Thomas Niedomysl
email: thomas.niedomysl@framtidsstudier.se

O15 -Human Resources|Income Distribution|Migration <

Keywords: Migration, Motives, Distance, Survey data, Sweden JEL codes: O15, O18, R11, R23

Migration researchers have long known that motives for changing place of residence vary over migration distance. Typically, short-distance moves are regarded as motivated by housing considerations and longer-distance moves primarily by employment considerations.

Using a large-scale survey on migration motives, this paper explores how migration motives change over migration distance. Particular attention is paid to variations across socioeconomic and demographic groups. The results show that the housing-versus employment-driven migration dichotomy, over short and long distances, respectively, is still somewhat valid, though the present findings give a much more nuanced interpretation. The paper reveals considerable variation in migration motives, not only over migration distance, but particularly in relation to migrant socioeconomic and demographic characteristics.

INTRODUCTION

It is commonly believed that peoples' motives for changing their place of residence vary over migration distance. Typically, short-distance moves are regarded as motivated by housing considerations and longer-distance moves primarily by employment considerations (e.g., [START_REF] Gordon | A model of labour-market interdependencies in the London region[END_REF][START_REF] Gordon | A multi-stream analysis of the changing pattern of interregional migration in Great Britain, 1960-1991[END_REF][START_REF] Huang Y | The life course and residential mobility in British housing markets[END_REF]. While this notion forms an important basis for the existing understanding of migration, it rests on surprisingly little empirical research.

Two factors help explain why the relationship between migration motives and migration distance has been inadequately researched. First, data limitations have forced most studies to use movement across or within administrative boundaries when defining migration as either short or long distance (WHITE and MEUSER, 1988). However, migration usually varies greatly in terms of distance and geographical context, simply because different regions vary in size, shape, and settlement pattern. In the extreme, an individual may move a short distance to a neighbouring house and still be considered a long-distance migrant if he or she crosses an administrative boundary. Second, the standard procedure for collecting data on migrant motives has been to use surveys with fixed response alternatives. It has recently been suggested that such approaches may be more problematic than previously assumed [START_REF] Niedomysl | Do open-ended survey questions on migration motives create coder variability problems?[END_REF]. Not only can bias arise from suggesting response alternatives to respondents, it is also difficult to know what alternatives to include, how to phrase them, and whether all alternatives are equally suitable for the migrant groups of interest.

The overarching aim of this paper is to determine how migration motives change over migration distance, paying particular attention to variations across socioeconomic and demographic groups. The data employed not only contain detailed information on migrant characteristics and migration distance, but, more importantly, draw on a recent large-scale survey in which migrants describe, in their own words, their reasons for migration. Taken together, this enables exploration, in greater detail than previously possible, of the relationship between migration motives and migration distance. The paper is well positioned to contribute significantly to the migration literature.

RESEARCH CONTEXT

Migration is a significant event in most people's lives: it is not something undertaken lightly, without seriously considering its consequences (LEE, 1966;[START_REF] Fielding | Migration and culture[END_REF]. However, there are key differences between different types of moves, and distance moved is obviously important in most cases. People who only move a short distance can usually continue living the same way as before the move, but people who move a longer distance are likely to experience a more dramatic change. This was discussed some time ago by [START_REF] Roseman | Migration as a spatial and temporal process[END_REF], who made a very useful pedagogical distinction in separating migration into two categories based on the daily/weekly reciprocal movement patterns of a migrant. His two categories, 'total' and 'partial' displacement migrations, are determined by whether a migrant changes all (total displacement) or only some (partial displacement) of the significant places (e.g., school, workplace, and grocery store) visited on a daily/weekly basis. Roseman's two categories arguably highlight the differences in general importance between short-and longdistance migration and why it is valuable, though problematic, to distinguish between them.

From a theoretical perspective, the importance of distance for migration is often framed within a human capital approach (e.g., [START_REF] Sjaastad | The Costs and Returns of Human Migration[END_REF], where migration is seen as an investment undertaken only if the expected returns exceed the benefits of remaining in the same place. Migrants are accordingly assumed to seek out places offering the greatest expected net return of benefits [START_REF] Shields | The emergence of migration theory and a suggested new direction[END_REF]. Migration distance is considered to have a deterrent effect, primarily due to lack of information about potential destinations, the costs of moving, and the loss of location-specific capital at the place of origin (DAVANZO and MORRISON, 1981). However, as far as the author is aware, little has been said about how various migration motives might fit into that framework, assuming that motives vary over migration distance.

In a well-known study, [START_REF] Gleave | Migration dynamics and labour market turnover[END_REF] were likely the first to explicitly examine the relationship between migration distance and migration motives. Using survey data from 1970 on the migration motives of British homebuyers, they were able to demonstrate a substantial increase in the prevalence of employment-related motives over migration distance. While only approximately 2% of those moving up to 8 km reported employment as the main motive for migration, the proportion citing this motive rose to approximately one third of respondents moving 40-80 km and to 70% for those moving distances exceeding 160 km. The converse was found for migration motives related to housing and marriage: some 40% of respondents cited housing reasons when moving up to 8 km and 36% cited marriage, but for migration distances greater than 160 km, these categories only made up 3% and 3%, respectively.

A quite unusual study is that of KÜHNL (1978), who drew on data from the Czech Socialist Republic, where migrants registered their main reason for migration when registering for permanent residence in a new location. Migration distances were inferred indirectly by referring to movement across boundaries at different scales (i.e., communities, districts, and regions), and data are provided for each year from 1966 to 1973, making it a unique source of information. Interestingly, most migrants, regardless of migration distance, cited housing reasons for moving. According to KÜHNL (1978, p. 4), this is the result of specific Czechoslovak conditions, including poor housing conditions and policies to level out economic differences between regions. Short-distance migration (i.e., between communities within districts) was strongly dominated by housing reasons, cited by over 50% of respondents. However, even when it comes to long-distance migration (i.e., between regions), housing constitutes the most common reason, cited by 35% of migrants in 1966 and 38% in 1973. Labour market migration motives vary quite significantly with time and migration distance: in 1966, this motive accounted for 23% of short-distance moves (14% in 1973) and 40% of long-distance moves (30% in 1973). [START_REF] Owen | Migration Patterns and Trends[END_REF] analysed data similar to those used by [START_REF] Gleave | Migration dynamics and labour market turnover[END_REF], from a survey carried out in Britain in 1981, also of house purchasers with mortgages from a particular company. Their findings indicated that housing (mainly that current accommodations were too small) and life cycle factors (mainly getting married) were clearly the main motives for short-distance moves (up to 16 km). Very few respondents reported housing-related reasons for long-distance moves. Work-related reasons displayed an opposite pattern, increasing dramatically in importance with increasing migration distance. While only 3% of respondents cited work-related reasons for moving up to 8 km, this proportion rose to 25% for those who moved 16-40 km and then more than doubled to 53% for those who moved 40-80 km. Employment-related migration accounted for nearly 80% of moves of distances exceeding 160 km. [START_REF] Huang Y | Linking Migration and Mobility: Individual and Contextual Effects in Housing Markets in the UK[END_REF] also used British survey data (the British Household Panel Sample) covering the 1990s, but employed a cruder definition of short-and longdistance migration, i.e., more or less than 50 km. They found that the largest single motive for long-distance migration was employment (cited by 36%), followed by family change (23%) and housing-related motives (22%). The most important motives for short-distance moves, on the other hand, were clearly housing related, being cited by 45% of respondents, followed by family change (19%) and employment-related motives (7%). common reason for long-distance migration (defined as moving across county boundaries), cited by slightly over a third of respondents, followed by housing, cited by 27%. Housing was a much more common reason for short-distance migration, being cited by 58% of respondents, while employment was cited by only 9%. The data, however, are not easily interpreted, since one category of migration motive was 'unintended' -somewhat ambiguous compared with the other categories.

