RESEARCH ARTICLE

Epidemiology of Fragile X Syndrome: A Systematic Review and Meta-Analysis Jessica Hunter,1 Oliver Rivero-Arias,2,3* Angel Angelov,4 Edward Kim,4 Iain Fotheringham,5 and Jose Leal2 1

Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia

2

Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

3 4

Novartis Pharmaceutical Corporation, East Hanover, New Jersey

5

Value Demonstration Practice, Oxford PharmaGenesisTM Ltd, Oxford, United Kingdom

Manuscript Received: 15 November 2013; Manuscript Accepted: 31 January 2014

Prevalence estimates for fragile X syndrome vary considerably. This systematic review and meta-analysis was conducted to provide an accurate prevalence estimate for this disorder using primary publications in PubMed, Embase, and the Cochrane library. Data were pooled using Bayesian fixed-effects and random-effects models. Primary analyses assessed the frequency of the full mutation and premutation in males and females in the total population (no bias against individuals with intellectual disability) and in female carriers of the premutation in normal populations (biased against individuals with intellectual disability), based on diagnosis by polymerase chain reaction or Southern blotting. A sensitivity analysis included studies using any diagnostic testing method and conference abstracts. Sixty-eight recorded observations provided data for the primary (56 observations) and sensitivity (12 observations) analysis. Using the random-effects model, frequency of the full mutation was 1.4 (95% CI: 0.1–3.1) per 10,000 males and 0.9 (95% CI: 0.0–2.9) per 10,000 females (1:7,143 and 1:11,111, respectively) in the total population. The premutation frequency was 11.7 (95% CI: 6.0– 18.7) per 10,000 males and 34.4 (95% CI: 6.3–83.3) per 10,000 for females (1:855 and 1:291, respectively) in the total population. The prevalence of female carriers of the premutation in the normal population was 34.4 (95% CI: 8.9–60.3) per 10,000, or 1:291. Sensitivity analyses resulted in similar prevalence estimates but with wider heterogeneity. Prevalence estimates for the full mutation from this meta-analysis are lower than those in previous reviews of fragile X syndrome epidemiological data. Ó 2014 Wiley Periodicals, Inc.

Key words: fragile X syndrome; epidemiology; full mutation; premutation

INTRODUCTION Fragile X syndrome (FXS) is an X-linked disorder and the most common form of inherited intellectual and developmental disability [Verkerk et al., 1991]. The phenotypes associated with FXS

Ó 2014 Wiley Periodicals, Inc.

How to Cite this Article: Hunter J, Rivero-Arias O, Angelov A, Kim E, Fotheringham I, Leal J. 2014. Epidemiology of fragile X syndrome: A systematic review and meta-analysis. Am J Med Genet Part A 9999:1–11.

include cognitive deficits, behavioral problems and characteristic physical features, all of which can vary considerably among patients [Gross et al., 2011; McLennan et al., 2011]. This disorder is associated with an unstable expansion of a polymorphic CGG repeat within the 50 untranslated region of the fragile X mental retardation 1 gene, FMR1 [Verkerk et al., 1991; Chonchaiya et al., 2009; Levenga et al., 2010; Berry-Kravis et al., 2011; Bagni et al., 2012]. There are four classes of the FMR1 allele. Normal alleles contain 5–45 CGG repeats, with 29 or 30 repeats being most common; these alleles are stable from one generation to the next. The intermediate allele (also termed the “gray zone”) contains 45–55 CGG repeats, which may expand across future generations Conflict of interest: none. Disclosures: A.A. was an employee of Novartis Pharmaceutical Corporation at time of study initiation and E.K. is a current employee of Novartis Pharmaceutical Corporation. I.F. is an employee of Oxford PharmaGenesisTM Ltd., which received funding from Novartis for this study. O.R.-A. received funding from Oxford PharmaGenesisTM Ltd to perform the meta-analysis. J.L. and J.H. have nothing to disclose. Grant sponsor: Novartis Pharma AG.  Correspondence to: Oliver Rivero-Arias, D.Phil., National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 00 Month 2014 DOI 10.1002/ajmg.a.36511

