Vêlayoudom Marimoutou
email: velayoudom.marimoutou@univ-amu.fr

Manel Soury
email: manel.soury@yahoo.fr

Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model

Keywords: CO 2 emissions, dependence, SCAR copula, Efficient Importance Sampling, GAS model

 in which the parameter driving the dynamic of the copula follows a stochastic autoregressive process. The standard likelihood method will be used together with Efficient Importance Sampling (EIS) method, to evaluate the integral with a large dimension in the expression of the likelihood function.

The main result suggests that the dynamics of the dependence between the volatility of the CO 2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils.

Introduction

Under the Kyoto protocol, OECD countries must reduce their emissions of greenhouse gas by a minimum of 5% from 1990 levels during the period 2008-2012. In this framework, the European Union has decided to reduce CO 2 emissions by 8%. To do that, the EU has proposed a framework scheme known as the European Union Emission Trading Scheme (EU-ETS) to determine the price of CO 2 (carbon dioxide) emissions. In this context, european plants with large CO 2 emissions obtain from their governments allowances to emit metric tonnes of CO 2 equivalent. Permits can be traded in spot, future and option markets. In the european context, the first phase of trading was in the years 2005 -2007 and the second one was in the years 2008 -2012 coinciding with the introduction of the Kyoto protocol. The third trading phase started in 2013 and will last until December 2020. In the European Union, the higher production of carbon dioxide emissions is concentrated on the power generation sector and on the small number of plants. The power sector and the heat generation industry drive approximately 55% of the total allowance in the first phase and thus are the key players in the EU-ETS and their behavior greatly influences the carbon price dynamics. The purpose of the EU-ETS trading scheme is to encourage firms to reduce their emissions. For [START_REF] Paolella | Econometric analysis of emission trading allowances[END_REF] [START_REF] Paolella | Econometric analysis of emission trading allowances[END_REF] the scarcity of allowances will drive-up the trend in prices. However, the short life of the prices of CO2 emissions is associated with a large level of uncertainty. The price of carbon is usually determined by the market structure and institutional policies. The level of emissions depends on unexpected movements in energy demand, the prices of oil, gas, coal,. . . and weather conditions (temperatures, rainfall,. . . ). Bredin and Muckley (2011) [START_REF] Bredin | Microstructure analysis of the carbon finance market[END_REF] show that this market is driven by its fundamental variables, and can be affected by economic growth and/or financial markets. So what are the factors that determine the price of CO 2 ? In a survey, [START_REF] Springer | The market for tradable ghg permits under the kyoto protocol: a survey of model studies[END_REF] [START_REF] Springer | The market for tradable ghg permits under the kyoto protocol: a survey of model studies[END_REF], shows that among the cofactors that determine the CO 2 emission allowance prices, energy prices and climatic conditions are fundamentals. The main drivers of the price of carbon can be categorized as factor driver by demand and supply forces. Thus, the key supply factors are the number of emission allowances, allocated to individual installations in the National Allocation Plans by the EU, as well as other regulatory uncertainties.

The demand factor, however, is more dynamic and the allowance demand is strongly influenced by the demand for electricity. As a result, factors that influence the demand for electricity , such as (extreme) temperature, seasonality and general economic activity are also thought to drive the demand for carbon emission allowances. In the recent literature about the empirical relationship between European Union Allowances prices and its fundamentals, a large theoretical review of the determinants was made by [START_REF] Springer | The market for tradable ghg permits under the kyoto protocol: a survey of model studies[END_REF] [START_REF] Springer | The market for tradable ghg permits under the kyoto protocol: a survey of model studies[END_REF]. [START_REF] Christiansen | Price determinants in the eu emissions trading scheme[END_REF] [5] identifies economic growth, energy prices and weather conditions as key drivers of EUA prices. [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF] [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF] found that the industrial production impact positively (negatively) the carbon market during periods of economic expansion (recession), confirming the relationship between macroeconomics and the price of carbon. (Burniaux (2000) [START_REF] Burniaux | Carbon emission leakages: A general equilibrium view[END_REF], Ciorba et al (2001) [START_REF] Ciorba | Kyoto commitment and emissions trading: A european union perspective[END_REF], Sjim (2005) [START_REF] Sijm | The interaction between the eu emission trading scheme and national energy policy schemes[END_REF] and van der Mensbrugghe (1998)) in the same way showed that energy prices influence CO 2 prices. Redmond and [START_REF] Convery | Market and price developments in the european union emissions trading scheme[END_REF] [START_REF] Convery | Market and price developments in the european union emissions trading scheme[END_REF], Battaler et al (2013) [START_REF] Bataller | co 2 prices, energy and weather[END_REF], Alberola et al (2008) [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF] and all studies including energy variables, assumed geometrical brownian motion process for modeling energy prices.