The U.S. Census Bureau regularly collects information on migration motives in their Current Population Survey [START_REF] Schachter | Why People Move: Exploring the March 2000 Current Population Survey[END_REF]. This large-scale survey asks migrants about their main reason for moving, and the data allow for detailed analyses of motives and individual characteristics. Using county boundaries to define long-and short-distance migration, the results indicate that the most common motive for short-distance migration is housing (cited by 65% of respondents) followed by family-related (26%) and work-related motives (6%). While family reasons are equally important motives for long-distance migration, cited by 27% of respondents, work-related reasons increase in importance, being cited by 31%; housing is less important, but is still the single most important motive, being cited by 32% of long-distance migrants.

The studies referred to above have clearly improved our knowledge by demonstrating that migration motives vary with migration distance; however, it should be noted that these studies are somewhat limited. For example, while [START_REF] Gleave | Migration dynamics and labour market turnover[END_REF] and [START_REF] Owen | Migration Patterns and Trends[END_REF] contain excellent information on migration distance, they are limited to a specific group of migrants (house buyers). In contrast, CLARK and[START_REF] Huang Y | Linking Migration and Mobility: Individual and Contextual Effects in Housing Markets in the UK[END_REF][START_REF] Withers S | Family migration and mobility sequences in the United States: Spatial mobility in the context of the life course[END_REF], and the U.S. Census Bureau [START_REF] Schachter | Why People Move: Exploring the March 2000 Current Population Survey[END_REF] use a cruder definition of short-and long-distance migration but use more recent data. These differences, along with differences in questionnaire design, make comparisons between the studies problematic. Nonetheless, all these studies indicate - In Sweden, on which the present research focuses, studies using material other than survey data on migration motives emphasize the influence of labour market factors on migration, when it comes to analysing both aggregate migration flows (e.g., [START_REF] Fredriksson | The dynamics of regional labor markets and active labor market policy: Swedish evidence[END_REF][START_REF] Nakosteen R | The effects of regional migration on gross income of labour in Sweden[END_REF][START_REF] Eliasson | Geographical mobility in Sweden -does the influence of labour market related factors diminish over time?[END_REF] and the residential preferences of potential migrants [START_REF] Niedomysl | Residential preferences for interregional migration in Swedendemographic, socio-economic and geographical determinants[END_REF].

Simple as this may seem, the notion that there is a straightforward distinction between short-distance housing-related migration and long-distance employment-related migration has been increasingly questioned [START_REF] Halfacree | A Utopian Imagination in Migration´s Terra Incognita?[END_REF][START_REF] Withers S | Family migration and mobility sequences in the United States: Spatial mobility in the context of the life course[END_REF]. While some claims refer directly to a lack of empirical research, arguments have been made linking this issue to more general societal changes, citing the decline in the importance of employment considerations in determining migrants' destination choices (e.g., [START_REF] Fotheringham | Measuring Destination Attractivity: A Migration Example[END_REF]. For example, the ageing of western societies means that an increasing share of the population will no longer have to live near the workplace. Moreover, ever faster commuting and new information and communication technologies will likely mean less dependence on living close to the workplace. If it is at all true that 'migration begins where commuting ends' [START_REF] Halfacree | A Utopian Imagination in Migration´s Terra Incognita?[END_REF], such changes will very likely affect migration. In addition, much employment nowadays requires higher education. University towns not only attract business and industry, but they conveniently also attract students, so that the workforce may already be in place when the students finish their education. In this case, however, the migration that brought the students near their employers is no longer easily categorized as employment related. For these reasons, it can be speculated that the factors determining migration may have changed. Obviously, such claims -if valid -will likely vary between migrant groups, an issue that calls for empirical testing.

RESEARCH DESIGN

Survey data

This paper uses data from Sweden, a country where high-quality, detailed data are available for migration research. Survey data were collected in collaboration with Statistics Sweden in spring 2007 using a postal questionnaire sent to a stratified sample of 10,000 migrants from the total population of 244,704 migrants who had moved at least 20 kilometres in 2006. This group was stratified by migrant sex, age (four age groups in a total range of 18-74 years), and migration distance (four groups). The questionnaire contained 40 questions covering various aspects of the migration and the migrants' individual characteristics. After two reminders, 4909 migrants had returned useful responses. In addition, data from the official registers were added by Statistics Sweden, including variables such as migration distance, income, and a calibration variable assigning a weight for each respondent according to his/her share of the total migrant population. The calibration variable also reduced skewness originating from non-responses, making it possible to draw conclusions about the whole migrant population.

Method

The first part of the analysis uses descriptive measures to explore how migration motives vary over migration distance for different socioeconomic and demographic groups. In the second part of the analysis, binary and multinomial logistic regression modelling is used to statistically determine the influence of migrant characteristics on migration motives. Logistic regression is an appropriate method to use when the key dependent variables are categorical, as is the case with migration motives (a similar approach was previously employed in a study of migration motives by VON REICHERT, 2001). First, binary logistic regressions are run to indicate whether there are significant differences between particular migration motives and the motives cited by other migrants. One regression model is run for each of the six dependent variables (i.e., migration motives), and the models estimate the effect of the independent variables (i.e., migrant characteristics and migration distance) on the probability of migrating for a specific motive. Second, since the results indicate significant differences, and since the binary model, as it is specified, does not allow comparisons across the specific groups (e.g., employment versus education), use of a multinomial logit model is justified. The reference category in the multinomial model constitutes those migrants who cited employment, since this was the most frequently mentioned migration motive (see Table 1 below).

Variables

Dependent variables. The dependent variables, six different categories of migration motives shown in Table 1, were derived from the survey where the respondents had been asked to state the main motive for their most recent migration. This open-ended question offered no response alternatives and let respondents respond in their own words, in order to avoid the bias that may arise from suggesting specific alternatives. These responses were transcribed and roughly sorted according to their wording, then coded into 23 initial categories before being sorted into six final categories. The 23 initial categories were quite detailed, and in the vast majority of cases there was little uncertainty as to the category to which a specific response belonged. Housing, for example, one of the six final categories, consisted of five initial categories: 'smaller/more easily maintained dwelling', 'larger/spacious dwelling', 'housing economics/leases', 'neighbours', and 'other dwelling-related' (the 23 initial categories are provided in [START_REF] Niedomysl | Do open-ended survey questions on migration motives create coder variability problems?[END_REF]. It should be noted that while the responses were given by individuals, the open-ended approach allowed for coding the responses at the household level when appropriate. For example, in cases where a respondent referred to his/her spouse getting a new job as the main reason for migration, such responses were coded as employment related to take into account the fact that migration decisions are usually made jointly amongst household members (see e.g., [START_REF] Bailey | Migration, care, and the linked lives of dual-earner households[END_REF][START_REF] Bushin | Researching Family Migration Decision-Making: A Children-in-Families Approach[END_REF][START_REF] Cooke | Migration in a family way[END_REF]. These cases were relatively few, except in the employment category where 13% of all employment-related motives were coded in this way.