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2 [Fernandez-Carvajal et al., 2009a]. The premutation (PM) allele is defined as containing 55–200 CGG repeats and is associated with a spectrum of phenotypes [Sherman et al., 2005]. First, men (and to a lesser extent women) who have the PM allele are at risk of developing the late-onset, neurological disorder fragile-X-associated tremor/ataxia syndrome (FXTAS) [Hagerman et al., 2004; Jacquemont et al., 2004]. Secondly, women who are carriers of the PM are at risk of developing fragile X-associated primary ovarian insufficiency (FXPOI) [Sherman, 2000; Welt, 2008]. Finally, PM alleles are associated with expansion to a full mutation (FM) in the next generation through maternal transmission [ACOG, 2010]. The FM is an expanded allele containing more than 200 CGG repeats and is associated with hypermethylation of the gene [Godler et al., 2010]. The subsequent loss of the protein product, fragile X mental retardation protein (FMRP), is the cause of FXS [Krueger and Bear, 2011]. The genetics and clinical heterogeneity of FXS have historically made diagnosis, and hence assessment of prevalence, challenging. The X-linked nature of the disease results in considerable variation in the severity of symptoms when comparing males and females. Females tend to be less severely affected owing to compensation by a normal X chromosome and, as a result, 44% of females diagnosed with FXS are able to live independently compared with 10% of males [Hartley et al., 2011]. As such, not all females who carry an FM allele exhibit intellectual disability and other phenotypes associated with FXS. However, a female who carries an FM can transmit the mutation to her child, therefore being at risk of having a child with FXS even though she might not be affected. Thus both women who carry an FM and a PM are at risk of having a child with FXS. Fragile X syndrome is associated with a considerable humanistic, caregiver, and economic burden [Bailey et al., 2009; Bailey et al., 2012; Sacco et al., 2013]. To comprehend the extent of this burden fully, detailed understanding of the epidemiology of the disease is required. Epidemiological evidence can also inform policy-making and has been considered, for example, when assessing whether routine screening for FXS is advisable in the United Kingdom. There is presently little consensus regarding the prevalence of FXS in the total population (i.e., all individuals in the population and not selected based on any specific criteria such as intellectual disability). Early studies at the end of the 1990s suggested prevalence estimates of 2.5 per 10,000 (1:4,000) in the male population and 1.25 per 10,000 (1:8,000) in the female population [Murray et al., 1997; Pembrey et al., 2001]. The only systematic review on this topic was performed in 2003 and examined studies that directly or indirectly estimated the prevalence of FXS in the total population by extrapolating prevalence data from populations with intellectual disability [Song et al., 2003]. Pooled estimates of FXS in the total population were estimated to be 1.4 per 10,000 (1 in 7,143) whereas estimates of prevalence calculated indirectly were reported to be 2.3 per 10,000 (1 in 4,425) in the male population. The authors from this report acknowledged the difficulties in obtaining reliable estimates of prevalence from the extrapolation exercises conducted in the studies. In addition, most of the studies reporting direct data on the frequency of the FM were based on small sample sizes and included healthy volunteers or excluded people with family his-

AMERICAN JOURNAL OF MEDICAL GENETICS PART A tory of intellectual disability, which is likely to bias against individuals with FXS. Within sexes, there is a high degree of inter-patient variability in clinical presentation, the extent of which can change with age [Chonchaiya et al., 2009; Hampson et al., 2011; McLennan et al., 2011; Gross et al., 2012]. In addition to the variability in clinical features, the behavioral manifestations (which often include attention problems, anxiety and autism-like behavior [Chonchaiya et al., 2009; Gross et al., 2012]) can lead to confusion of FXS with other disorders. The molecular genetic basis for FXS was identified in 1991. Before this, clinical diagnoses were made on the basis of mental and physical examinations and cytogenetic testing (which involves the visual examination of chromosomes extracted from patient tissue). This approach is less reliable than polymerase chain reaction (PCR) and Southern blotting techniques, a combination of which is currently considered the optimal method for testing for FMR1 carrier status [Song et al., 2003; Sofocleous et al., 2009]. In recent years, high-throughput PCR and Southern blotting analytical tools have been developed that can accurately and rapidly screen large numbers of samples for CGG repeat length [Tassone et al., 2008; Todorov et al., 2010; Elias et al., 2011]. It is, therefore, now feasible to perform large screening studies within the total population, which will provide a more accurate prevalence estimate than extrapolation from populations with intellectual disability. Recent studies using these screening tools suggest that the prevalence of FXS is in the range of 1:2,500–5,000 [Pesso et al., 2000; Coffee et al., 2009; Fernandez-Carvajal et al., 2009b], although rigorous analysis (e.g., meta-analysis) of the available data is lacking. The aim of the present analysis is to update previous estimates of the prevalence of FXS in the total population based on a quantitative analysis of published literature. To do this, a systematic literature review was performed to identify studies reporting the prevalence of FXS in the total population based on testing with PCR or Southern blotting techniques. Results from the identified studies were then pooled using regression methods that explored sources of heterogeneity; a sensitivity analysis was performed that included studies using any other diagnostic method for FXS as well as abstracts. An additional analysis was performed assessing the prevalence of FXS in populations with intellectual disability before extrapolating to the total population.

MATERIALS AND METHODS Systematic Review Review process. The systematic review and meta-analysis was performed in accordance with current preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines [Liberati et al., 2009] and included publications assessing the epidemiology of FXS. Searches were conducted on May 9, 2012 in PubMed, Embase, and the Cochrane library using Medical Subject Headings, and other associated terms, for FXS, epidemiology, genetic screening, and PCR. Comprehensive search terms were based on those used in the systematic review conducted by Song et al. [2003], although they were expanded to include search terms for screening and epidemiology. Full search terms are presented in the Supple-