To model electricity, natural gas spot prices, commodity prices, or to describe energy commodities, we use a geometric brownian motion with mean reversion in a long term value θ in the drift term. Concerning the stochastic volatility model, Eydeland and Geman (2005) [START_REF] Eydeland | Fundamentals of electricity derivatives[END_REF] extend the Heston model (1993) [START_REF] Heston | A closed-form solution for options with stochastic volatility with applications to bonds and currency options[END_REF] to gas and/or electricity prices. The movements in price are, however, not independent. If they were, then it would be possible to form a portfolio with negligible volatility. To understand the relative magnitude of all these correlations and why they change, it is important to look at the economic factors behind the movements in asset prices. Changes in asset prices reflect changing forecasts of future payments. The information that changes the forecasts is often called "news". Every element of news affects all asset prices; this is one of the most important reasons why correlations change over time. The second im-portant reason is the characteristics of the news change. Time variations arise only from substituting volatility in the innovation for dividends. If there is no predictability in expected returns, then this is also the conditional variances of returns. The longer the memory of the dividend process, the more important is this effect and the greater is the volatility. For these energy commodities, the price is strongly time dependent, and consequently, the covariance and the unconditional correlation are time dependent as well.

We examine, in this paper, the dependence between the conditional volatility of the prices of CO 2 emissions with the conditional volatility of their fundamentals (energy prices): coal, natural gas and Brent Oil as well as the SP-GSCI energy price. We focus on energy because it is used in industrial production and activities with high fossil fuel consumption, and consequently have large CO 2 emissions and as energy prices are one of the main factors determining and driving the carbon prices as stated above. The dependence between the returns will be approached by a particular class of dynamic copula, the Stochastic Autoregressive Copulas (SCAR), a time varying copula that was first introduced by Hafner and Manner (2008) [START_REF] Hafner | Dynamic stochastic copula models: estimation, inference and applications[END_REF] in which the parameter driving the dynamics of the copula follows a stochastic autoregressive process and takes into account the non linearity of the data. In this copula the parameters of volatilities and dependence are estimated by standard maximum likelihood together with Efficient Importance Sampling.

Our article contributes to the literature in several important aspects. We use the dynamic SCAR copula approach to examine the relationship over time between the variables in pairs. In other words, we examine the dynamics of the correlation or dependencies in term of conditional volatility pair by pair between the carbon dioxide emission prices and the other energy prices: (CO 2 /Brent oil, CO 2 /Natural Gas,CO 2 / SP energy index and the CO 2 /coal). We used the dynamic SCAR copula approach, the choice of the best fitted copula model, presented as follows, for each pair mentioned above, respectively, is based on the log-likelihood criteria: the rotated Gumble copula, the Gumbel, the Normal copula and the Frank copula. We have observed for the last pair a strong corre-lation and common movement, after mid-2011, in the level of trends (obtained by a decomposition analysis in state space framework (see fig 4 in Annex). In addition it is important to mention the fact that since the CO 2 emission prices are traded essentially in European countries, our variables concern also the European markets, except the Natural Gas that is traded in the American market.

However we have kept it since it does not differ from the evolution of Natural Gas prices in European countries. Copulas are a flexible, non-standard tools that help decomposing any multivariate distribution into marginal distributions, that describe individual behavior, and fully capture the dependence between the variables. The fact that dependencies can be modeled independently of the marginal distributions, contributed to the expansion of this approach especially since it can be applied over the various type of data and not only financial ones.

