A Markov blanket-based method for detecting causal SNPs in GWAS
2009

Bing Han
Meeyoung Park
Xue-Wen Chen
A Markov blanket-based method for detecting causal SNPs in GWAS

From IEEE International Conference on Bioinformatics and Biomedicine
Washington, DC, USA2009P R O C E E D I N G S Open Access
Background: Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. Results: We propose a new Markov Blanket-based method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control GWAS. Markov blanket of a target variable T can completely shield T from all other variables. Thus, we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB uses a heuristic search strategy by calculating the association between variables to avoid the time-consuming training process as in other machine-learning methods. We apply our algorithm to simulated datasets and a real case-control dataset. We compare DASSO-MB to other commonly-used methods and show that our method significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases. Conclusions: Our study shows that DASSO-MB can identify a minimal set of causal SNPs associated with diseases, which contains less false positives compared to other existing methods. Given the huge size of genomic dataset produced by GWAS, this is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.

Background

Compared to Mendelian disorders that are monogenic and rare in population, some common complex diseases like various types of cancers, diabetes and hypertension are conjectured to be caused by two types of interactions related with multiple genetic factors: gene-gene interactions and gene-environment interactions [1].

Interactions between genes or single nucleotide polymorphisms (SNPs) in chromosomal regions are called epistasis [2][3][4]. Detecting epistasis associated with complex and common diseases became an important issue in human genetics and can build a new pavement towards the improvement of prevention, diagnosis and treatment of these diseases.

Recent development of high-throughput technologies has made it possible to produce a huge amount of genotype data and contribute to the analysis of genome-wide association studies (GWAS) [5][6][7]. Furthermore, the international HapMap project has been used to support GWAS actively by the analysis of the common patterns of DNA sequence variations in different populations [8,9]. However, the number of SNPs from case-control GWAS is typical more than 10 million and using traditional epistatic interactions detection methods such as parametric regression to identify multiple loci causing diseases simultaneously among all possible combinations of SNPs is inappropriate from genome-wide case-control data. Therefore, designing robust and manageable methods to address this mathematical and computational problem presents a great challenge to scientists in bioinformatics.

By far, a number of statistical methods have been proposed to detect epistatic interactions. Among these, the most commonly-used parametric statistical method is logistic regression [10]. Logistic regression models the probability of a disease as a linear function of independent SNPs (SNPs are expressed as ternary variables) and finds an optimal logical SNP set associated with the disease status by simulated annealing algorithm [11]. When used for modelling high-order interactions, logistic regression methods relates to many empty contingencytable cells, which often leads to very large standard errors for parameter estimation and therefore increases the type I errors. Meanwhile, if the number of samples is small, high-order interaction models creates a large number of parameters and often results in an overfitting problem. To overcome these problems in logistic regression, Richie et al proposed and developed a multifactor dimensionality reduction (MDR) method [12][13][14][15]. MDR first constructs a contingency table for every possible set of SNPs and then labels the cells of the table "high risk" or "low risk" based on the cases/control ratio of each cell. By the label of each cell in the contingency table, MDR runs 10-fold cross-validation to select an SNP set with the smallest prediction error and/or the largest consistency. The merit of MDR compared to other statistical methods is that MDR is non-parametric and model-free. However, it has two fundamental limitations: MDR selects the k-way interactions purely by the prediction performance and moreover it employs an exhaustive searching strategy to avoid local optima, which makes it impractical for large-scale datasets. Therefore, when applied to large-scale datasets, MDR requires to use some feature selection methods such as ReliefF [16] as a filter for the top N SNPs, which will affect the performance of MDR significantly. Park and Hastie [17] made efforts to detect gene-gene interactions using a forward stepwise method based on penalized logistic regression (stepPLR). However, regression methods are typically computationally expensive because of the time needed for parameter estimations. Although stepPLR adopted forward selection and penalization to choose the causal SNPs, it can not overcome the essential limitations of regression. Recently, Zhang and Liu proposed a Bayesian epistasis association mapping (BEAM) method [18]. BEAM is a Bayesian marker partition model using Markov Chain Monte Carlo to reach an optimal marker partition with the highest posterior probability and a new B statistic instead of the conventional x 2 statistic to check each marker or set of markers for significant associations with the disease. Despite their success to some degrees, statistical methods can only be applied to small-scale analysis due to their computational complexity.

The alternative approaches for statistical methods are machine-learning methods since detecting epistatic interactions is highly related to feature selection problem. Chen et al. proposed a support vector machine approach for detecting gene-gene interactions based on RFE (recursive feature elimination), RFA (recursive feature addition) and GA (genetic algorithm) feature selection methods [19]. Jiang et al. adopted random forests, which is an ensemble learning technique, to the detection of epistatic interactions in case-control studies [20]. They first ranked SNPs based on gini importance of each SNP from random forests and then performed a greedy search for a small subset of SNPs that could minimize the classification error by a Sliding Window Sequential Forward feature Selection (SWSFS) algorithm. The common limitation of machine learningbased methods is that they typically identify a SNP set that produces the highest classification accuracy, but not necessarily has the strongest association with the diseases. As a result, machine learning-based approaches tend to introduce many false positives, since the including of more SNPs increases classification accuracies.