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mentary Materials (see Supplementary Tables I–III in Supporting Information Online). Studies estimating the frequency of either the FM or the PM allele in any population were considered relevant, and no limit was placed on language or date of publication. Only primary publications were considered, including both retrospective and prospective studies; reviews and editorials were excluded. Studies assessing three types of population were considered: (1) total population studies assessed the whole population without any selection bias; these studies were typically screening studies of pregnant women and random newborns; (2) normal population studies assessed healthy individuals without any intellectual disability. Studies in normal populations were used only to assess the carrier frequency of FMR1 mutations in females, because these individuals are usually considered to be healthy; (3) populations with intellectual disability were defined individually in each study; definitions included children enrolled in special education classes, those with an IQ of less than 70, or those identified by cognitive, behavioral, and/or physical assessment. The primary analysis was conducted to assess the frequency of the FM and PM alleles, as judged by PCR or Southern blotting analysis, in the total population. Only full papers (as opposed to abstracts or short communications) were considered in the primary analysis owing to the importance of evaluating study methodology. A sensitivity analysis was conducted to assess how prevalence was affected when studies using less rigorous methodology were considered, by including studies using any testing method for diagnosing FXS (e.g., including cytogenetic testing or physical or mental assessment by healthcare professionals) and studies presented as abstracts only. A secondary analysis was performed on studies assessing the frequency of the FM and PM alleles in populations with intellectual disability, before extrapolating to the total population.

Meta-Analysis and Meta-Regression

Screening

 Fixed effects within covariate of interest j: logit ðpFM j;i Þ ¼ mj and logit ðpPM Þ ¼ m ; j j;i  Random effects within covariate of interest j:, logit ðpFM j;i Þ  PM 2 Normal ðmj;i ; t2 Þ and logit ðp Þ  Normal ðm ;t Þ j;i j;i P  Where mj;i ¼ a+ bj xj;i

Each reference was screened independently by two researchers; disputes regarding suitability for inclusion in the systematic review were resolved after discussion with a third researcher. Full papers deemed potentially relevant from their abstracts were reviewed independently by two researchers, and any disputes were resolved after discussion with a third researcher.

Data Extraction Information to be included in a comprehensive data extraction table was agreed by all authors prior to extraction. Data extracted from the selected publications included the number of individuals studied and frequency of individuals with FM/PM alleles identified, as well as sex, geographical region, type of population assessed (e.g., newborn children, individuals with intellectual disability), CGG repeat length ranges and testing methods employed to identify mutations (e.g., PCR, Southern blotting). Where possible, data for males and females were extracted separately. Data extraction was carried out by a single researcher and accuracy was verified by a second researcher; discrepancies were resolved by discussion and any disputes were resolved after discussion with a third researcher.

Data were pooled by type of population (total population and normal females), sex (males, females, and both) and type of carriers of an FMR1 mutation (FM and PM alleles). Only studies with complete information on both the total tested population and the number of carriers were included in the meta-analysis. The identified data report the number of carriers of an FMR1 mutation (either FM or PM allele) out of all tested individuals, which follows a binomial distribution. Each of the data points available were assumed to be independent and expressed by binomial processes, rFM  Binomial ðpFM i i ; ni Þ  Binomial ðpPM rPM i i ; ni Þ

where rFM and rPM represent the number of FM and PM carriers, i i respectively, and ni concerns the number of tested individuals in data point i. We adopted a Bayesian approach, and considered two logistic models:  Fixed Effects, in which it is assumed that every study estimated the same frequency of FM or PM, for example, logit ðpFM i Þ ¼ m;  Random Effects, in which it is assumed that the frequencies of FM or PM in each study varied randomly around an overall mean, for 2 example, logit ðpFM i Þ\simNormal ðmi ;t Þ. The logistic models were further expanded to estimate carrier frequency, conditional on sex (males and females), publication year (before or in/after the year 2000; this date was chosen because the FMR1 mutation was identified in 1991 and it was assumed that sufficient time would have elapsed by 2000 for large-scale studies assessing prevalence to be devised and performed), and geographical region (the USA, Canada and Australia [termed USA/CAN/ AUS], Europe, Asia, and other) while accounting for within- and between-study variation:

The key assumption in random-effects models is the expectation, a priori, that the estimates obtained from the different studies are similar but not identical. We assumed that the degree of variation between the studies t2 was the same across all studies/covariates of interest. As part of the Bayesian approach, all parameters were given vague priors: a;bj  Normalð0; 10002 Þ; t  Uniformð0; 10Þ

Modeling was carried out using a Bayesian Markov chain Monte Carlo simulation using WinBUGS version 1.4.3 [Lunn et al., 2000]. Convergence was achieved after 10,000 simulations according to Gelman–Rubin statistics [Brooks and Gelman, 1998]. The final results were obtained from the subsequent 150,000 simulations averaged over three Markov chains. To assist model selection, we looked at the posterior-corrected mean deviance (Dbar) and the deviance information criterion (DIC), a measure of fit that includes

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a penalty for additional parameters, where lower values indicate a better fit [Spiegelhalter et al., 2002]. The WinBUGS code is available on request from the corresponding author.

