA analysis in hereditary Dentatorubral-Pallidoluysian Atrophy – correlation between CAG repeat length and phenotypic variation and the molecular-basis of anticipation. Neurology45, 143–149. 10.1212/wnl.45.1.143
30
Laffita-MesaJ. M.Velázquez-PérezL. C.Santos FalcónN.Cruz-MariñoT.González ZaldívarY.Vázquez MojenaY.et al (2012). Unexpanded and intermediate CAG polymorphisms at the SCA2 locus (ATXN2) in the Cuban population: evidence about the origin of expanded SCA2 alleles. Eur. J. Hum. Genet.20 (1), 41–49. 10.1038/ejhg.2011.154
31
La SpadaA. (1999). “Spinal and bulbar muscular atrophy,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
32
La SpadaA. R. (1998). “Spinocerebellar ataxia type 7,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
33
La SpadaA. R.WilsonE. M.LubahnD. B.HardingA. E.FischbeckK. H. (1991). Androgen receptor gene mutations in X-linked spinal and bulbar muscular atrophy. Nature352, 77–79. 10.1038/352077a0
34
LeeJ. M.RamosE. M.LeeJ. H.GillisT.MysoreJ. S.HaydenM. R.et al (2012). CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashion. Neurology78, 690–695. 10.1212/WNL.0b013e318249f683
35
LiT.MartinsS.PengY.WangP.HouX.ChenZ.et al (2019). Is the high frequency of machado-joseph disease in China due to new mutational origins?Front. Genet.9, 740. 10.3389/fgene.2018.00740
36
LiebermanA. P.ShakkottaiV. G.AlbinR. L. (2019). Polyglutamine repeats in neurodegenerative diseases. Annu. Rev. Pathol.14, 1–27. 10.1146/annurev-pathmechdis-012418-012857
37
MacCluerJ. W.VandeBergJ. L.Read BR. O. A. (1986). Pedigree analysis by computer simulation. Zoo. Biol.5, 147–160. 10.1002/zoo.1430050209
38
MacielP.CostaM. C.FerroA.RousseauM.SantosC. S.GasparC.et al (2001). Improvement in the molecular diagnosis of Machado-Joseph disease. Arch. Neurol.58, 1821–1827. 10.1001/archneur.58.11.1821
39
MacielP.GasparC.DeStefanoA. L.SilveiraI.CoutinhoP.RadvanyJ.et al (1995). Correlation between CAG repeat length and clinical features in Machado-Joseph disease. Am. J. Hum. Genet.57, 54–61.
40
MacielP.GasparC.GuimarãesL.GotoJ.Lopes-CendesI.HayesS.et al (1999). Study of three intragenic polymorphisms in the Machado-Joseph disease gene (MJD1) in relation to genetic instability of the (CAG)n tract. Eur. J. Hum. Genet.7, 147–156. 10.1038/sj.ejhg.5200264
41
MargolisR. L.RudnickiD. D.HolmesS. E. (2005). Huntington’s disease like-2: review and update. Acta Neurol. Taiwan14, 1–8.
42
MartinsS.CalafellF.GasparC.WongV. C.SilveiraI.NicholsonG. A.et al (2007). Asian origin for the worldwide-spread mutational event in Machado-Joseph disease. Arch. Neurol.64, 1502–1508. 10.1001/archneur.64.10.1502
43
MartinsS.MatamáT.GuimarãesL.ValeJ.GuimarãesJ.RamosL.et al (2003). Portuguese families with dentatorubropallidoluysian atrophy (DRPLA) share a common haplotype of Asian origin. Eur. J. Hum. Genet.11, 808–811. 10.1038/sj.ejhg.5201054
44
MartinsS.SequeirosJ. (2018). Origins and spread of machado-joseph disease ancestral mutations events. Adv. Exp. Med. Biol.1049, 243–254. 10.1007/978-3-319-71779-1_12
45
MittalU.RoyS.JainS.SrivastavaA. K.MukerjiM. (2005). Post-zygotic de novo trinucleotide repeat expansion at spinocerebellar ataxia type 7 locus: evidence from an Indian family. J. Hum. Genet.50, 155–157. 10.1007/s10038-005-0233-0
46
OpalP.AshizawaT. (1998). “Spinocerebellar ataxia type 1,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
47
OrrH. T.ChungM.-y.BanfiS.KwiatkowskiT. J.ServadioA.BeaudetA. L.et al (1993). Expansion of an unstable trinucleotide CAG repeat in spinocerebellar ataxia type 1. Nat. Genet.4, 221–226. 10.1038/ng0793-221
48
PaulsonH.ShakkottaiV. (1998). “Spinocerebellar ataxia type 3,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.Pagon.R. A.et al (Seattle, Washington, D.C, USA: University of Washington).