In this paper, we propose a new Markov Blanketbased method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control studies. The Markov Blanket is a minimal set of variables, which can completely shield the target variable from all other variables based on Markov condition property. Thus we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB performs a heuristic search by calculating the association between variables to avoid the time-consuming training process as in SVMs and Random Forests.

We compare DASSO-MB with four other commonly used methods (BEAM [18], SVM [19], MDR [12][13][14][15] and stepPLR [17]) on simulated datasets generated from three disease models [10,18,20]. The results show that DASSO-MB significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases. For genome-wide case-control datasets, we use the Age-related Macular Degeneration (AMD) dataset containing 116,204 SNPs genotyped for 96 cases and 50 controls [7]. DASSO-MB can find the AMD associated SNP rs380390 in the result SNP set and this demonstrates the power and scalability of DASSO-MB.

Results and discussion

Epistatic models and simulation study

We first evaluate the proposed DASSO-MB on simulated data sets, which are generated from three commonly-used disease models developed elsewhere [10,18]. We show the three disease models in Table 1. In each cell of the table are the disease odds for each genotype combination at two loci (A and B), where a is the baseline effect and θ is the genotypic effect. In model 1, two disease loci contribute to the disease risk independently and produce additive effects. In model 2, the disease risk is presented only when both loci have at least one disease allele. Model 3 is a threshold model and is similar to model 2 except that additional disease alleles at each locus do not further increase the disease risk.

To generate data, we need to determine three parameters associated with each model: the marginal effect of each disease locus (l), the minor allele frequencies (MAF) of both disease loci, and the strength of linkage disequilibrium (LD) between the unobserved disease locus and a genotyped locus. LD is a non-random association of alleles at different loci and is quantified by the squared correlation coefficient r 2 calculated from allele frequencies [21]. The prevalence of a disease is the proportion the total number of cases of the disease in the population and in this paper we assume that the disease prevalence is 0.1 for all these three disease models [10]. The marginal effect of each disease locus (l) can be determined by the baseline effect a and the genotypic effect θ in Table 1 and the minor allele frequencies (MAF) of both disease loci. So first we fix l, the disease prevalence and MAF of both disease loci. Then we numerically derive the model parameters θ and a. Based on θ and a, we calculate the conditional probability of each genotype combination given disease status which is necessary for generating data [22]. In this paper, we set parameters for each model as follows:

• For each non-disease marker, we randomly chose its MAF from a uniform distribution in [0.0. 0.5]. We generate 50 datasets and each dataset contains 100 markers genotyped for 1,000 cases and 1,000 controls based on each parameter setting for each model.

We compare the DASSO-MB algorithm with four commonly used methods: BEAM, Support Vector Machine, MDR and stepPLR on the three simulated disease models. We use power as our evaluation criterion, which is defined as the proportion of simulated datasets in which only two diseases associated markers are identified without any false positives, to measure the performance of each method.

BEAM uses a Bayesian marker partition model to partition SNPs into three groups: group 0 contains markers unlinked to the disease, group 1 contains markers contributing independently to the disease, and group 2 contains markers that jointly influence the disease. After the partition step by MCMC, candidate SNPs or groups of SNPs are further filtered by the B statistic [18]. The BEAM software is downloaded from http://www.fas. harv-ard.edu/~junliu/BEAM.

For support vector machines, we use LIBSVM with a RBF kernel to detect gene-gene interactions [23]. A grid search is used for selecting optimal parameters. Instead of using the exhaustive greedy search strategy for SNPs as in [19], which is very time-consuming and infeasible to large-scale datasets, we turn to a search strategy used in [20]. First we rank SNPs based on the mutual information between SNPs and disease status label that is 0 for the control and 1 for the case. Then, we use a sliding window sequential forward feature selection (SWSFS) algorithm in [20] based on SNPs rank. The window size in SWSFS algorithm determines how robust the algorithm could be and we set it to 20.

Since MDR algorithm can not be applied to a large dataset directly, we first select top 10 SNPs by ReliefF [16], a commonly-used feature selection algorithm, and then MDR performs an exhaustive search for a model consisting of no more than four SNPs that can maximize cross-validation consistency and prediction accuracy. When one model has the maximal cross-validation consistency and another model has the maximal prediction accuracy, MDR follows statistical parsimony (selects the model with fewer SNPs).