RESULTS Systematic Review In total, 5,582 references were identified through the electronic searches, of which 54 were accepted for inclusion in the systematic review (Fig. 1). The main reasons for exclusion were incorrect study type (e.g., reviews, duplicates, and case reports) or studies examining a non-relevant population (e.g., other disease indication or populations biased against individuals with FXS). Of the accepted studies, 31 studies performed population-based screening techniques without biasing against individuals with intellectual disability and were included in the primary analysis [Arinami et al., 1993; Reiss et al., 1994; Dawson et al., 1995; Eichler et al., 1995; Holden, 1995; Spence et al., 1996; Chiang et al., 1999; Pang et al., 1999; Ryynanen et al., 1999; Tzeng et al., 1999, 2005; Drasinover et al., 2000; Geva et al., 2000; Larsen et al., 2000; Pesso et al., 2000; Toledano-Alhadef et al., 2001; Dombrowski et al., 2002; Chow et al., 2003; Huang et al., 2003; Rife et al., 2003; Cronister et al., 2005; Mitchell et al., 2005; Berkenstadt et al., 2007; Saul et al., 2008; Coffee et al., 2009; Fernandez-Carvajal et al., 2009b; Levesque et al., 2009; Otsuka et al., 2010; Hantash et al., 2011; Indhumathi et al., 2012; Seltzer et al., 2012]. An additional eight assessed prevalence using diagnostic methods other than PCR or Southern blotting, or were abstracts that met all other inclusion criteria, and were included in

the sensitivity analysis [Abuelo et al., 1985; Temtamy et al., 1994; Matilainen et al., 1995; Movafagh et al., 2008; Hwang et al., 2010; Field et al., 2011; Tassone et al., 2011; Wotton et al., 2011]. A total of 15 studies assessed prevalence in populations with intellectual disability before extrapolating to the total population [Blomquist et al., 1983; Gustavson et al., 1986; Turner et al., 1986; Webb et al., 1986; Kahkonen et al., 1987; Asthana et al., 1990; Jacobs et al., 1993; Mazurczak et al., 1996; Murray et al., 1996; Arvio et al., 1997; de Vries et al., 1997; Elbaz et al., 1998; Crawford et al., 2002; Meguid et al., 2007; Puusepp et al., 2008] and were assessed separately. Nineteen studies were conducted in USA/CAN/AUS, 16 in Europe, nine in Asia and ten in other countries (Israel, Egypt, Guadeloupe, India, and Iran). Twenty-five studies were published before the year 2000 and 29 in 2000 or after. Most studies used PCR or Southern blotting to diagnose FXS. Most studies contributed more than one data point to the analysis, because frequencies of FM and PM carriers were reported in the same study and/or were reported for different types of population (total population, intellectual disability, and normal female population). Therefore, a total of 87 observations were recorded in the final data extraction table (summarized in Table I). In the primary analysis, there were 24 observations reported for carriers of the FM allele and 22 for carriers of the PM allele in the total population; an additional 10 observations were reported for the frequency of individuals with the PM allele in females in normal populations. All data points for the primary analyses were reported separately for males and females. Seven additional observations were reported for the frequency of individuals with the FM allele in the sensitivity analyses, and an additional five were reported for individuals with the PM. There were 19 observations for populations with intellectual disability, of which the majority were in males. Full details of all studies included in the meta-analysis are presented in the Supplementary Materials (see Supplementary Tables IV–VII in Supporting Information Online).

Meta-Analysis

FIG. 1. Flow diagram of the screening process demonstrating the process used in this analysis.

Primary analysis. For the primary analysis, the aggregated population assessing the frequency of individuals with the FM allele in the total population was over 78,000 males and 75,500 females; 14 and nine affected individuals, respectively, were identified (Table II). The cumulative total population tested for the frequency of individuals with the PM allele was approximately 45,000 males and 89,000 females; 62 and 539 affected individuals were identified, respectively. As no FM carriers were identified in normal female populations, no meta-analysis was conducted. In the studies assessing the frequency of the individuals with the PM allele in normal females, 237 affected individuals were identified in approximately 43,000 women. Table III reports the results and goodness-of-fit statistics (DIC and Dbar) for the two logistic models for the frequency of FM and PM carriers in the total and normal female populations. In all analyses, the random-effects model showed better predictive power and overall fit than the fixed-effects model (DIC was smaller than for the fixed-effects model and Dbar was similar to the number of data points). Nonetheless, the heterogeneity between study obser-

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TABLE I. Distribution of Observations From Studies Providing Direct Estimates of Frequency of the Full Mutation and Premutation in the Systematic Review Analysis Primary analysis

Sensitivity analysis (additional observations to the primary analysis)

Intellectual disability analysis (separate analysis)

Population Full mutation Male—total populationa Female—total populationb Female—normal populationc Premutation Male—total populationd Female—total populatione Female—normal populationf Full mutation Male—total populationg Female—total populationh Male and female—total populationi Female—normal population Premutation Male—total population Female—total populationj Male and female—total populationk Female—normal populationl Malem Femalen Male and femaleo