49
PereiraF. S.MonteT. L.Locks-CoelhoL. D.SilvaA. S.BarsottiniO.PedrosoJ. L.et al (2015). ATXN3, ATXN7, CACNA1A, and RAI1 genes and mitochondrial polymorphism A10398G did not modify age at onset in spinocerebellar ataxia type 2 patients from South America. Cerebellum14, 728–730. 10.1007/s12311-015-0666-8
50
PlatonovF. A.TyryshkinK.TikhonovD. G.NeustroyevaT. S.SivtsevaT. M.YakovlevaN. V.et al (2016). Genetic fitness and selection intensity in a population affected with high-incidence spinocerebellar ataxia type 1. Neurogenetics17, 179–185. 10.1007/s10048-016-0481-5
51
PrestesP. R.Saraiva-PereiraM. L.SilveiraI.SequeirosJ.JardimL. B. (2008). Machado-Joseph disease enhances genetic fitness: a comparison between affected and unaffected women and between MJD and the general population. Ann. Hum. Genet.72, 57–64. 10.1111/j.1469-1809.2007.00388.x
52
PulstS. M. (1998). “Spinocerebellar ataxia type 2,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
53
PulstS. M.SantosN.WangD.YangH. Y.HuynhD.VelazquezL.et al (2005). Spinocerebellar ataxia type 2: polyQ repeat variation in the CACNA1A calcium channel modifies age of onset. Brain128, 2297–2303. 10.1093/brain/awh586
54
RamosE. M.MartinsS.AlonsoI.EmmelV. E.Saraiva-PereiraM. L.JardimL. B.et al (2010). Common origin of pure and interrupted repeat expansions in spinocerebellar ataxia type 2 (SCA2). Am. J. Med. Genet. B Neuropsychiatr. Genet.153B (2), 524–531. 10.1002/ajmg.b.31013
55
SanpeiK.TakanoH.IgarashiS.SatoT.OyakeM.SasakiH.et al (1996). Identification of the spinocerebellar ataxia type 2 gene using a direct identification of repeat expansion and cloning technique, DIRECT. Nat. Genet.14, 277–284. 10.1038/ng1196-277
56
SauteJ. A. M.JardimL. B. (2015). Machado Joseph disease: clinical and genetic aspects, and current treatment. Expert Opin. Orphan Drugs3, 517–535. 10.1517/21678707.2015.1025747
57
SenaL. S.CastilhosR. M.MattosE. P.FurtadoG. V.PedrosoJ. L.BarsottiniO.et al (2019). Selective forces related to spinocerebellar ataxia type 2. Cerebellum18, 188–194. 10.1007/s12311-018-0977-7
58
SenaL. S.Dos Santos PinheiroJ.HasanA.Saraiva-PereiraM. L.JardimL. B. (2021a). Selective forces acting on spinocerebellar ataxia type 3/Machado-Joseph disease recurrency: a systematic review and meta-analysis. Clin. Genet.100, 347–358. 10.1111/cge.13888
59
SenaL. S.Dos Santos PinheiroJ.Saraiva-PereiraM. L.JardimL. B. (2021b). Selective forces acting on spinocerebellar ataxia type 3/Machado-Joseph disease recurrency: a systematic review and meta-analysis. Clin. Genet.99, 347–358. 10.1111/cge.13888
60
SenaL. S.FurtadoG. V.FagundesN. J. R.PedrosoJ. L.BarsottiniO.RibeiroP.et al (2023). Spinocerebellar ataxia type 2 has multiple ancestral origins. https://www.medrxiv.org/content/10.1101/2023.09.12.23295432v1.