By focusing on the particular case of the dynamic type of copula, we have improved our model further, since investigating the dependence structure between the commodities and CO 2 emission prices through time is much more realistic and efficient than doing it in a static way. In this context, we find some papers that used the copula approach either in its static version or its dynamic one introduced by Patton, with different commodities. However, in our knowledge, this is the first paper to deal with the implication of energy price commodities on CO 2 emission prices by means of the dynamic SCAR copula. To the best of our knowledge, copulas have been used in commodities markets by Zohrabyan (2014) [START_REF] Sukcharoen | Interdependence of oil prices and stock market indices: A copula approach[END_REF], Kharoubi and German(2008) [START_REF] Gemana | Wti crude oil futures in portfolio diversification: The time-to-maturity effect[END_REF], [START_REF] Reboredo | How do crude oil prices co-move?: A copula approach[END_REF] [START_REF] Reboredo | How do crude oil prices co-move?: A copula approach[END_REF], [START_REF] Nguyen | Copula model dependency between oil prices and stock markets: Evidence from china and vietnam[END_REF] [START_REF] Nguyen | Copula model dependency between oil prices and stock markets: Evidence from china and vietnam[END_REF], [START_REF] Alouia | A time-varying copula approach to oil and stock market dependence: The case of transition economies[END_REF] [START_REF] Alouia | A time-varying copula approach to oil and stock market dependence: The case of transition economies[END_REF] and Syed et al(2014) [START_REF] Bashera | Dependence patterns across gulf arab stock markets: A copula approach[END_REF]. In addition, the returns of the series are modeled by the Generalized Autoregressive Score (GAS) model that can deal with the jumps, and occasional and temporary changes in the returns better than the GARCH-type model and thus lessens the impact of occasional extreme observations in the series. With the GAS model, the time-varying parameter which characterizes the conditional distribution can be updated using the scaled score of the likelihood function. In section two we present the model and the estimation method used; in section three, the empirical results will be presented and discussed before concluding.

The model

The SCAR model

We introduce the Stochastic Copula Autoregressive (SCAR) model proposed by [START_REF] Hafner | Dynamic stochastic copula models: estimation, inference and applications[END_REF] [START_REF] Hafner | Dynamic stochastic copula models: estimation, inference and applications[END_REF], that can be seen as a multivariate stochastic volatility model. We consider the bivariate time series (u 1,t , u 2,t ) for t = 1 . . . T distributed using a time varying copula C with a dynamic parameter θ:

(u 1,t , u 2,t ) ∝ C(u 1 , u 2 |θ t ) (1)
where θ t ∈ Θ ⊂ R. We suppose that θ t is driven by a latent stochastic process where θ t = Ψ(λ t ) and Ψ : R → Θ is a predefined function to assure that the copula parameter is defined in its own domain, depending on the chosen copula.

1
λ t is an unobservable underlying process that follows a first order autoregressive process:

λ t = α + βλ t-1 + κε t , |β| < 1, κ > 0 (2)
with ε t is a Gaussian innovation process. The observed variables are transformed into uniform distribution. In the SCAR copula the dynamics are not generated by the data/observations as in the dynamic conditional correlation model or the copula based on the Patton model (Patton, 2006 [20]), but by an independent stochastic process. This model is non linear and can be written in its state-space form. The state equation given by

(u t , v t )|λ t ∼ C(u, v|Ψ(λ t )) (3)
and the transition equation:

λ t = α + βλ t-1 + κε t ( 4
)
1 For Frank copula the transformation Ψ is Ψ(x) = x, for the Clayton copula, it is Ψ(x) = exp(x), for the Gumbel copula Ψ(x) = exp(x) + 1 and for the Gaussian, the Ψ is the inverse Fisher transform, Ψ(x) = exp(2x)-1 exp(2x)+1 .

Estimation

Before focusing on the dependence structure that can be defined using the copula function, we first need to obtain uniform inputs from the marginal distributions. In the first step, we fit a Generalized Autoregressive Score (GAS) model and then we extract standardized residuals. and the copula likelihood(L C ). The joint likelihood is written as follows:

L(δ 1 , δ 2 , θ, X 1 , X 2 ) = L X1 (δ 1 ; X 1 ) + L X2 (δ 2 ; X 2 ) + L C (θ; F (X 1 , δ 1 ), G(X 2 , δ 2 )) (5)
F and G are the respective marginal distributions, of the processes X 1 and X 2 , δ 1 and δ 2 are the parameters of each distribution. The inference function for margins (IFM) will be used to estimate the parameters of the previous equation.

In the first stage only the marginal distributions parameters are estimated:

δ = argmaxL Xi (δ i ), i = 1, 2 (6)
In the second stage, the dependence parameter or the copula parameter is estimated from the copula likelihood L C w.r.t the estimated parameters of the marginal distributions.