For stepPLR, we download the R package from CRAN (ftp://220.127.116.11/CRAN/ web/packages/stepPlr).
BB a a (1+ θ) a (1+ θ) 2 Bb a (1+ θ) a (1+ θ) 2 a (1+ θ) 3 bb a (1+ θ) 2 a (1+ θ) 3 a (1+ θ) 4 Model 2 AA Aa aa BB a a a Bb a a (1+ θ) a (1+ θ) 2 bb a a (1+ θ) 2 a (1+ θ) 4 Model 3 AA Aa aa BB a a a Bb a a (1+ θ) a (1+ θ) bb a a (1+ θ) a (1+ θ)
StepPLR provides both stepwise forward and backward methods for feature selection procedure. We use both methods and set the regularization parameter l to default value (10 -4 ) for the L2 norm of the coefficients. The results on the simulated data are shown in Figure  1. As can be seen, among the five methods, the DASSO-MB algorithm performs the best. BEAM is the second best. Interestingly, BEAM prefers to assign the two disease-associated markers to group 1, which means that BEAM considers that the two disease SNPs affect the disease independently. In most cases, the powers of both MDR and SVM are much smaller than those of the DASSO-MB and BEAM algorithms. For the MDR algorithm, the poor performance may be due to the use of ReliefF to reduce SNPs from a very large dimensionality.

In some other studies, the definition of power is not in a strict sense. For example, in [18,20] the power is defined as the proportion of 50 data sets in which all associated markers are identified at a significance threshold of 0.1 after Bonferroni correction. In other words, false positives are allowed in the final SNP sets. Accordingly, we also evaluate the methods in terms of the power defined as the proportion of simulated datasets in which two diseases associated markers are identified with no more than two false positives. The results of those three models are shown in Table 2. In parentheses we list the average number of false positives. From Table 2, we can see that the DASSO-MB again outperforms other algorithms. Furthermore, the DASSO-MB algorithm finds SNP sets with fewer false positives. Compared to the strict definition of power, a difference we can see is that for MAF > 10%, SVM actually detects the two disease associated markers in more datasets than BEAM, however, at the cost of introducing more false positives.

Application to real data

From the results on simulated data with 100 SNPs, DASSO-MB demonstrates a better performance than four other methods. Notice that a real genome-wide case-control association study may require genotyping of 30,000-1,000,000 common SNPs. In this section, we show that DASSO-MB algorithm can also handle largescale datasets in real genome-wide case-control studies. We consider an Age-related Macular Degeneration (AMD) dataset, which contains 116,204 SNPs genotyped with 96 cases and 50 controls [7]. AMD (OMIM 603075) [24] is a common genetic disease related to the progressive visual dysfunction in age over 70 in the developed country. A GWA study was successfully conducted on this disease finding two associated SNPs, rs380390 and rs1329428 ('rs': assigned reference SNP ID by dbSNP [25]) in non-coding region of the gene for complement factor H (CFH), which is located on chromosome 1 in a region linked to AMD [7]. In the phase of preprocessing data, we remove nonpolymorphic SNPs and those that significantly deviated from Hardy-Weinberg Equilibrium (HWE). We also remove all SNPs that have more than five missing genotypes. After filtering, there are 91,495 SNPs lying in 22 autosomal chromosomes remained.

DASSO-MB detects two associated SNPs. The first one is SNP rs380390, which is already found in [7] with a significant association with AMD. The other SNP detected by the DASSO-MB algorithm is SNP rs1374431, which is also located in a non-coding region between LOC644301 and KIAA1715 in chromosome 2q31 [26]. KIAA1715, alternatively called LNP (Lunapark), is reported at OMIM (OMIM 610236) and usually found in adult brain regions. Although no evidences were reported with this gene related to AMD in the literature, it may be a plausible candidate gene associated with AMD.

Conclusions

Detecting epistatic interactions associated with complex and common diseases has become an important issue in human genetics and can improve prevention, diagnosis and treatment of those diseases. GWAS provides a huge amount of whole genomic data and therefore an unprecedented opportunity to identify causal genes or SNPs for some complex diseases. Traditional statistical methods, however, are not suitable for dealing with large datasets because of their computational complexity. Machine-learning approaches can be scaled to large datasets, but most existing machine-learning methods do not consider the complexity of genetic mechanisms and only focus on the selection of SNPs sets, which show the best classification capacity. This will introduce many false positives inevitably.

In this paper, we use a Markov Blanket-based method, DASSO-MB, to identify epistatic interactions. We compared DASSO-MB with four other methods, BEAM, Support Vector Machine, MDR and stepwise penalized logistic regression over simulated datasets. Our results show that the DASSO-MB algorithm outperforms other methods in terms of the power. It can identify a minimal set of SNPs associated with diseases, which contains less false positives. This is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.

Methods

Markov Blanket

Bayesian networks are probabilistic graphical models representing a joint probability distribution J over a set of random variables X 1 ,X 2 …,X n by a directed acyclic graph (DAG) G and encode the Markov condition property: each node is conditionally independent of its nondescendents given its parents [27]. In this case, the joint probability distribution J can be represented as

where Pa(X i ) denotes the set of parents of X i in G.