Total number of observations (n ¼ 87) 13 7 4 13 9 10 1 2 4 0 0 1 3 1 13 4 2

Total population, a randomly screened population with no selection bias; normal population, a population of healthy individuals (i.e., excluding those with intellectual disability). a Chiang et al. [1999], Chow et al. [2003], Coffee et al. [2009], Dawson et al. [1995], Dombrowski et al. [2002], Eichler et al. [1995], Fernandez-Carvajal et al. [2009b], Larsen et al. [2000], Mitchell et al. [2005], Reiss et al. [1994], Rife et al. [2003], Saul et al. [2008], Tzeng et al. [2005]. b Berkenstadt et al. [2007], Chiang et al. [1999], Chow et al. [2003], Coffee et al. [2009], Cronister et al. [2005], Dawson et al. [1995], Dombrowski et al. [2002]. c Indhumathi et al. [2012], Otsuka et al. [2010], Ryynanen et al. [1999], Tzeng et al. [1999]. d Chow et al. [2003], Dawson et al. [1995], Dombrowski et al. [2002], Eichler et al. [1995], Fernandez-Carvajal et al. [2009b], Holden [1995], Larsen et al. [2000], Mitchell et al. [2005], Reiss et al. [1994], Rife et al. [2003], Saul et al. [2008], Seltzer et al. [2012], Tzeng et al. [2005]. e Berkenstadt et al. [2007], Cronister et al. [2005], Dawson et al. [1995], Geva et al. [2000], Huang et al. [2003], Levesque et al. [2009], Pesso et al. [2000], Reiss et al. [1994], Seltzer et al. [2012]. f Arinami et al. [1993], Drasinover et al. [2000], Hantash et al. [2011], Indhumathi et al. [2012], Otsuka et al. [2010], Pang et al. [1999], Ryynanen et al. [1999], Spence et al. [1996], Toledano-Alhadef et al. [2001], Tzeng et al. [1999]. g Matilainen et al. [1995]. h Hwang et al. [2010], Matilainen et al. [1995]. i Movafagh et al. [2008], Tassone et al. [2011], Temtamy et al. [1994], Wotton et al. [2011]. j Hwang et al. [2010]. k Field et al. [2011], Tassone et al. [2011], Wotton et al. [2011]. l Abuelo et al. [1985]. m Arvio et al. [1997], Asthana et al. [1990], Crawford et al. [2002], de Vries et al. [1997], Elbaz et al. [1998], Gustavson et al. [1986], Kahkonen et al. [1987], Mazurczak et al. [1996], Meguid et al. [2007], Murray et al. [1996], Puusepp et al. [2008], Turner et al. [1986], Webb et al. [1986]. n Asthana et al. [1990], Kahkonen et al. [1987], Turner et al. [1986], Webb et al. [1986]. o Blomquist et al. [1983], Jacobs et al. [1993].

vations was high across all logistic regressions, with between-study standard deviation (SD) varying from 0.7 (PM frequency in the male population) to 4.1 (FM frequency in the female population). The high heterogeneity impacts on the predicted frequencies, which are interpreted as a predictive distribution of the frequency of FM and PM carriers that would be expected if a new study was to be performed. These predictions summarize both the uncertainty in the mean estimate and the between-study variation. In the assessment of the frequency of individuals with the FM allele in the total male population, the random-effects model produced a mean prevalence of 1.4 (95% confidence interval {CI}: 0.1–3.1) per 10,000 or 1:7,143, with wide between-study variation (SD, 1.2; 95% CI: 0.1–4.0). Conversely, when assessing the frequency of individuals with the FM allele in the total female population, the mean prevalence was lower at 0.9 (95% CI: 0.0–2.9) per 10,000 or 1:11,111, with significant heterogeneity between the

data points (between-study SD, 4.1; 95% CI: 0.5–9.5). There was no significant difference for males and females when all FM allele frequency data were pooled (Table IV; odds ratio {OR}, 1.8; 95% CI: 0.2–21.7). The mean frequency of individuals with the PM allele in the total male population based on the random-effects model was 11.7 (95% CI: 6.0–18.7) per 10,000 or 1:855, with considerable heterogeneity (between-study SD, 0.7; 95% CI: 0.3–1.3). In contrast, the mean frequency of individuals with the PM allele in the total female population was 34.4 (95% CI: 6.3–83.3) per 10,000 or 1:291. When these data were pooled, there was a lower frequency for individuals with the PM allele in males than in females (Table IV; OR, 0.30; 95% CI: 0.12–0.75). Also, in the normal female population, the mean frequency of individuals with the PM allele was similar to that estimated in the total female population (34.4 {95% CI:8.9–60.3} per 10,000 or 1:291).

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Studies Conducted in Populations With Intellectual Disability

TABLE II. Total Tested Population and Number of Mutations Identified in the Primary Analysis Population Total population—FM Total population—PM Normal population—FM Normal population—PM Intellectual disability—FM

Gender Male Female Male Female Female Female Male Female Male and female

Tested 78,104 75,539 45,253 88,673 2,606 42,647 6,735 510 313

Identified mutations 14 9 62 539 0 237 148 28 9

Total population, a randomly screened population with no selection bias; normal population, a population of healthy individuals (i.e., excluding those with intellectual disability). FM, full mutation; PM, premutation.