61
ShizukaM.WatanabeM.IkedaY.MizushimaK.OkamotoK.ShojiM. (1998). Molecular analysis of a de novo mutation for spinocerebellar ataxia type 6 and (CAG)n repeat units in normal elder controls. J. Neurol. Sci.161, 85–87. 10.1016/s0022-510x(98)00270-6
62
SonakarA. K.ShamimU.SrivastavaM. P.FaruqM.SrivastavaA. K. (2021). SCA2 in the Indian population: unified haplotype and variable phenotypic patterns in a large case series. Park. Relat. Disord.89, 139–145. 10.1016/j.parkreldis.2021.07.011
63
SouzaG. N.KerstingN.Krum-SantosA. C.SantosA. S.FurtadoG. V.PachecoD.et al (2016). Spinocerebellar ataxia type 3/Machado-Joseph disease: segregation patterns and factors influencing instability of expanded CAG transmissions. Clin. Genet.90, 134–140. 10.1111/cge.12719
64
StevaninG.GiuntiP.BelalG. D.DürrA.RubergM.WoodN.et al (1998). De novo expansion of intermediate alleles in spinocerebellar ataxia 7. Hum. Mol. Genet.7 (11), 1809–1813. 10.1093/hmg/7.11.1809
65
The Huntington’s Disease Collaborative Research Group (1993). A novel gene containing a trinucleotide repeat that is expanded and unstable on huntington’s disease chromosomes. Cell72, 971–983. 10.1016/0092-8674(93)90585-e
66
ToyoshimaY.OnoderaO.YamadaM.et al (2005). “Spinocerebellar ataxia type 17,” in GeneReviews®. Editors AdamM. P.ArdingerH. H.PagonR. A.et al (Seattle, Washington, D.C, USA: University of Washington).
67
WarbyS. C.MontpetitA.HaydenA. R.CarrollJ. B.ButlandS. L.VisscherH.et al (2009). CAG expansion in the Huntington disease gene is associated with a specific and targetable predisposing haplogroup. Am. J. Hum. Genet.84, 351–366. 10.1016/j.ajhg.2009.02.003
68
YuF.SabetiP. C.HardenbolP.FuQ.FryB.LuX.et al (2005). Positive selection of a pre-expansion CAG repeat of the human SCA2 gene. PLoS Genet.1 (3), e41. 10.1371/journal.pgen.0010041
69
ZhuchenkoO.BaileyJ.BonnenP.AshizawaT.StocktonD. W.AmosC.et al (1997). Autosomal dominant cerebellar ataxia (SCA6) associated with small polyglutamine expansions in the alpha 1A-voltage-dependent calcium channel. Nat. Genet.15, 62–69. 10.1038/ng0197-62
Summary
Keywords
allele dynamics, Machado-Joseph disease, mathematical model, polyglutamine diseases, spinocerebellar ataxia type 2, spinocerebellar ataxia type 3, selective forces
Citation
Sena LS, Lemes RB, Furtado GV, Saraiva-Pereira ML and Jardim LB (2023) A model for the dynamics of expanded CAG repeat alleles: ATXN2 and ATXN3 as prototypes. Front. Genet. 14:1296614. doi: 10.3389/fgene.2023.1296614
Received
18 September 2023
Accepted
27 October 2023
Published
14 November 2023
Volume
14 - 2023
Edited by
Gyaneshwer Chaubey, Banaras Hindu University, India
Reviewed by
Jorge Diogo Da Silva, University of Minho, Portugal
Sandra Martins, Universidade do Porto, Portugal
Douglas Langbehn, The University of Iowa, United States
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© 2023 Sena, Lemes, Furtado, Saraiva-Pereira and Jardim.
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*Correspondence: Laura Bannach Jardim, ljardim@hcpa.edu.br
ORCID ID: Maria Luiza Saraiva-Pereira, orcid.org/0000-0003-3905-9563; Laura Bannach Jardim, orcid.org/0000-0001-6907-5068
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