For three random variables X, Y and Z, if the probability distribution of X conditioned on both Y and Z is equal to the probability distribution of X conditioned only on Y, i.e., P(X|Y,Z) = P(X|Y), X is conditionally independent of Z given Y. This conditional independence is represented as  We show the number of datasets in which two disease-associated markers can be identified with no more than two false positives. The average number of false positives is in the parentheses.
( | ) X Z Y  . Similarly, ( | ) X Z Y   represents conditional dependence.
Definition 1 (Faithfulness)A Bayesian network N and a joint probability distribution J are faithful to each other if and only if every conditional independence entailed by the DAG of N and the Markov Condition is also present in J [28].

We can define the Markov Blanket of a target variable of T, MB(T), as a minimal set for which Given the definition of Markov Blanket, the probability distribution of T is completely determined by the values of variables in MB(T). Therefore, the detection of Markov Blanket has been applied for optimal variable selection problem [29]. In addition, the Markov Blanket can be used for causal discovery because MB (T) is the union of direct cause variables (parents), direct effect variables (children), and direct cause variables (spouse) of direct effect variables of T. Thus the Markov Blanket learning method is suitable for detection of epistatic interactions in genome-wide case-control studies, e.g., to identify a minimal set of SNPs which may cause the disease for further experiments.
( | ( )) X T MB T  , for all X V T MB T    { } ( ) where V is

G 2 Test

The G 2 test is commonly used to test independence and conditional independence between two variables for discrete data as an alternative to the X 2 test because G 2values are additive and can be applied to more complicated statistical designs [28,30,31]. The null hypothesis for G 2 test is that the two variables are independent.

Assume that we have a contingency table to record and analyze the joint distribution of two variables. The count in a particular cell in a contingency table, x ij , is the value of a random variable from N samples with a multinomial distribution. Let x i represent the sum of elements in all cells along the i th row, and x j  denote the sum of the counts in all cells along the j th column. If these two variables are independent based on the null hypothesis, the expected value of the random variable x ij is:
E x x x N ij i j ( )   (2)
We can compute the conditional independence from appropriate marginal distributions in a similar way. For instance, to determine whether the first variable is independent of the second conditioned on the third, we can calculate the expected value of a cell x ijk as
E x x x x ijk i k jk k ( )    (3)
For n cells in a contingency table, assume that the observed numbers are denoted by O 1 , O 2 , …, O n and the corresponding expected numbers by E 1 , E 2 , …, E n , then, the G 2 is given by
G O O E i i i i n 2 2   ln( )(4)
which has an asymptotical distribution as chi-square (X 2 ) with appropriate degrees of freedom. The degrees of freedom (df) for the G 2 test between two variables A and B can be calculated as:
df = (Cat(A) -1)×(Cat(B)-1)(5)
and the degrees of freedom (df) for the G 2 test between A and B conditional on the third variable C can be calculated as: where Cat(X) is the number of categories of the variable X and n is the number of variables in C. Here in (5) and (6) we assume that there are no empty cells in the contingency table. If there are some empty cells in the contingency table, we should reduce the degrees of freedom from (5) or (6) by the number of empty cells.

As described next, the proposed DASSO-MB uses G 2 to test the association and independence between SNPs and disease status.

DASSO-MB

We use a Markov Blanket-based algorithm, DASSO-MB, to detect gene-gene interactions (Figure 3). Let T denote the disease status and V the set of all variables containing T and all SNPs. There are two types of phases in DASSO-MB: forward phase and backward phase. In each loop of the forward phase, if one variable shows a maximal G 2 score conditioned on MB(T) and is dependent on target variable T, it will be admitted into MB (T). This admission operation is followed by a backward phase to remove false positives by conducting conditional independence tests. If no more variable will be added into MB(T) in the forward phase, we will enter the final backward phase to remove variables that do not belong to MB(T).

There are several methods to find the Markov Blanket for the target T: KS algorithm [32], GS algorithm [33], IAMB [34], MMMB [35] and HITON-MB [29]. Different Markov Blanket methods have their own advantages and disadvantages. For example, IAMB is computationally efficient, but tends to include some false positives and is not sample-efficient. Comparing to IAMB, DASSO-MB adds a backward phase after each step of selecting a variable in the forward phase to remove false positives, make the size of MB(T) as small as possible and therefore improve the sample-efficiency. In addition, it uses subset S of MB (T) rather than the remaining set MB (T) -{Y} while conducting the conditional independence tests in the backward phase. Here we let the size of subset S of MB (T) be larger than zero and exclude the empty set because of the joint effect of set of SNPs on the disease status. These two changes can make the detected results more reliable.
) (T MB Y ∈ If }) { ) ( ( Y T MB S − ⊆ ∃ s.t. ) | ( S T Y ⊥ and 0 ) ( > S size } { ) ( ) ( Y T MB T MB − = ;

Figure 1
1Performance comparison The power is defined as the proportion of simulated datasets whose result only contains two disease associated markers without any false positives.

the variable set in Bayesian network N. The Markov Blanket of a variable T is a minimal set of variables which can completely shield variable T from all other variables. All other variables are probabilistically independent of the variable T conditioned on the Markov Blanket of variable T. We show an example of the Markov Blanket in Figure 2. The MB(T) of the variable T is the set of gray-filled nodes {B, L, M, D, X } and variable S and U are independent of T conditioned on {B, L, M, D, X }. Theorem 1. If Bayesian network N is faithful to its corresponding joint probability distribution J, then for every variable T, MB(T) is unique.