Fifteen studies assessed populations with intellectual disability before extrapolating to the total population. These included a total of 7,475 individuals, 178 (2.4%) of whom had the FM. Most of these studies reported only point estimates of prevalence, which varied considerably when extrapolated to the total population (from one FM in 13,942 individuals to one FM in 1,079 individuals [Meguid et al., 2007; Puusepp et al., 2008]). Only three studies reported measures of uncertainty (e.g., CIs, standard errors) [Mazurczak et al., 1996; de Vries et al., 1997; Puusepp et al., 2008], preventing the combination of data. It was therefore not possible to extrapolate from these studies to calculate a valid mean prevalence.

DISCUSSION Table IV also reports the results of the meta-regression evaluating the impact of key covariates on the estimation of prevalence, for which the random-effects model showed the best fit (lowest Dbar value). Using the random-effects model, a lower frequency of individuals with the PM allele was seen in studies conducted in USA/CAN/AUS (OR, 0.58; 95% CI: 0.34–0.99) and Asian (OR, 0.25; 95% CI: 0.11–0.50) populations, compared with those conducted in European populations. Finally, studies published before the year 2000 were associated with a lower frequency of PM carriers than those published in 2000 or after (OR, 0.21; 95% CI: 0.05–0.62).

Sensitivity Analysis Table V presents a summary of the sensitivity analysis, which included abstracts and studies using diagnostic methods other than PCR and Southern blotting. Overall, the inclusion of these studies resulted in wider heterogeneity across all regression models and no significant difference in the prevalence estimates.

This meta-analysis was conducted to provide the most recent FXS prevalence estimate based on the currently available literature and to determine which types of study are most appropriate for developing valid estimates of prevalence. The meta-analysis estimated the prevalence of FM and PM carriers using both fixedeffects and random-effects models. In all population groups, the random-effects model showed better predictive power and overall fit than the fixed-effects model, which is not surprising as the random-effects model was able to accommodate the large betweenstudy variability observed in the data. Based on the random-effects model, the frequency of individuals with the FM allele in the total population was 1.4 per 10,000 or 1:7,143 in males, and 0.9 per 10,000 or 1:11,111 in females. The frequency of individuals with the PM allele in the total population was 11.7 per 10,000 or 1:855 in males, and 34.4 per 10,000 or 1:291 in females. The frequency of the PM allele in a healthy female population was the same as that in the total female population (34.4 per 10,000 or 1:291). These estimates are lower than those reported in the only other systematic review of the prevalence of FXS, conducted by Song et al. [2003] (Table VI). The current study was conducted 10 years after publication of the

TABLE III. Mean Frequency (95% CI) of Full Mutation and Premutation Carriers in Studies Concerning the Total and Normal Female Populations (Primary Analysis) Fixed-effects model

Population assessed FM Male—total population Female—total population PM Male—total population Female—total population Female—normal population

n

Frequency per 10,000 (95% CI)

DIC

13 7

1.8 (1.0–2.8) 1.2 (0.5–2.1)

23.6 18.8

13 9 10

13.7 (10.5–17.3) 60.8 (55.8–66.0) 55.6 (48.8–62.8)

Random-effects model

Dbar

Frequency per 10,000 (95% CI)

Predicted frequency per 10,000

Between-study SD (95% CI)

DIC

Dbar

15.3 11.6

1.4 (0.1–3.1) 0.9 (0.0–2.9)

11.2 (0.0–19.3) 138.2 (0.0–709.3)

1.2 (0.1–4.0) 4.1 (0.5–9.5)

20.5 7.2

11.1 3.7

11.7 (6.0–18.7) 15.6 (2.1–51.2) 34.4 (6.3–83.3) 139.4 (0.5–855.4) 34.4 (8.9–60.3) 62.2 (2.1–216.9)

0.7 (0.3–1.3) 1.6 (0.7–3.6) 0.9 (0.3–2.5)

20.8 20.1 23.1

11.1 10.1 11.7

36.8 31.9 220.0 197.8 64.2 48.7

CI, confidence interval; Dbar, posterior-corrected mean deviance; DIC, deviance information criterion; FM, full mutation; PM, premutation; SD, standard deviation. Total population, a randomly screened population with no selection bias; normal population, a population of normal individuals (i.e., excluding those with intellectual disability).

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TABLE IV. Multivariate Logistic Regression Evaluating the Impact of Key Covariates on the Estimation of Prevalence of Fragile X Syndrome Fixed-effects model Covariate assessed FM Sex Region PM Sex Region 1 Region 2

Publication date

Comparison

Random-effects model

Odds ratio (95% CI)

Dbar

Odds ratio (95% CI)

Between-study SD (95% CI)

Dbar

1.5 (0.7–3.7)

27.0

1.8 (0.2–21.7)

1.4 (0.1–3.7)

15.9

0.5 (0.2–1.8)

26.4

0.3 (0.0–7.0)

1.7 (0.2–5.0)

14.7

0.2 (0.2–0.3)

229.7

0.3 (0.1–0.8)

0.9 (0.5–1.4)

22.8

2.3 (1.4–3.8)

220.5

2.2 (0.5–9.0)