---TABLE 1 ABOUT HERE ---

Although response coding is central to data reliability, potential problems associated with coding process should not be exaggerated. For example, 97 respondents had simply written the word 'work' (arbete) and 17 respondents had written 'a job' (arbetet), leaving little room for misunderstanding. Nonetheless, since not all responses were as simple as this, a more sophisticated test was carried out whereby five coders independently coded 500 randomly selected responses to test the level of agreement between the coders. Krippendorff's alpha (a standard measure in content analysis expressing the extent to which the observed amount of agreement among coders exceeds a completely random coding) was used and yielded an alpha value of 0.82, establishing the reliability of the open-ended questions approach. Furthermore, the test indicated that it was relatively more common for the coders to disagree on responses that, according to most coders, belonged to the 'other reasons' category. The 'housing' and 'living environment' categories also displayed somewhat higher rates of inter-coder discrepancy. Further details of the motive categories and the reliability test The use of migration motives as dependent variables, instead of migration distance, warrants a brief discussion. Though the migration decision making process that leads to the decision to move for a specific motive is not known, people are more likely to first come up with the idea of moving (for whatever reason), and then consider the pros and cons of moving versus staying in place (where distance may clearly be influential). This motivates the inclusion of distance as an independent variable, but it is still not possible to say that there is a straightforward causality running from distance to motive, since only the outcome is known (i.e. an individual moved for a specific motive over a certain distance). Nothing is known about those who may have considered moving, but decided not to do so, nor about the role distance may have played for that decision. However, this is not considered a problem since the main purpose of this paper is not to determine causality, but rather to examine how these two variables interact. Independent variables. A summary of the independent variables is provided in Table 2 and briefly explained in the following. Two variables that describe the respondents' demographic characteristics are included: gender and age. The four age groups are intended to correspond broadly to different life phases. Four variables that describe the respondents' socioeconomic characteristics are included: civil status, education, income, and occupation. Information on civil status is drawn from register data and, although the categories are self-explanatory, it should be noted that people registered as single, divorced, or widowed may still be living in a relationship (only marriages are included in the official registers). Education is divided into four categories. Compulsory schooling in Sweden lasts nine years and upper-secondary school adds three more, after which it is possible to embark on a university education. The ---TABLE 2 ABOUT HERE ---Three income groups were calculated using the lower and upper quartiles of the Swedish population's annual disposable income (the amount left after tax) as cut-off points. The lowincome group thus consists of people with disposable income of under SEK 120 thousand per year while the high-income group consists of people with disposable income exceeding SEK 210 thousand. Information on the respondents' occupation before migration was drawn from the survey and divided into four groups: employed (including the self-employed), unemployed, students, and retirees. Very few respondents, mainly those on sick leave for over three months or who categorized themselves as maintaining the household, were excluded.

Finally, migration distance was divided into five categories. Note that the (non-weighted) sample size for migration of 101-150 km is relatively small, so caution should be exercised in the any analyses of results for this distance.

RESULTS

Descriptive results

The results presented in Fig. 1 clearly indicate that migration motives vary over migration distance. Motives that refer to housing, employment, and education all display significant variation. Housing-related motives dominate the shortest migration distance, being cited by 35% of migrants, but drop considerably in importance with longer distances where they are cited by only a small proportion of migrants. Almost the opposite pattern is found for education-and employment-related migration. Migration to begin higher education is negligible over short distances, but its share steadily increases and peaks at 34% of migrants having moved 101-150 km, before decreasing to 23% over the longest distances.

Employment-related migration displays a similar pattern and is the most often mentioned motive of long-distance migration. Taken together, these last two motive categories account for 50-60% of migration of distances exceeding 100 km.

---FIG. 1 ABOUT HERE ---However, not all categories of migration motives exhibit such dramatic changes. Social motives, motives referring to the living environment, and the 'other reasons' category vary only slightly over migration distance (although to a lesser extent as regards living environment, which appears to be more important over shorter distances). Nonetheless, social reasons are very prominent over all distances, making up approximately one quarter of the cited migration motives. Closer inspection of the open-ended responses turned out to be particularly valuable for as regards social motives. It was revealed that short-distance movers more often had responded that they had moved in with someone or separated, whereas longdistance movers more frequently reported moving in order to come close to family and friends. Also notable is the proportion of migrants citing living environment, which, together with the arguably closely related housing motive, account for over 50% of migrations of 20-35 km.

The finding that most migrants cited reasons other than employment, even for migration over longer distances, warrants further clarification. It should not be taken to imply that employment has lost its importance for migration. In fact, the situation looks quite different when comparing those who cited employment as their main migration motive with those who, in response to another question, stated that they changed their workplace when migrating (see Fig. 2 below). While only 15% of all short-distance migrants said that they changed their workplace and 16% cited employment as their most important migration motive, that relationship changes quite significantly over increased migration distance. Approximately 60% of those who migrated a distance exceeding 100 km reported changing their workplace, but only 30% cited employment as their main migration motive. Even greater differences are registered for those who migrated 150 km or more. Hence, it is obvious that a migrant may cite a factor other than employment as most important migration motive, but still change his or her workplace. Employment is thus likely to constitute a precondition for migration in some cases and a driving force in others. Over shorter distances, people may choose to commute instead of migrate [START_REF] Lindgren | Geographical Labour Mobility: Migration or Commuting?[END_REF].

---FIG. 2 ABOUT HERE ---While it is beyond the scope of this paper to give a full descriptive review of the relationship between migrant characteristics, migration motives, and migration distance, a summary is provided in Fig. 3 (note that only two distance categories, 20-35 km and >150 km, are shown in the figure). Starting with gender differences (3.1), men are more likely to cite employmentrelated motives than are women, who more often cite education and social reasons. Migration distance does not appear to change these relationships between the sexes to any great extent.

Looking at age differences (3.2), it is not surprising to find that young people frequently cite education as a motive for long-distance migration (though 45% of migrants is still notably large), nor is it surprising that employment reasons are prominent for migrants 26-59 years old. Since employment and education are not concerns of the oldest age group, more of them are likely to move for housing, living environment, and social reasons (the last only in the ---FIG. 3 ABOUT HERE ---One finding as regards income differences (3.3) is a slight tendency for people with low incomes to move more often over short distances for employment reasons, while the opposite is found for people with high incomes, who tend to move more often over long distances for employment. The latter group also tends to move more often over longer distances for social reasons.

The most conspicuous differences between the different educational groups (3.4) appear over long-distance migration. Having a university degree (requiring more than three years of study) considerably increases the likelihood of employment-related migration. Migrants with only the nine years of compulsory education are far more likely to cite social reasons as the most important motive for migration. The two intermediate educational groups set themselves apart by having a much larger share of education-related migration.

Occupation before migration (3.5) is an individual characteristic that varies considerably in relation to migration motive and distance. Most findings were quite expected, such as the finding that students migrate more often for education reasons and that 47% of unemployed respondents cite employment-related reasons as the main reason for migration.

Other results may appear somewhat more unexpected, such as the result that a notable proportion of retired migrants cited social reasons (48%) and housing (20%) as their main motives for long-distance migration. Finally, the four civil status categories (3.6) vary considerably between both short-and long-distance migration. Married and widowed respondents very often cited housing reasons for moving short distances. Those categorized as separated frequently cited social reasons for migration, though this was partly an effect of the motive categorization where separation and divorce were considered social reasons. However, widowed respondents were even more likely to cite social reasons for long-distance migration.

Regression results

The descriptive statistics presented above, while interesting in various ways, only show one individual characteristic at a time and hence can only hint at how different characteristics interact. Table 3 presents the results of the binary logistic regressions that take into account how migrant characteristics influence the likelihood of moving for a specific reason (only statistically significant variables are reported in the table).

People moving for education are likely to be women, very young (18-25 years old), single, students, and long-distance migrants.

Employment-driven migrants are more likely to be male, married or single, highly educated, unemployed, and move long distances. Somewhat unexpectedly, high-income migrants are much less likely to move for employment reasons (people with middle incomes are the most likely to move for this reason). This could presumably be explained by the expectation that people with high incomes are likely to have reached a career phase in which employment is no longer a key driver.

There is a positive relationship between increasing age and the probability of citing living environment as the main reason for migration. Compared with singles, married migrants are more likely to emphasize living environment; widowed migrants, in contrast, are less likely to do so. People with middle and high incomes, as well as retired people, are more ---TABLE 3 ABOUT HERE ---Older people are more likely to move for housing reasons, as are married and widowed migrants. Migrants with more education are also more likely to cite housing, although less so than are retired migrants. There is a negative relationship between migration distance and the probability of moving for environmental reasons.