Figure 2
2Markov Blanket in a Bayesian Network The gray-filled nodes are the Markov Blanket of node T.

List of abbreviations used GWAS: genome-wide association studies; DASSO-MB: Detection of ASSOciations using Markov Blanket; SNP: single nucleotide polymorphisms; MDR: multifactor dimensionality reduction; stepPLR: stepwise penalized logistic regression; BEAM: Bayesian epistasis association mapping; MCMC: Markov Chain Monte Carlo; RFE: recursive feature elimination; RFA: recursive feature addition; GA: genetic algorithm; SWSFS: Sliding Window Sequential Forward feature Selection; AMD: Age-related Macular Degeneration; MAF: minor allele frequencies; LD: linkage disequilibrium; HWE: Hardy-Weinberg Equilibrium; DAG: directed acyclic graph.

Table 1
1Three disease modelsModel 1
AA
Aa
aa

Table 2
2Comparison of performance of DASSO_MB, BEAM and SVM algorithms
AcknowledgementsThis work is supported by the US National Science Foundation Award IIS-0644366. This article has been published as part of BMC Bioinformatics Volume 11 Supplement 3, 2010: Selected articles from the 2009 IEEE International Conference on Bioinformatics and Biomedicine. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S3.Authors' contributions BH designed and implemented the DASSO-MB method. BH and MP participated in testing the existing methods and analyzing experimental results. XWC conceived the study and designed the experiments. All authors helped in drafting the manuscript and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
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Psychological aspects in asthma: do psychological factors affect asthma management?

Ilaria Baiardini
Francesca Sicuro
Francesco Balbi
Giorgio Walter Canonica
Fulvio Braido
Psychological aspects in asthma: do psychological factors affect asthma management?
10.1186/s40733-015-0007-1R E V I E W
Despite the regular treatment with inhaled corticosteroids (ICS) or ICS plus long-acting beta2-agonists, permits to control de majority of asthmatics, a significant proportion of patients does not respond to this treatment. This review was aimed to explore the role of psychological factors associated to the unsuccessful fulfilment of optimal levels of asthma control, especially in patients suffering from severe asthma. The results of a Medline search were 5510 articles addressed to different psychological key concepts, constructs and variables. This review will highlight how some selected psychological factors may have a burden on asthma management. Evidences are now available about the link between asthma (in terms of severity and control), some psychological aspects (subjective perception, alexithymia, coping style) and mental health (anxiety, depression). Taking into account this most probably bidirectional influence, a screening of mental symptoms and psychological aspects related to asthma, could lead to plan appropriate interventions to better control asthma and to improve the patient's wellbeing.

Introduction

It is now well recognized that the regular treatment with inhaled corticosteroids (ICS) or ICS plus long-acting beta2-agonists (LABA), permits to control the majority of asthmatics. However, a significant proportion of patients does not respond to this treatment [1,2]. It has been estimated that severe asthma accounts for approximately 5-10 % of the total asthmatic population [3,4]: this range most likely depends on changing definitions and criteria adopted in the past decades. Recently, a Task Force, supported by the European Respiratory Society (ERS) and American Thoracic Society (ATS) [5], proposed a revised definition of severe asthma in children ≥6 years and adults.

Severe asthma has been defined (5) as asthma which requires treatment with guidelines suggested medications for GINA steps 4-5 asthma (high dose ICS and LABA or leukotriene modifier/theophylline) for the previous year or systemic CS for ≥50 % of the previous year to prevent it from becoming "uncontrolled" or which remains "uncontrolled" despite this therapy (as described in Table 1). The definition of high dose inhaled corticosteroids (ICS) is age-specific.

The aim of asthma therapy is to reach and maintain the disease control, minimizing symptoms, daily activity limitation, and the risks for life-threatening exacerbations and long-term morbidity [6]. As available population-based studies clearly illustrate, in a large portion of asthma patients a satisfactory level of disease control is not achieved. In the Asthma Insights and Reality in Europe (AIRE) study, involving more than 2,800 subjects living in different European countries, more than 50 % of the interviewees exhibited daytime symptoms and one out of three respondents experienced asthma-related sleep problems [7]. In the International Asthma Patient Insight Research (INSPIRE) study 3,415 treated asthma adults were interviewed by telephone [8], nearly 74 % used a short-acting bronchodilator every day, and 51 % had at least one exacerbation that required medical treatment in the past year.

An incomplete success of asthma treatment may depend on the presence and interaction of different causes related to the disease itself (i.e. triggers, comorbidities, asthma phenotype), the treatment (i.e. inadequate treatment, inadequate inhaler device), the patient (i.e. socio-demographic factors, adherence, knowledge), the physician (i.e. lack of consciousness and familiarity about guidelines) [9].