0.9 (0.5–1.5)

22.7

0.6 (0.3–1.0) 2.0 (1.1–3.5) 0.3 (0.1–0.5)

96.7

0.6 (0.2–1.9) 2.6 (0.6–12.4) 0.3 (0.1–1.0)

0.6 (0.3–1.1)

22.7

0.2 (0.1–0.6)

221.7

0.2 (0.0–1.0)

0.8 (0.5–1.4)

22.4

Female Male Europe No Europe Female Male Europe No Europe Europe USA/CAN/AUS Other Asia After 2000 Before 2000

The results from all models are adjusted for sex. Italics represent the reference case in the logistic regression. Bold highlights the statistically significant results. CI, confidence interval; Dbar, posterior-corrected mean deviance; FM, full mutation; PM, premutation; SD, standard deviation.

systematic review from Song et al. [2003] and included more studies, partly due to an increased number of studies that have become available in the public domain and partly reflecting the improvement of diagnostic techniques to identify FXS mutations over the last 10 years. In addition, our study employed an improved statistical analysis in an effort to pool data while accounting for its heterogeneity. This is a considerable improvement over the Song et al. [2003] study, which pooled data without considering the large between-study variability in the prevalence estimates. Therefore, the results presented here reflect an accurate update to the common prevalence estimates reported in the literature.

It is interesting to note that the point estimate for prevalence of individuals with an FM allele is higher in men than it is in women, though it may be expected that both sexes would have an equal chance of inheriting the FM allele, which would lead to equal prevalence in men and women [Hagerman, 2008]. However, the unique inheritance of FMR1 alleles makes it difficult to fully anticipate and explain prevalence comparisons between sexes. For example, FM alleles are only transmitted to the next generation via maternal transmission, either by transmission of an FM allele or the expansion of an PM allele to a FM allele in the next generation. In addition, these FM alleles have an equal likelihood of being passed

TABLE V. Mean Frequency (95% CI) for Full Mutation and Premutation Carriers in the Sensitivity Analysis Fixed-effects model

Gender FM Male—total population Female—total population Male and female—total population PM Male—total populationa Female—total population Male and female—total population Female—normal population

Random-effects model

n

Mean per 10,000 (95% CI)

Dbar

Mean per 10,000 (95% CI)

Between-study SD (95% CI)

Dbar

14 9 4

2.3 (1.4–3.5) 0.7 (0.3–1.1) 3.0 (1.6–4.7)

51.3 46.3 135.6

1.3 (0.0–5.6) 0.8 (0.0–3.9) 101.9 (0.0–731.1)

3.6 (1.6–7.7) 6.1 (2.5–9.7) 6.1 (2.7–9.7)

9.3 4.6 3.4

10 3 1

36.0 (33.3–38.8) 33.1 (24.7–42.7) 55.8 (48.9–63.0)

533.4 1.2 50.4

29.8 (7.5–67.8) 62.7 (6.4–134.1) 37.3 (12.5–63.5)

1.5 (0.7–3.0) 0.8 (0.0–4.9) 0.8 (0.3–2.2)

10.9 2.1 13.8

CI, confidence interval; Dbar, posterior-corrected mean deviance; FM, full mutation; PM, premutation; SD, standard deviation. Total population, a randomly screened population with no selection bias; normal population, a population of healthy individuals (i.e., excluding those with intellectual disability). a No additional studies were included in the sensitivity analysis for assessing frequency of the PM in males in the total population.

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TABLE VI. Comparison of Approximate Prevalence Estimates for Fragile X Syndrome Obtained From the Present Study and the Song et al. [2003] Systematic Review Population assessed Full mutation Male—total population Female—total population Premutation Male—total population Female—total population Female—normal population

Song et al. [2003]

Current study

1:4,000 1:8,000

1:7,000 1:11,000

1:650 1:100 Not reported

1:850 1:300 1:300

Total population, a randomly screened population with no selection bias; normal population, a population of normal individuals (i.e., excluding those with intellectual disability).

on to either her son or daughter. Meanwhile, males who have either a PM allele can only pass on a PM allele to their daughters. Although data are limited on the male transmission of FM alleles, it appears that the sperm of males with the FM allele only contain the PM allele [Reyniers et al., 1993]. In the present study, although the prevalence estimate for individuals with the FM allele was slightly higher in males than females, the 95% CIs for these estimates overlapped. Therefore, it cannot be concluded that individuals with the FM allele are more prevalent in males than females. It is also important to note that the prevalence of FM alleles is not equivalent to the prevalence of FXS. Due to the X-linked nature of FMR1, it would be expected that the prevalence of FXS (i.e., presenting with symptoms of the disease) in females would be roughly half that observed males. Therefore, previous studies extrapolating prevalence rates to the total population based on rates in populations with intellectual disability are likely to have overestimated prevalence in males, as they are more likely to be ascertained through populations with intellectual disability compared with females. Approximately 2.4% of individuals with intellectual disability identified in this study had the FM. Studies assessing the prevalence of the FM or PM allele in these populations were originally intended for inclusion in the meta-analysis. However, this was not possible owing to high variability among these studies and a lack of reported measures of uncertainty. Variation in study design principally arose from different approaches to identifying those with intellectual disability (e.g., enrollment in special education classes, receiving treatment for mood disorders/intellectual disability, poor performance in school examinations, or low IQ scores) and screening method (in several studies, only a small selection of patients were tested for FXS after being identified by subjective parameters such as physical characteristics or intellectual disability). In addition, many of these studies were conducted or published before 1991, when the CGG expansion was first identified, and used inaccurate and unreliable cytogenetic testing, which has now been superseded by PCR and Southern blotting. The high variability in study design means that this approach for obtaining prevalence estimates is likely to be less accurate than those using recently developed, validated, high-throughput analytical tools for the diagnosis of FXS [Coffee et al., 2009]. A meta-regression was performed to assess the impact of covariates on the prevalence of individuals with the FM or PM allele.