Presumably reflecting traditional gender roles in society, women are found to have a higher probability of moving for social reasons. However, the social reasons category is somewhat ambiguous, as it includes respondents who stated that they had, for example, moved in with someone, separated, or moved to be closer to family and friends. In terms of age differences, the 26-37 year olds set themselves apart in having a notably higher probability of moving for social reasons, and the same is true of divorced and widowed migrants. However, some caution should be exercised regarding the respondents' civil status and the social reasons category, since a social reason could in fact relate to civil status (e.g., moving due to divorce).

Table 4 presents the results of the multinomial logistic regression where migrants who cited employment reasons constitute the reference category. Overall, and as expected, the conclusions of the multinomial logistic regression are very similar to those drawn from the binary logistic regressions. However, it can now be firmly concluded that there are significant differences between the groups. For example, men are more likely to have moved for employment reasons whereas women have significantly higher odds of moving for any of the other categories of motives. In particular, women are almost twice as likely to have cited a social reason for moving. In terms of age differences, the oldest age group stands out. It is hardly surprising that older migrants are unlikely to have moved for employment reasons, but the findings presented here may contribute to an improved understanding of other research. For example, [START_REF] Nivalainen | Determinants of family migration: short moves vs. long moves[END_REF] assumed, in agreement with most economic literature, that long-distance migration is mainly driven by employment considerations but found, against expectations, that older migrants in Finland were more likely to move over longer distances compared to younger migrants. The results presented here illustrates that when older migrants move, they are driven by a variety of non-economic factors (see also Fig. 3.2), but it should of course be kept in mind that in absolute numbers, older migrants only account for a minority of long-distance migrants.

---TABLE 4 ABOUT HERE ---Lastly, note the confirmation that long-distance migrants are more likely to move for education reasons compared to employment reasons. Though this is clearly an interesting finding, considering the hegemonic role usually ascribed to employment for explaining longdistance migration. However, it should presumably not be taken to imply that the importance of employment for long-distance migration has been overthrown, but -recalling Fig. 2 -is more likely to reflect the role of employment as a precondition for migration in most cases.

Arguably, migration for educational purposes is less likely to be obscured by other reasons for moving.

CONCLUSIONS

The departure point of this paper was the notion that people's migration motives vary over migration distance, short-distance migration traditionally being regarded as housing related and long-distance migration as driven by employment considerations. The present findings confirm that, while there is still some validity to such claims, they are highly general notions whose relevance varies considerably depending on the migrant group. This paper demonstrates the importance of acknowledging the diversity of migrants in terms of their socioeconomic and demographic characteristics. While migration researchers have known of this for quite some time, mainly from qualitative studies or studies focussing on migration to specific environments, it has previously only been demonstrated in quite specialized studies restricted to specific migrant groups. The present paper has demonstrated, more comprehensively than previous research has, how migration motives vary over migration distance and with a range of individual migrant characteristics. However, it should be recalled that moves of under 20 km were not examined in this study, which restricts the conclusions that can be drawn about why people move across the entire spectrum of migration distances.

While the broad scope of the paper precludes highly detailed discussion of the results, it should be noted that all but one of the individual migrant characteristics (i.e., age, civil status, education, income, occupation, and migration distance) displayed varying degrees of statistically significant influence on the probabilities of moving for different migration reasons (i.e., education, employment, living environment, housing, social, and other reasons).

The only exception was gender: men and women were found to have the same probability of citing living environment and other reasons as their main migration motive, though they did have different probabilities of citing the other four migration reasons.

The fact that motives varied considerably between different groups has clear implications, not only for understanding migration more generally, but arguably also for how migration could be more successfully researched using other methodological approaches. For example, macro approaches to migration that focus on aggregate migration flows usually treat migration flows from one region to another rather crudely; for example, gross or net migration between regions is explained by aggregate measures of regional unemployment. If such approaches paid more attention to migration distance and the characteristics of the migrants that constitute these flows, it would likely be rewarding. For example, this paper has demonstrated that while long-distance migrants may cite reasons other than employment (though largely depending on the migrants' characteristics), most of them do change their workplace when migrating. In a clear majority of cases, therefore, employment is likely a prerequisite for migration that enables people to move for whatever cited reason. This explains why studies of aggregate migration flows have found that labour market factors have positive effects, despite the fact that studies of migration motives using survey data tend to suggest that the proportion of employment-related migration may be diminishing.

The findings presented here are expected to be relevant in other developed countries as well, though two aspects warrant attention: migration for educational purposes and the size and shape of Sweden. First, education was found to be a key driver of long-distance migration in Sweden, for younger migrants in particular (in terms of migration volume, this is by far the most important migrant group). The Swedish higher education system has expanded greatly over the last few decades, and such migration is presumably replacing part of what was earlier employment-related migration. However, in some other countries, education-driven migration may not be as prominent as in Sweden. In the U.S.A., for example, where students who live in dorms during the semester are registered as inhabitants elsewhere (presumably as living with their parents), education-related migration is not detectable [START_REF] Schachter | Why People Move: Exploring the March 2000 Current Population Survey[END_REF]. In other countries, such as the U.K., where changes introduced in the latest census now consider students as resident at their school-term address [START_REF] Simpson | Census fieldwork in the UK: the bedrock for a decade of social analysis[END_REF], educationrelated migration is likely to suddenly emerge as a 'new' and important phenomenon.

Second, since all countries vary in size, shape, settlement pattern, transportation infrastructure, etc., it follows that the present findings may not be directly transferable to

  More recently, CLARK and DAVIES-WITHERS (2007) used data covering 1970-1992 from the U.S. Panel Study of Income Dynamics and found that employment was the most

  strength over time -that housing considerations are still central motives for short-distance migration, while long-distance migration seems primarily driven by employment considerations.

  well as a more general discussion of the use of open-ended questions when researching migration motives is provided in[START_REF] Niedomysl | Do open-ended survey questions on migration motives create coder variability problems?[END_REF].

  split into those with a university degree requiring three years or less of study and those with a degree requiring over three years, i.e., with 15 or more years of education.

  -distance migration, however). It should also be noted that the oldest age group provided a much larger share of responses belonging to the 'other reasons' category. This suggests that the coding procedure of the open-ended questions may not have been ideal for this group of migrants.

  living environment, and there is a clear distance decay in the probability of stating this motive.

  implications of a migrant moving 100 km in Sweden likely differ from those of a migrant moving 100 km, for example, in the U.S.A. or the Netherlands. Since this paper has demonstrated that migration motives vary both with migration distance and in relation to migrant socioeconomic and demographic characteristics, migration researchers urgently need to seize the opportunity and use the increasingly sophisticated data now available in many countries to account for this diversity. Acknowledging the heterogeneity of migrants will lead to more insightful analyses and allow for stronger generalizations.

  J. and MUESER P. R. (1988) Implications of Boundary Choice for the Measurement of Residential Mobility, Demography 25, 443-459. VON REICHERT C. (2001) Returning and new Montana migrants: socio-economic and motivational differences, Growth and Change 32, 447-465.

Fig. 2 .Fig. 3 .

 23 Fig.2. Comparison between respondents stating employment as their main migration motive and respondents that changed their workplace when migrating, not mutually exclusive (all migrants aged 18-65 year, weighted data)

Table 1 .

 1 Dependent variables.

		Number	Number	Per cent
	(not weighted) (weighted) (weighted)
	Migration motives
	Education	383	41007	17%
	Employment	982	61410	26%
	Living environment	761	28226	12%
	Housing	1193	38421	16%
	Social reasons	1262	57656	24%
	Other reasons	328	12825	5%
	Total	4909	239545	100%

Table 2 .