Nevertheless, different psychological factors play a primary role in daily asthma management. The ideas that the patient has regarding asthma, the impact of the disease on daily life, the subjective interpretation of symptoms, and therapeutic adherence can be significantly influenced by psychological aspects. Moreover, the presence of the disease itself may have an impact on patients' affective sphere, representing an obstacle to an optimal disease management. It is now recognized that asthma can influence mental health and vice versa [10,11], and that a link exist between psychological functioning and level of asthma control [12].

The purpose of this review is to explore how different psychological aspects may be associated to the unsuccessful fulfilment of optimal levels of asthma control, especially in patients suffering from severe asthma.

To address this issue, Medline searches were performed by pairing the word 'asthma' with the following keywords: psychological factors, psychological disorders, and psychological characteristics. The results of our search were 5510 articles addressed to different psychological key concepts, constructs and variables. This review will highlight how some selected psychological factors may have a burden on asthma management.

Review

Specific psychological factors that likely affect asthma experience and management Symptoms' subjective perception, alexithymia, coping strategies, depression and anxiety are the psychological factors that, in cognitive or in emotional dimensions, are most involved in Asthma experience and management.

The personal report of disease, the difficulty in recognizing feelings and sensations, the ability to solve problems and tolerate stress, the presence of emotional and mental health disorders, can influence the success of the treatment and the prognosis of the disease.

The relationship between each dimension and the experience of Asthma will be analyzed in detail.

Symptoms' subjective perception

Patients' report of symptoms constitutes an important factor in determining the diagnosis of asthma and in influencing the treatment plan. However, self-reported symptoms poorly correlate with pulmonary function measures and the perception is not necessarily linearly related to the sensory input: patients may exhibit limited disease severity, but describe high levels of dyspnea, or vice versa. From 15 to 60 % of patients [13], depending on the measurements used, report difficulty in recognizing their own degree of airway obstruction. Poor symptom perception may result in a significant overuse of reliever medication, irrespective of lung function, whereas under-perception presents the potential risk of delayed treatment. In a 2-year follow-up, patients with both increased and decreased symptom perception exhibited a four-fold increased rate of visits to the emergency department, a five-fold increased hospitalization rate, and a six-fold increased rate of nearly fatal or fatal asthma attacks [14]. As these findings show, perception represents a relevant key factor in the management of this chronic condition.

A study of Lurie [15] shows that the variables actually used by patients to determine the severity of their asthma, are very different from those that doctors employ to assess and monitor asthma severity and, in general, patients have the tendency to underestimate the level of disease severity.

The accuracy of symptom perception depends on a wide variety of cognitive and affective variables, such as emotional state, previous life experiences, attributions, contextual information, attentional and learning processes, expectation and prior asthma experiences, personality traits, and psychopathologic disturbances. All these factors can profoundly affect the perception of dyspnea and asthmatic symptoms, often independent of airflow obstruction. Many subjects with asthma learn to associate negative situations and emotional distress with difficult breathing and are thus likely to over-perceive dyspnea.

Studies in healthy volunteers and asthma patients demonstrate that subjects with high negative emotionality, tend to report more dyspnea than those with low negative emotionality. Similarly, the experimental induction of negative affective states in healthy individuals and patients in able to increase reports of respiratory sensations such as dyspnea. The feeling of unpleasantness related to dyspnea, rather than its sensory intensity, appears to be related to affective influences [16]. Individuals with asthma may under-perceive dyspnea when experiencing a positive Table 1 The definition of uncontrolled asthma [5] Uncontrolled asthma defined as at least one of the following: 1) Poor symptom control: ACQ consistently >1.5, ACT <20 (or "not well controlled" according NAEPP/GINA guidelines);

2) Frequent severe exacerbations: two or more bursts of systemic CS (>3 days each) in the previous year;

3) Serious exacerbations: at least one hospitalisation, ICU stay or mechanical ventilation in the previous year; 4) Airflow limitation: after appropriate bronchodilator withhold FEV1 < 80 % predicted (in the face of reduced FEV1/FVC defined as less than the lower limit of normal);

Controlled asthma that worsens on tapering of these high doses of ICS or systemic CS (or additional biologics) * GINA Global initiative for asthma, ICS Inhaled corticosteroids, LABA, Long-acting beta2-agonists, CS Corticosteroids, ACQ Asthma control questionnaire, ACT Asthma control test, NAEPP National asthma education and prevention program, ICU Intensive care unit, FEV1 Forced expiratory volume in the first second, FVC Forced vital capacity emotional state, potentially reducing the use of prescribed medications [17].