There were sufficient data to assess only sex and region as predictors of the FM, owing to limited numbers of studies assessing covariates, and neither sex nor geographical region was found to be significant. Using the random-effects model, only gender was identified as being a significant predictor of the frequency of the PM allele, with males being associated with a lower frequency than females, as expected from the literature and our genetic understanding of the disease [Bagni et al., 2012]. Higher rates were estimated in European populations than in USA/CAN/AUS and Asian populations, although the differences were not significant. A high frequency of the PM allele in Europeans has also been noted in comparison with individuals of African-American or Latino descent [Dombrowski et al., 2002; Fernandez-Carvajal et al., 2009b]. Further study will be required to assess whether this relates to a genuine predisposition to the disease, which is considered unlikely as the mutation is relatively simple, or variability in the demographics or rates of diagnosis between Europe and USA/CAN/AUS during the review period. Studies conducted before the year 2000 were also associated with a lower frequency for the PM allele than those conducted in 2000 or after, although this finding was not significant. These findings are not surprising, as diagnostic tools and awareness of FXS have improved considerably in recent years. The prevalence estimates from the sensitivity analysis that included other diagnostic methods and conference abstracts were similar to those obtained in the primary analysis, although there was greater heterogeneity across the studies. Alternative diagnostic methods included cytogenetic testing (now considered inaccurate and unreliable) or no active testing (i.e., patients were reported based on previous diagnoses for which methods were not reported), while conference abstracts generally lacked information regarding the populations sampled or the methods employed. These factors are likely to contribute to the greater heterogeneity observed in the sensitivity analysis compared with the primary analysis. These data suggest that the most reliable estimate of the prevalence of individuals with FM or PM alleles will be obtained by including only studies using PCR and Southern blotting techniques. This is the most comprehensive systematic review and metaanalysis conducted to date on available data regarding the prevalence of individuals with FM or PM alleles. Findings from this study are likely to have implications regarding screening for individuals with the PM and FM alleles (both within the total population and those with a family history of intellectual disability), by informing on the cost-effectiveness of screening and genetic counseling regarding the likelihood of detecting an expanded allele. It has been proposed that unbiased screening of at least 50,000 individuals is needed to accurately estimate prevalence [Hagerman, 2008]. Therefore, although our results provide the most robust prevalence estimates for individuals with the FM or PM allele possible at present, future large-scale studies are needed. Our results also provide some indication of differences in prevalence across regions, although the random effects model indicated they were not significant. Further study in this area is clearly warranted. The study has some limitations. The available data on the frequency of FM and PM in the total population was limited. For example, only two of seven FM studies in female populations and five of 14 FM studies in male populations reported any affected

HUNTER ET AL. patients, which is not surprising considering the small sample sizes examined in most studies. There was also considerable heterogeneity across some studies which may suggest that the protocols for data collection may have selectively included high-risk individuals or repeat specimens. This supported the use of a random-effects model as a way of incorporating heterogeneity that could not be fully explained, with the results showing the choice to be appropriate as it fitted the data more closely than the fixed-effects model. However, the number of studies available for synthesis limited the exploration of possible causes of heterogeneity and resulted in a wide CI around both the frequency estimates and the betweenstudy variation. In conclusion, the estimated frequencies of individuals with the FM allele in the total population are approximately 1:7,000 for males and 1:11,000 for females, and the frequencies of individuals with the PM allele in the total population are approximately 1:850 for males and 1:300 for females. In addition, the prevalence of PM carrier females in the normal population is approximately 1:300. Estimates obtained in this study are lower than those reported in earlier reviews. Overall, these findings provide the most up-to-date and robust estimate of the prevalence of individuals with the FM or PM alleles available from the literature, but also highlight the need to pursue further primary research into the prevalence of this condition.

ACKNOWLEDGMENTS The authors thank Arne Blackman (Value Demonstration Practice, Oxford PharmaGenesisTM Ltd., Oxford, UK) for help in conducting the searches, screening and data extraction, Raquel Lahoz (Novartis Pharma AG, Basel, Switzerland) for analytical input and editorial support, and Adam Giles (Value Demonstration Practice, Oxford PharmaGenesisTM Ltd.) for help in reference screening, and editorial support in collating authors’ comments and preparing the final manuscript for submission.

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