 2 Independent variables. Fig. 1. How migration motives vary over distance (all migrants, weighted data)

		Number	Number	Per cent
	(not weighted) (weighted) (weighted)
	Gender
	Male	2177	100016	42%
	Female	2732	139529	58%
	Age (years)
	18 -25	1013	95345	40%
	26 -37	1143	79705	33%
	38 -59	1145	47656	20%
	60 -74	1608	16839	7%
	Civil status
	Married	1674	49690	21%
	Single	2194	165856	69%
	Divorced	816	21003	9%
	Widowed	225	2994	1%
	Education
	Compulsory	1094	23806	10%
	Upper-secondary	1626	97677	41%
	University < 3 years	698	36298	15%
	University >= 3 years 1443	79866	34%
	Income
	Low	1714	111757	47%
	Middle	2009	88285	37%
	High	1186	3905	17%
	Occupation
	Employed	2623	128560	58%
	Unemployed	251	16302	7%
	Student	722	63044	29%
	Retired	921	12379	6%
	Migration distance (km)
	20 -35	1215	56743	24%
	36 -50	915	22576	9%
	51 -100	1618	41646	17%
	101 -150	206	21900	9%
	150 +	955	96679	40%

http://mc.manuscriptcentral.com/cres Email: regional.studies@fm.ru.nlRegional Studies

Acknowledgements -This study was funded by the Swedish Governmental Agency for Innovation Systems (VINNOVA). The author gratefully acknowledges the anonymous referees for their valuable comments.

Acknowledging the Non-Economic Worlds of Migration Decision-Making, Population, Space and Place 10, 239-253. KÜHNL K. (1978) Selected aspects of migration motivation in the Czech Socialist Republic,

O1 -Economic Development < O -Economic Development, Technological Change, and Growth, O18 -Regional, Urban, and Rural Analyses < O1 -Economic Development < O -Economic Development, Technological Change, and Growth, R11 -Regional Economic Activity: Growth, Development, and Changes < R1 -General Regional Economics < R -Urban, Rural, and Regional Economics, R23 -Regional Migration|Regional Labor Markets|Population < R2 -Household Analysis < R -Urban, Rural, and Regional Economics

http://mc.manuscriptcentral.com/cres Email: regional.studies@fm.ru.nl http://mc.manuscriptcentral.com/cres Email: regional.studies@fm.ru.nl

Regional Studies

F o r P e e r R e v i e w O n l y

Regional Studies

F o r P e e r R e v i e w O n l y


Dr Andrea Dardis
email: dardis.andrea@aoud.sanita.fvg.it

Zampieri Stefania

Emanuele Buratti

Silvia Dominissini

Anna Lisa Montalvo

Gabriella Pittis

Bruno Bembi

Stefania Zampieri

Maria Gabriela Pittis

Splicing mutations in Glycogen Storage Disease Type II: evaluation of the full

Keywords: Glycogenosis type II, splicing mutations, GAA gene, phenotype/genotype correlation Running title: Splicing mutations in GSDII R2-1

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Glycogen Storage Disease Type II (GSDII; Pompe disease, acid maltase deficiency, MIM# 232300) is an autosomal recessive inherited disorder due to the deficiency of acid α-glucosidase (GAA; E.C. 3.2.1.20) that results in impaired glycogen degradation, which accumulates within the lysosomes. The GAA gene (MIM# 606800) has been localized to human chromosome 17q25.2-25.3. The enzyme is synthesized as an inactive precursor of 110 kD, which is transported to the pre-lysosomal and lysosomal compartment via the mannose-6-phosphate receptor where it is processed into a 95 kD intermediate and the fully active forms of 76 and 70 kD. [START_REF] Hoefsloot | Characterization of the human lysosomal alpha-glucosidase gene[END_REF][START_REF] Martiniuk | Isolation and partial characterization of the structural gene for human acid alpha glucosidase[END_REF][START_REF] Hirschhorn | Glycogen storage disease type II: acid αglucosidase (acid maltase) deficiency[END_REF][START_REF] Moreland | Lysosomal acid alpha-glucosidase consists of four different peptides processed from a single chain precursor[END_REF] Clinically, GSDII encompasses a continuous spectrum of phenotypes, from a rapidly progressive infantile form to a slowly progressive late-onset form. Classic infantile GSDII manifests soon after birth and is characterized by absent or nearly absent enzyme activity, severe muscle weakness, cardiomyopathy and respiratory insufficiency, that typically lead to death within the first year of life. [START_REF] Hirschhorn | Glycogen storage disease type II: acid αglucosidase (acid maltase) deficiency[END_REF][START_REF] Raben | Acid alpha-glucosidase deficiency (glycogenosis type II, Pompe disease)[END_REF][START_REF] Van Den Hout | The natural course of infantile Pompe's disease: 20 original cases compared with 133 cases from the literature[END_REF][START_REF] Kishnani | Pompe disease in infants and children[END_REF] Late-onset (LO) GSDII comprises all milder subtypes: partial enzyme deficiency manifests in children and adults as slowly progressive skeletal muscle weakness without cardiac involvement. Respiratory muscle weakness, particularly of the diaphragm, is the leading cause of death in the late-onset cases. [START_REF] Hirschhorn | Glycogen storage disease type II: acid αglucosidase (acid maltase) deficiency[END_REF][START_REF] Raben | Acid alpha-glucosidase deficiency (glycogenosis type II, Pompe disease)[END_REF][START_REF] Kishnani | Pompe disease in infants and children[END_REF][START_REF] Hagemans | Late-onset Pompe disease primarily affects quality of life in physical health domains[END_REF][START_REF] Hagemans | Clinical manifestation and natural course of late-onset Pompe's disease in 54 Dutch patients[END_REF] A large number of sequence variations in the GAA gene have been described to date [http://www-fgg.eur.nl/ch1/pompe/en/?Molecular_aspects:Mutations]. Among them, approximately 15% are variations that may affect pre-messenger RNA (pre-mRNA) splicing process.

Pre-mRNA defects seem to have a role in almost all known genetic disorders. [START_REF] Tazi | Alternative splicing and disease[END_REF][START_REF] Wang | Splicing in disease: disruption of the splicing code and the decoding machinery[END_REF] However, unless the mutation affects the highly conserved nucleotides at R2-3 the exon 3'ss and 5'ss boundaries, it has often been very difficult to show a clear correlation between a suspected mutation and the disease. Recently, several methods have been developed to evaluate the clinical effect of mutations that may cause splicing defects. [START_REF] Baralle | Missed threads. The impact of pre-mRNA splicing defects on clinical practice[END_REF] As is intuitively obvious, direct analysis of the mature mRNA from the patient remains the most reliable method to determine whether or not a genetic variation affects splicing. However, cells/RNA from the patient might not be available or the transcript may be expressed only in highly selected tissues, making this approach not always possible. To overcome this problem, alternative systems such us minigene-based assays have been used. [START_REF] Vibe-Pedersen | Expression of a human alphaglobin/fibronectin gene hybrid generates two mRNAs by alternative splicing[END_REF] All these approaches, however, require a substantial amount of time and skill if they have to be applied to a large number of putative splicing mutations.

For this reason, several in silico approaches to assess the effects of sequence variants on splicing have been developed. In general, these splice-prediction programs (SPPs) evaluate the effect of putative splicing mutations on the strength of 5' splice site and 3' splice site sequences, or search for potential changes within the vast array of splicing regulatory element (SREs) known to this date. Although the predictions obtained are usually not enough to establish with sufficiently high accuracy the clinical impact of genetic variations on splicing, it has been proposed that SPPs could be used to perform a first selection of those variants that may have an effect on the pre mRNA splicing before starting with time-consuming and labour-intensive mRNA analysis. [START_REF] Vreeswijk | Intronic variants in BRCA1 and BRCA2 that affect RNA splicing can be reliably selected by splice-site prediction programs[END_REF] In this study, we have comprehensively reviewed the available information on splicing mutations of the GAA gene and we have evaluated the possible impact of these genetic variations on pre-mRNA splicing process using different in silico approaches. In addition, using a minigene system assay we have performed the functional characterization of three sequence variants previously found in Italian patients affected with late onset GSDII.