Alexithymia

Alexithymia, a term derived from the Greek words alexis ("no words") and thymos ("emotion"), is a personality trait characterized. As presently defined, the alexithymia construct is composed of the following factors: (i) difficulty in identifying feelings and distinguishing between feelings and bodily sensations; (ii) difficulty in describing feelings; (iii) constricted imaginative processes; and (iv) a stimulus-bound, externally oriented cognitive style [18]. Persons with alexithymia have an impaired ability to build mental representations of their emotions, and therefore they misinterpret physical symptoms related to emotional arousal (i.e., tachycardia or tremors, dyspnea) as symptoms of somatic diseases. These subjects may increase susceptibility to disease development, therefore alexithymia is considered as a risk factor for a variety of medical conditions.

Patients with alexithymia tended to perceive and to live their disease as a cyclical disorder, not a chronic condition, and they feel high levels of distress. Furthermore, in alexithymic patients, asthma is less controlled, independent of the GINA classification of severity [6], and the lack of control leads to a reduced Health Related Quality of Life (HRQoL). Alexithymia altered the recognition of sensations and symptoms, judgments, thoughts, and emotions related to respiratory allergy. Summarizing, in asthmatic patients, the presence of alexithymia leads to report more serious negative consequences and emotions, affecting physical, psychological, and social aspects of life [19][20][21][22]. Literature concerning the links between alexithymia and asthma helps to identify some of the reasons for non-optimal disease management.

Studies focused on patients with extremely severe or nearly fatal attacks, suggest that this personality trait is associated with disease exacerbations. It has been shown that a significantly higher prevalence of alexithymia occurs among patients who have experienced a nearly fatal asthma attack (36 %), compared with patients with matched asthma severity who have not experienced a nearly fatal attack (13 %) [20] or with the general population (5-13 %). A study evaluating 270 asthmatic subjects during a severe attack showed that alexithymic patients reported significantly lower scores for nine symptom and sign categories on the Asthma Symptom Checklist scale [20]. These results show that alexithymic patients tend to underestimate both physical and emotional components of asthma exacerbations, irrespective of of the severity of the disease. A greater frequency of recurrent hospitalizations due to asthma was found in alexithymic patients (37.4 versus 28.4 % over 6 months): the difficulty in recognizing and expressing symptoms intensity and frequency could lead patients underestimate the risk of asthma exacerbation [22]. In addition, higher alexithymia scores have been found to be associated with increased report of asthma symptoms and with decreased pulmonary function, as indicated by %FEV1/ FVC [23].

A recent observational study shows pulmonary function and disease control were associates to but not to anxiety and depression [24]; furthermore the level of alexithymia correlated positively with mood and anxiety.

Moreover, the presence of alexithymia has been associated with poor treatment adherence and worse HRQoL. More in detail, it was found a correlation between alexithymia and intentional non-adherence, that is the result of an active choice [25].

The prevalence of alexithymia resulted higher than in general population, indicating that it is a common feature that characterizes almost 1 patient out of 5 [26]. Despite no differences between alexithymic and non alexithymic patients were observed for what concerns GINA severity level, the level of alexithymia was associated to different disease experiences and outcomes. In alexithymic patients, asthma is less controlled, independently of GINA classification, and the lack of control leads to a poorer HRQoL.

Moreover, in asthmatic patients who develop post traumatic stress disorder (PTSD) symptoms after an asthma attack, alexithymia represents a factor that mediate the relationship between asthma severity and psychiatric comorbidity [27].

Coping strategies

The study of coping describe how people deal with stressful events and ongoing situations [28]. Suffering from a chronic disease such as asthma predisposes individuals to a degree of stress that requires continuous cognitive, emotional, behavioral, and social adaptation. Coping efforts may help to modify the situation, change the meaning of the experience, or mitigate the stress related to asthma management (e.g. improve asthma control, accept the disease, be optimistic about the sickness course). Active coping strategies addressed directly to the problem are considered to be adaptive, whereas passive, emotionfocused coping is related to poor adjustment to illness [28].

Studies of chronic disorders have underlined significant associations between coping styles and clinical outcomes like physical functioning, levels of disease control, morbidity, mortality and HRQoL [29]. Research suggests avoidant coping (for example, ignoring, denying, or avoiding a problem) is associated with poor HRQoL among adult asthma patients [30,31], whereas active coping (taking an active approach to the problem, whether cognitive or behavioral) is associated with better HRQoL scores [30,31]. Asthmatic patients with effective coping skills (characterized by maintaining a positive vision of the disease without minimizing its potential danger and by using active, cognitive strategies and flexible and diversified behaviors) will experience less psychological morbidity, a feeling of greater personal control over asthma, and better long-term management of the disease [32]. In contrast, the use of avoidance strategies is associated with a lower level of asthma control, with a worsening of clinical outcomes, and with a greater number of hospitalizations, unscheduled health care visits, and episodes of nearly fatal asthma [33,34]. Patients with ineffective coping strategies tend to have difficult-to-treat asthma: interviews regarding behaviours to adopt during an hypothetical asthma attacks, showed that patients suffering from brittle asthma tend delayed up to 7 days before seeking medical attention [35].