Materials and methods

Mutation Nomenclature

All mutations are described according to mutation nomenclature, considering nucleotide +1 the A of the first ATG translation initiation codon (http://www.hgvs.org/mutnomen). [START_REF] Den Dunnen | Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion[END_REF][START_REF] Den Dunnen | Standardizing mutation nomenclature: why bother?[END_REF] Nucleotide numbers are derived from cDNA GAA sequence (RefSeq cDNA Y00839.1).

Splice-Site prediction of intronic variants previously described in the GAA gene

The sequence environment of all acceptor and donor sites was analyzed using

Splice

Site Prediction by Neural Network, NNSPLICE http://www.fruitfly.org/seq_tools/splice.html/. [START_REF] Reese | Improved splice site detection in Genie[END_REF] Maximum entropy scores were obtained using the software based on the maximum entropy principle, MaxEntscan (http://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html/. [START_REF] Yeo | Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals[END_REF] The H-bond scores were calculated at http://www.uni-duesseldorf.de/rna/html/hbond_score.php. [START_REF] Freund | A novel approach to describe a U1 snRNA binding site[END_REF] Finally, the Sroogle scores were obtained using the software available at http://sroogle.tau.ac.il/. [START_REF] Schwartz | SROOGLE: webserver for integrative, user-friendly visualization of splicing signals[END_REF] In addition, the potential effect of the nucleotide variants on splicing regulatory elements was also analyzed with ESEfinder (http://rulai.cshl.edu/cgibin/tools/ESE3/esefinder.cgi), [START_REF] Smith | An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers[END_REF][START_REF] Cartegni | ESEfinder: A web resource to identify exonic splicing enhancers[END_REF] RESCUE-ESE (http://genes.mit.edu/burgelab/rescue-ese/), [START_REF] Fairbrother | Predictive identification of exonic splicing enhancers in human genes[END_REF] and PESX (http://cubweb.biology.columbia.edu/pesx/). [START_REF] Zhang | Computational definition of sequence motifs governing constitutive exon splicing[END_REF][START_REF] Zhang | Exon inclusion is dependent on predictable exonic splicing enhancers[END_REF]

Minigene constructs

To evaluate the expression of exons

Minigene splicing assay

COS-1, CHO, Hep3b and HeLa cells were transfected with the wild type and mutant minigene constructs. Total RNA was extracted after 48 h using TRIzol ® Reagent and analyzed by RT-PCR. Reverse transcription was performed using the oligo (dT) primer; the PCR reaction was carried out with a forward vector specific primer (5'AGGGAGACCCAAGCTTGATG3') and the reverse primers 8R, 12R or 19R (table1). PCR products were resolved in a 1% agarose gel and sequenced.

Results

The complete spectrum of GAA mutations that may affect pre-mRNA splicing is shown in Figure 1. So far, 39 different sequence variations that may alter the splicing process have been described (http://wwwfgg.eur.nl/ch1/pompe/en/?Molecular_aspects:Mutations). All of them were analyzed in silico using 3 Splice Site Prediction (SSP) programs to evaluate the potential effect on 5'ss and 3'ss strengths (MaxEntScan, NNSplice, and HBond). These programs were chosen on the basis that they are very common in the scientific literature that focuses on splicing mutations and use different approaches to measure splice site strengths. For example, the MaxEntScan server scores 5'splice site sequences taking into account known dependencies between adjacent and non-adjacent positions of the splice site consensus, the NNSplice program is a machine-learning approach that recognizes sequence patterns once it was trained with DNA sequences encompassing authentic splice sites, and HBond analyses individual hydrogen-bonding patterns to the U1 snRNA 5' end irrespective of nucleotide frequencies.

In addition, all mutations were analyzed with respect to the eventual presence of SR-binding sites or general enhancer/silencer splicing regulatory elements using the ESEfinder, RescueESE, and PESX programs. Also in this case, these In general, for all these programs the user is allowed to make his/her own decision with regards to deciding whether a prediction is positive or negative. In our analyses, we have therefore decided to look predominantly to score variations rather than their basic numerical value. In general, we have considered that a mutant score should have at least a 10% difference from the wild-type one to be classified as a deleterious change. For all these programs, of course, the key question is represented by the degree of reliance that one can place in their predictions. In general, because the donor and acceptor elements tend to be reasonably conserved in human genes the programs that evaluate these elements seem to be rather more successful than those which aim to target the much more loosely conserved SRE elements. However, all bioinformatics approaches tend to suffer from individual drawbacks that have been summarized recently by Spurdle et al. [START_REF] Spurdle | IARC Unclassified Genetic Variants Working Group Prediction and assessment of splicing alterations: implications for clinical testing[END_REF] In addition, for obvious reasons none of these programs can easily take into account all the factors which influence splice site selection and include transcriptional effects, influence of genomic context, relative abundance of splicing factors, and of course the presence of still unidentified regulatory elements. [START_REF] Buratti | Defective splicing, disease and therapy: searching for master checkpoints in exon definition[END_REF] Therefore, it is common consensus that in order to evaluate a potential splicing mutation it is advisable to use as many programs as possible to perform the analysis. [START_REF] Houdayer | Evaluation of in silico splice tools for decision-making in molecular diagnosis[END_REF] For this reason, in addition to all these programs we have also decided to

R1-4 R1-1 R1-8
test these mutations using a more recent in silico prediction program (Sroogle) [START_REF] Schwartz | SROOGLE: webserver for integrative, user-friendly visualization of splicing signals[END_REF] that employs an integrated approach to answer the question whether a nucleotide variation has a good chance of representing a splicing mutation as opposed to a benign polymorphism. The results of this analysis are reported in Table 2.

Out of the thirty nine mutations taken into consideration, thirty eight are clearly predicted to affect the splicing process by at least one of the programs used (but very often more), while only the c.-32-13T>G scores as negative with all programs except for MES and Sroogle where it barely clears the 10% cut off score.

Among the programs tested, the most successful ones are represented by those which aim to predict the eventual disruption of the natural 5' and 3' splice sites. In fact, thirty seven mutations are clearly predicted to affect the 3' or 5' splice site signal strengths. More interestingly, it is worthy to point out that with possibly just one exception (c.-32-3C>A) where MaxEntScan performs apparently better, in all other cases the integrated Sroogle program performs as well as any of the individual programs. In some cases, such as c.1636+5G>C and c.1636+5G>T, it even performs better than the MaxEntScan and NNsplice programs.

We also looked at whether the programs were able to predict the degree of changes correctly. However, no correlation between the degree of score change and the consequence on the mRNA splicing observed in vivo was found.

Next, in order to analyze the possible correlation between the effect of GAA mutations on the splicing process and the clinical phenotype we reviewed the clinical presentation of patients carrying these mutations. As shown in table 3, fifteen splicing mutations were reported in infantile patients and the impact on pre-mRNA processing has been reported for 11 of them.

R1-5

R2-3

Interestingly, ten of these 11 mutations associated with infantile onset caused a severe defect on mRNA splicing leading to either exon skipping, shift in the reading frame, or the lack of the corresponding transcript in vivo. 26,30,31,[START_REF] Raben | Novel mutations in African American patients with glycogen storage disease Type II. Mutations in brief no. 209. Online[END_REF][START_REF] Stroppiano | Aberrant splicing at catalytic site as cause of infantile onset glycogen storage disease type II (GSDII): molecular identification of a novel IVS9 (+2GT-->GC) in combination with rare IVS10 (+1GT-->CT)[END_REF][35][START_REF] Palmer | Pompe disease (glycogen storage disease type II) in Argentineans: clinical manifestations and identification of 9 novel mutations[END_REF][START_REF] Mccready | Development of a clinical assay for detection of GAA mutations and characterization of the GAA mutation spectrum in a Canadian cohort of individuals with glycogen storage disease, type II[END_REF][START_REF] Hermans | Mutation detection in glycogen storage-disease type II by RT-PCR and automated sequencing[END_REF][START_REF] Fernandez-Hojas | Identification of six novel mutations in the acid alpha-glucosidase gene in three Spanish patients with infantile onset glycogen storage disease type II (Pompe disease)[END_REF] In the light of these findings, therefore, it is not unexpected that patients carrying these mutations presented a severe form of GSDII. In fact, only in cells from a patient carrying the c.1437G>A mutation a very low amount of normal mRNA was detected (1.2%) 31,32 , suggesting that the amount of enzyme translated from this mRNA is probably not enough to prevent the development of a severe form of the disease.

In contrast to this picture, in patients affected with LO GSDII eight mutations were reported in a homozygous state or in association with severe mutations. In this case, the effect on pre-mRNA processing has been analyzed in vivo for seven of them. It is interesting to note that in the cells of patients presenting six of these mutations variable amounts of normal spliced GAA mRNA have been detected (from 3.6 to 13.7

% of GAA mRNA expressed in normal cells). 26,[START_REF] Maimaiti | Silent exonic mutation in the acidalpha-glycosidase gene that causes glycogen storage disease type II by affecting mRNA splicing[END_REF]31,35,40 As a result, these data may explain the less severe phenotype reported in these patients.

Five of these LO-GSDII associated splicing mutations were found in patients with the common c.-32-13T>G mutation. 27,29,30,32 It is worth mentioning that the mutation c.-32-13T>G, the most common one among the Caucasian late onset GSDII patients [START_REF] Hirschhorn | Glycogen storage disease type II: acid αglucosidase (acid maltase) deficiency[END_REF] has been previously studied in vitro using a minigene system assay. [START_REF] Raben | A model of mRNA splicing in adult lysosomal storage disease (glycogenosis type II)[END_REF] The results have shown that this mutation does not completely prevent normal splicing since low levels of the correctly splice mRNA were generated with the mutant construct. In fact, three splice variants (SV1, SV2 and SV3-table2) were observed with both the wild type and the mutant constructs, indicating that these forms represent normal alternative spliced products. Thus, the major effect of the mutation seems to affect mostly the R1-9

overall splicing efficiency of the pre-mRNA transcript rather than in a qualitative way.

Since the presence of the c. In order to complete this analysis of GAA splicing mutations it should also be noted that in a previous study we have characterized the mutation profile of the GAA gene in 40 Italian patients with LO GSDII. 30 Overall, five mutations that might have affected the splicing process were found. However, since RNA and/or cells from patients carrying 3 of them (c.1626C>G, c.1194+2T>A and c.2646_2646+1delTG) were unavailable, their deleterious effect could not be confirmed. The sequence variation c.1626C>G was found in the homozygous state and did not disrupt the reading frame and codon usage, while mutations c.1194+2T>A and c.2646_2646+1delTG

affected the consensus 5' splice donor sites of exons 7 and 18, respectively. Several SPP (Table 2) clearly predicted that mutation c.1626C>G would create a novel donor site, which would have caused the exclusion of 11 bp of exon 11, whereas the mutated sequences c.1194+2T>A and c.2646_2646+1delTG would not longer be recognized as donor sites. Therefore, to test the predicted effects of these mutations on GAA pre-mRNA splicing we have now performed a functional splicing assay.

As shown in Figure 2, cells transfected with the mutant constructs bearing these mutations produced aberrant transcripts in all cases. However, it is worth noting that in cells transfected with the GAA1626m construct, a low amount of a transcript similar in size to the normal one was also present. Since the wild type acceptor and donor sites of intron 11 seem to not be abolished, this data suggests that even in the presence of this mutation a low amount of wild type transcript would still be produced.

Sequencing analysis of the aberrant PCR product showed that as predicted by the SPPs the c.1194+2T>A and c.2646_2646+1delTG mutations led to the skipping of exon 7 and exon 18, respectively (Figure 3a and3c). In the light of these results, both mutations should be considered to have a severe impact on pre-mRNA splicing.

However, in both patients these mutations were found in association with the common c.-32-13T>G mutation (Table 3) which, as mentioned above, would probably have been enough to determine the observed LO phenotype.

Finally, the analysis of the RT-PCR products found in cells transfected with the GAA1626m construct showed that, unexpectedly, the shorter transcript was not only lacking 11 nt of exon 11 as predicted by the SPPs, but also a portion of 46 nucleotides of exon 12 was missing (figure 3b). In any case, the presence of low amounts of the wild type transcript was confirmed (data not shown). If we consider the c.1626C>G

mutation was found at the homozygous state, this finding may also explain the LO form of the disease observed this patient (Table2).

R1-10

Discussion

The complexity of the splicing process has revealed that almost all types of nucleotide changes may potentially affect the pre-mRNA splicing process. However, distinguishing between benign and disease causing sequence substitutions may be challenging. In fact, it is not realistic to test all nucleotide modifications at RNA level.

Therefore, several in silico approaches to assess the effects of sequence variants on splicing have been developed and it has been proposed that they could be used to perform a first selection of those variants that may have an effect on the pre mRNA splicing. [START_REF] Houdayer | Evaluation of in silico splice tools for decision-making in molecular diagnosis[END_REF][START_REF] Baralle | Splicing in action: assessing disease causing sequence changes[END_REF] In this work we have reviewed the genetic variations reported in the literature that might affect the GAA pre-mRNA splicing process. The in silico analysis of 39 sequence variants showed that this approach may be useful to select those mutations that affect the splicing process. As previously mentioned, among these programs the most successful ones are represented by those which aim to predict the eventual disruption of the natural 5' and 3' splice sites. This observation is consistent with previous analyses which show that even a moderate consensus, such as the one present in the 5' and 3'ss splice sites of higher eukaryotes, greatly helps this kind of computational studies. It is common knowledge, in fact, that the much more loosely conserved enhancer or silencer elements are much more difficult to be defined, and in this respect it is worthy to point out the comment by Rogan PK [START_REF] Rogan | Deeper understanding of unclassified intronic variants and ESEs[END_REF] which highlights how only one in four programs which aim to predict ESE motifs was actually capable of correctly recognizing the creation of a novel ESE element in a pathological pseudoexon activation event. [START_REF] Homolova | The deep intronic c.903+469T>C mutation in the MTRR gene creates an SF2/ASF binding exonic splicing enhancer, which leads to pseudoexon activation and causes the cblE type of homocystinuria[END_REF] Considering that splicing signals are highly context specific it is not surprising that they are hard to predict and that programs that uses an integrated approach perform better. In keeping with this hypothesis, the analysis performed here indeed has shown that the integrated Sroogle program performed at least as well as any of the individual programs. This conclusion is supported by the observation that another integrated

R2-7 R1-2 R2-1 R1-8
program (Automated Splice Site Analysis, ASSA, available at https://splice.uwo.ca) [START_REF] Nalla | Automated splicing mutation analysis by information theory[END_REF] used to analyze splicing mutations in the RB1 gene provided several advantages with respect to individual programs. [START_REF] Houdayer | Evaluation of in silico splice tools for decision-making in molecular diagnosis[END_REF] Therefore, it is possible to conclude that integrated approaches such as the one represented by Sroogle may successfully substitute for the use of separate analyses programs shorten the time to complete a preliminary screening of a large number of mutations.

There are also some cases, however, where in silico analysis did not perform so well. Not surprisingly, these cases include mutations that occur more distant to the splice site (ie. c.-32-13T>G, and c.1195-8G>T). However, in the case of c.1195-8G>T mutation, the use of the ESEfinder and PESX programs might have substituted for the relative inability of the other softwares to detect the splicing-modifying potential of this mutations. In the case of the c.-32-13T>G substitution even using a very favourable degree of score change (10%), it was barely predicted to affect the splicing process.