Asthmatic subjects with avoidant strategy preferences are less adherent to medical therapy and prone to reliever drug abuse and to inadequate controller drug use [32]. Coping skills training and educational interventions aimed at improving coping strategies can increase a subjective sense of competence and self-efficacy in dealing with a wide range of daily demands and health issues, with benefits not only to asthma symptoms but also to psychological functioning, sense of well-being, and anxiety [36].

A large, cross-sectional survey, involving 6,474 patients and 3,089 general practitioners [29], explored coping in asthma management. Active coping strategies were described by approximately one-half of the patients, whereas the physicians reported a significantly lower frequency of effective coping strategies in their patients. A similar percentage of patients and a greater percentage of physicians reported the use of negative strategies, with "rely on fate or faith" chosen more frequently by patients than physicians. The degree of concordance between the choices of patients and general practitioners was only fair, indicating inadequate patient-physician communication.

Depression and anxiety

Asthma has long been associated with symptoms of mood and anxiety disorders [37,38].

Available studies show that the prevalence of anxiety and depressive disorders is more elevated among asthma patients than in general population. However, the association of mental health problems with asthma severity is controversial. Some studies have shown significantly higher level of anxiety and depression in patients with severe asthma as compared to those with milder disease, while other studies did not detect such differences [39,40].

Clinical data has shown that the presence of psychiatric and psychological symptoms is associated with increased severity of asthma symptomatology, health service use and costs, functional impairment and poorer asthma control [41][42][43][44].

Depression would be significantly worse among severe asthmatics as compared to those with less severe asthma, and lower levels of asthma control would explain the greater degree of depressive symptoms in severe asthma. Individuals with severe asthma endorsed more symptoms of depression than those with milder asthma, with the severe asthmatics who endorsed poor asthma control being at particularly heightened risk. Psychological morbidity seems to be associated with increases in asthma severity, routine screening for depression among patients with severe asthma may result in the opportunity to tailor patient-centred clinical asthma management efforts resulting in improved disease management and control [40].

Children and adolescents with symptomatic asthma are more likely to suffer from a wide range of mental health problems, compared to healthy children. The likelihood of mental health problems appears closely associated to asthma severity. Youth with poorly controlled and/or more severe and persistent asthma may be considered a group at high vulnerability for mental health problems. Management for paediatric asthma should include screening tools and counselling interventions to detect, prevent and reduce the risk of mental health problems [40].

A study of 67 adults with severe prednisone-dependent asthma, 47 with severe non prednisone dependent and 73 patients with mild-moderate asthma [45] was aimed to investigate the prevalence of anxiety and depression symptoms. That research suggests that there are significantly more anxiety and depression symptoms in patients with severe prednisone dependent asthma compared to patients with non-steroid dependent severe asthma or mild-moderate asthma: patients with prednisonedependent asthma were 3.5 times more likely to have clinically significant depression symptoms, and about 2.5 times more likely to have significant anxiety symptoms as compared to patients with severe or mildmoderate disease. However, the available data about the effect of steroids on mood are poor and few controlled studies have been performed to assess treatment for steroids induced psychiatric, cognitive and behavioural problems [46].

A cross-sectional study conducted at the Asthma Outpatient Clinic of the Federal University of São Paulo Hospital São Paulo [47] show how the prevalence of anxiety is higher in the patients with uncontrolled asthma than in those with controlled asthma.

In stable primary care patients, both physician and patient-reported depressive symptoms were associated to asthma severity. On the contrary, an association between depression and level of asthma control has been detected according patients' reports, but not in those of physicians [48].

The available evidence suggests that asthma may precede and predispose to the development of anxiety and mood disorders, but also that the presence of psychological and behavioural problems may precede and predispose to asthma [49].

From a neurobiological perspective, several interesting hypotheses have been suggested with the aim to explain the association between asthma ad mood disorders: systemic inflammation seems to be a key link between these conditions and studies conducted so far suggesting a possible role of cytokines in the neurotransmission in the central nervous system (CNS) [50].

Conclusion

The psychological characteristics of asthmatic patients and the presence of mental problems, have been shown to be linked to both asthma severity and level of disease control. Difficulties in achieving the objectives of asthma therapy suggested by current guidelines may depend also on psychological factors such as symptoms' perception, alexithymia, coping strategies, mood disorders.

Despite numerous data support this association, a causal relationship between asthma and mental health is not clear. The hypothesis of a bidirectional influence seems to be more acceptable.

Independently of the nature of this association, when a psychological problem or difficulty is present, it interferes with an optimal disease management, especially in patients with severe asthma and poor control. At the same time, the psychological characteristics of asthmatic patients have an influence on symptoms' recognitions, daily management and disease outcomes.

A screening of mental symptoms and psychological aspects that are known as associated to asthma, could lead to plan appropriate intervention to better control asthma and to improve the patient's well-being.
 AcknowledgementsThe study was supported by ARMIA (Associazione Ricerca Malattie Immunologiche e Allergiche) and ASPADIRES (Associazione Pazienti Disturbi Respiratori nel Sonno).Competing interestThe authors declare that they have no competing interests.
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