There are several reasons which may account for this failure but the fact that this mutation still allows a certain amount of normal splicing (as discussed above) most probably places it in the limit of the detection threshold of these bioinformatics programs. Taken together, therefore, approximately 97% of the GAA splicing mutations taken in consideration in this analysis could have been clearly predicted to have an impact on the splicing process by in silico analysis alone.

The remarkable heterogeneity of mutations found in GSDII patients makes it difficult to correlate the genotype with the phenotype. However, the results reviewed here show a good correlation between the impact of the mutation on the splicing process and the clinical phenotype (i.e. the more severe the impact on the splicing process the more likely the presence of an infantile form of the disease, and vice versa).

R1-5

R1-8

Finally, using a minigene assay we confirmed the pathogenic effect of three sequence variants found in Italian patients affected with GSDII. All these three mutations were predicted to affect the splicing process by SPPs. However, in the case of mutation c.1626C>G, while several SPP predicted that the mutation would create a novel donor site, which would caused the exclusion of 11 bp of exon 11, the functional analysis showed that this mutation not only creates a new donor site as predicted but also causes also the activation of a cryptic splice site localized in exon 12, downstream of the normal acceptor site of exon 11.

In conclusion, this study has shown that in silico analysis represents a useful tool to select mutations that affect the splicing process of the acid α-glucosidase. This type of analysis is quite straight forward and reliable. However, it is worth to highlight the importance of functional studies for the correct evaluation of sequences variations. In fact, a comprehensive analysis of the mechanism by which a sequence variant affect the mRNA splicing is crucial to analyze possible correlations between the mutation and the clinical phenotype and to evaluate the feasibility of using emerging splicing-based therapeutic approaches. RT-PCR analysis of the GAA mRNA in cells transfected with wt and minigenes containing mutations c.1194+2T>A, c.1626C>G and c.2646_2646+1delTG (panels A2, B2 and C2, respectively) using a forward vector specific primer which amplified only the minigene product.

3 R2- 2 R1- 1 eventual

 321 programs use different approaches to identify and score putative splicing regulatory sequences. The ESEfinder program is based on functional SELEX binding analyses to identify, using a matrix-scoring approach, putative SR protein binding sites and their R2-disruption following the introduction of nucletide changes. On the other hand, both RescueESE and PESX servers have used statistical analyses to identify putative regulatory motifs (hexamers or octamers) whose frequency differs between exons/introns and between exons with weak or strong splice sites (RescueESE) or within internal non-coding exons vs. adjacent pseudoexons or the 5'UTR of intronless genes (PESX).

- 32 -

 32 13T>G mutation in one allele is enough to determine the LO phenotype, independently of the mutation present in the second allele, it was not possible to establish a correlation between mutations present in association with the c.-32-13T>G allele and the clinical presentation. The only exception is represented by mutation c.1076-1G>C, which had been analyzed in vivo. In this case, RT-PCR analysis performed in cultured fibroblasts from a patient carrying the c.1076-1G>C mutation showed the inclusion of intron 6 (79 bp) and intron 7 (89 bp) into the transcribed mRNA. If we consider the fact that the c.1076-1G>C mutation has also been found in homozygosis in infantile GSDII patients,27 these data indicate that c.1076-1G>C could be classified as a severe mutation.

Figure 1 :Figure 2 :

 12 Figure 1: Schematic representation of GAA gene.

Figure 3 :

 3 Figure 3: Sequencing analysis of the RT-PCR products obtained from amplification of

  7, 11 and 18 of the GAA gene, wild type

	minigenes: GAA1194wt, GAA1626wt and GAA2646wt were obtained by insertion in
	the pcDNA3 plasmid of PCR fragments containing the genomic GAA sequence from
	exon 6 to 8, 10 to 12 or 17 to 19, respectively. PCR amplification was performed using
	primers 6F-8R, 10F-12R and 17F-19R (listed in Table 1). The forward and reverse
	primers carried a Hind III and EcoR1 restriction site, respectively. Mutated minigenes:
	GAA1194m, GAA1626m, and GAA2646m, carrying mutations c.1194+2T>A,
	c.1626C>G and c.2646_2646+1delTG, respectively, were prepared by site directed
	mutagenesis (SDM) using the Quickchange Site-Directed Mutagenesis Kit (Stratagene,
	Cedar Creek, TX, USA) according to the manufacturer's instructions. Primers used for
	the SDM are listed in Table 1. Each clone was entirely sequenced to confirm that no
	other mutations were introduced.
	Cell culture and transient transfection
	COS-1, CHO and Hep3B cells were grown on monolayers in Dulbecco's
	modified Eagle's medium (DMEM) supplemented with 10% foetal calf serum, 2mM L-
	glutamine and 50 mg/ml penicillin/streptomycin (Gibco). HeLa cells were cultured in
	RPMI 1640 supplemented with 10% foetal calf serum, 2mM L-glutamine and 50 mg/ml
	penicillin/streptomycin (Gibco). Cells were transfected with Lipofectamine™ 2000
	(Invitrogen, Carlsbad, CA, USA) using 4 μg of total plasmid DNA Endofree purified
	(Sigma, St. Louis, MO, USA) following the manufacturer's instructions.

TABLE 1 Primers

 1 Mut ss: mutant splice site; WT ss: wild type splice site; MES: MaxEntScan (Maximum entropy scan); NN splice: Neural network splice site prediction; Hbond: Hydrogen bond; ESE Finder: Exonic splicing enhancer finder; Rescue-ESE: Relative Enhancer and Silencer Classification by Unanimous Enrichment; PESX: putative Exonic splicing enhancer/silencer; Pese: putative Exonic splicing enhancer; Sroogle: Splicing regulation online graphical engine; PSSM: position-specific scoring matrix; DG: delta free energy; Senep: Senepathy; Ex: exon; In: intron; NR: not recognize because below detection threshold; NA: non applicable; NC: normal control; r.0: mRNA transcribed from the mutant allele non detected in patient's fibroblasts SV: splice variant. * For polypyrimidine tract and branch site recognition the algorithms developed by Kol et al.[44] and Schwartz et al.[45] were used. Threshold for NNsplice was set at 0.1. For the other programs default thresholds were used. Mutations barely predicted to affect the splicing porcess are underline.

	used for PCR amplification

Table 3 : Correlation between the effect of GAA mutations on the splicing process and the clinical phenotype.

 3

	Mutation	Clinical Phenotype	Effect on RNA level (when	Reference
			available)
	c.-32-3C>A	I	Exon skipping.	[37]
	c.692+1G>C	I	r.0	[30]
	c.1194+2T>C	I	Intron retention.	[31]
	c.1195-2A>G	I		[42]
	c.1326+1G>A	I	r.0	[33]
	c.1327-2A>G	I		[43]
	c.1437+2T>C	I	Exon skipping	[34]
	c.1437G>A	I	Normal spliced mRNA	[31, 32]
			1.2% of NC
	c.1551+1G>C	I	Exon skipping	[34,35,
				36]
	c.1636+5G>C	I	Intron retention.	[37]
	c.1755-1G>A	I		[36]
	c.2040G>A	I	Intron retention.	[38]
	c.2041-2A>C	I	Skipping 6 nt at 5' exon 15. [26]
	c.2331+2T>C	I	Skipping 16 nt exon16.	[39]
	c.2646+2T>A	I		[41]
	c.-32-13T>G	LO	Normal spliced mRNA
			reduced with respect to NC

Acknowledgments

This work was supported by a grant from Italian Health Ministry (Italia-USA RF526 D/47) and by a European community grant (EURASNET-LSHG-CT-2005-518238)

Conflict of Interest Statement

The authors declare no conflict of interest.