θ = argmaxL C (θ, δ) (7)
The likelihood function of stochastic copula models is complex and cannot be evaluated numerically using only the ML. Instead we use a Maximum Likelihood technique by introducing the Efficient Importance Sampling (EIS) procedure introduced by Liesenfeld and Richard (2003) [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF] and Richard and Zhang (2007) [START_REF] Richard | Efficient high-dimensional importance sampling[END_REF]. As the model has nonlinear state space form, the estimation procedure can also be considered in a nonlinear filtering algorithm and consequently can be related to the nonlinear filtering technique (Doucet et al , 2001[23]). A particular case of the latter is the method of importance sampling, discussed by [START_REF] Durbin | Time series analysis of non-gaussian observations based on state space models from both classical and bayesian perspectives[END_REF]Koopman (2000, 2001)[24] and used to estimate time series models with a state space representation. The EIS procedure, a particular importance sampling technique, is a Monte Carlo (MC) technique aiming to evaluate integrals with high dimensions. Using this technique, we will evaluate efficiently and flexibly the likelihood function with high-dimensional variables in its expression.

When we combine both the ML and the EIS, we call it the Maximum Likelihood-Efficient Importance Sampling (ML-EIS) method, and we obtain asymptotically efficient estimators. In this section, we describe and adapt the EIS method to estimate the parameters of our model.

The aim is to estimate the vector of parameters ω = (α, β, κ) of the dependence structure. We consider the bivariate process (

U 1 ; U 2 ) with U 1 = {u 1,t } T t=1 and U 2 = {u 2,t } T t=1 . The function f (U 1 , U 2 , Λ; ω)
represents the joint density of the two variables (U 1 ; U 2 ) and the latent process Λ defined by Λ = {λ t } T t=1 . The likelihood associated to (U 1 , U 2 ) with the parameter ω is the following:

L(ω; U 1 , U 2 ) = f (U 1 , U 2 , Λ, ω)dΛ (8)
which can be factorized into a product of conditional densities:

L(ω; U 1 , U 2 ) = T t=1 f (u 1,t , u 2,t , λ t |U 1,t-1 , U 2,t-1 , Λ t-1 , ω)dΛ (9)
Here U 1,t-1 = {u 1,1 ...u 1,t-1 }, the same applies for U 2,t-1 and Λ t-1 .

The joint density is:

f (u 1,t , u 2,t , λ t |U 1,t-1 , U 2,t-1 , Λ t-1 , ω) (10)
which can be decomposed into the copula density c(u

1,t , u 2,t |λ t , U 1,t-1 , U 2,t-1 , ω) multiplied by the conditional density of λ t w.r.t (U 1,t-1 , U 2,t-1 , Λ t-1 ), p(λ t |U 1,t-1 , U 2,t-1 , Λ t-1 , ω).
Since the conditional density p is independent of past observations of (U 1,t-1 , U 2,t-1 ), then we can get rid of it and the new expression of the likelihood function can be written as:

L(ω; U 1 , U 2 ) = T t=1 c(u 1,t , u 2,t |λ t , U 1,t-1 , U 2,t-1 , ω) × p(λ t |Λ t-1 , ω)dΛ (11)
A natural estimate of the likelihood is based upon drawing sample paths from the sequence of densities p also called the natural sampler, which are directly obtained from the statistical specification of the model. In other words, it would be better to simulate the T -integral likelihood instead of evaluating it since it cannot be determined by analytical or numerical methods because of its high dimensionality. Then, we simulate a large number N of trajectories

λ(i) t (w) T t=1
from the natural sampler p. In that case, the likelihood function is written as :

LN (w, U 1 , U 2 ) = 1 N N i=1 T t=1 c(u 1t , u 2t | λ(i) t (w), U 1,t-1 , U 2,t-1 , w) (12)
However, this ignores the fact that the observable variables contain information on the latent process as quoted in [START_REF] Danielsson | Quadratic acceleration for simulated maximum likelihood evaluation[END_REF] [START_REF] Danielsson | Quadratic acceleration for simulated maximum likelihood evaluation[END_REF] and [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF] [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF] since the λ(i)

t (w) T t=1
are simulated independently of the observed variables U 1 and U 2 and thus the information given by the data is not exploited. As a result, such estimates exhibit very large variance and require a large number of draws. To overcome this issue, the Efficient Importance Sampling method (EIS) aims to find or to construct new samplers such that the information on Λ contained in the data (

U 1 , U 2 ) is ex- ploited. Let {m(λ t |Λ t-1 , a t )}
T t=1 be a sequence of auxiliary samplers indexed by the parameters a t to be estimated. The likelihood function can have the following expression:

L(ω; U 1 , U 2 ) = T t=1 f (u 1,t , u 2,t , λ t |U 1,t-1 , U 2,t-1 , Λ t-1 , ω) m(λ t |Λ t-1 , a t ) T t=1 m(λ t |Λ t-1 , a t )dΛ (13)
which can be calculated by using

N trajectories λ(i) t (a t ) T t=1 drawn from the importance sampler m by LN (ω, U 1 , U 2 ) = 1 N N i=1 T t=1 f (u 1,t , u 2,t , λ(i) t |U 1,t-1 , U 2,t-1 , Λ(i) t-1 (a t-1 ), ω) m( λ(i) t (a t )| Λ(i) t-1 (a t-1 ), a t ) (14)
The main idea behind the EIS method is to find a sequence of auxiliary constants {a t } T t=1 and a function m providing a good match between the numerator and the denominator in the equation ( 14) so that the variance of L is minimized.

The problem is the high dimensionality and therefore we decompose the task into a sequence of manageable low-dimensional optimization sub-problems. The function f will be approximated by a function k(Λ t , a t ) such that:

m(λ t |Λ t-1 , a t ) = k(Λ t , a t ) χ(Λ t-1 , a t ) (15)
and

χ(Λ t-1 , a t ) = k(Λ t , a t )dΛ t (16)
The right match between f and m can be seen as a match between f

(Λ t , U 1,t , U 2,t | Λ t-1 , U 1,t-1 , U 2,t-1
, ω) and k(Λ t , a t ) its functional approximation, and also the density kernel of m.

Jointly, this gives us a method to obtain the auxiliary constants {a} T t=1 . We need to solve the following least squares problems for each period t with low dimensions:

ât = argmin at N i=1 log Ft -c t -ln k( Λ(i) t (w); a t 2 (17)
with

Ft = f u 1,t , u 2,t , λ(i) t (w)|U 1,t-1 , U 2,t-1 , Λ(i) t-1 (w).χ( Λ(i) t-1 (w); ât+1 ) ( 18
)
for t = T..1 and χ(Λ T ; a t+1 ) ≡ 1. The EIS estimate of the likelihood function is obtained by substituting the estimated sequence {â t } T t=1 and N draws from the importance sampler m into the likelihood function in the equation [START_REF] Sukcharoen | Interdependence of oil prices and stock market indices: A copula approach[END_REF].

In addition, if k(Λ t ; a t ) as well as ξ belong to the exponential family, the EIS least-squares problem in [START_REF] Nguyen | Copula model dependency between oil prices and stock markets: Evidence from china and vietnam[END_REF] become linear in a t . The expression of k proposed by [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF] [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF] is:

k(Λ t ; a t ) = p(λ|λ t-1 , ω)ξ(λ t , a t ) (19)
with

ξ(λ t , a t ) = exp(a 1,t λ t + a 2,t λ 2 t ) (20)
to simplify the least square system above and make it linear. In this case, the conditional mean and variance of m are the following:

µ t = σ 2 t × ( α + βλ t-1 ν 2 + a 1,t ), (21)
and

σ 2 t = ν 2 1 -2ν 2 a 2,t (22)
The explicit expressions of p, k and ξ are given by Liesenfeld and Richard (2003) [START_REF] Liesenfeld | Univariate and multivariate stochastic volatility models: estimation and diagnostics[END_REF]. The steps required to compute and implement the EIS estimation of the likelihood function are the following:

• Step(1): draw N trajectories called λ(i) t (ω) T t=1
by using the natural sampler p. from m, the importance sampler, from which the EIS estimate of the likelihood function is evaluated according to the equation ( 14).

Estimating the Underlying Process

Obtaining the set of the estimated parameters (α, β, κ) of the underlying process is crucial, however, not enough to determine exactly the path of the dependence pattern of observations. In other words, we need to get an estimate of the sequence of the process {λ t } as well as the function Ψ(λ t ). For this matter we make use of the EIS method. The EIS exploits all the information provided by the set of variables and thus generates efficient samples of the underlying process

{λ t } T t=1
, to which we apply the transformation Ψ. Finally, the dependence path or the smoothed estimate of Ψ(λ t ) is obtained by this expression:

Ψ( λt/T ) = 1 N N i=1 Ψ λ(i) t (â t ) (24)

The Model for the Marginal Distributions

It is crucial to capture the dynamic behavior of the time series data whether in their one-dimensional or multidimensional form. In this context, we can categorize time series with time-varying or dynamic parameters to two types of models: parameter driven models and observation driven models. A time series process is an observation driven model when the dynamics of its parameters are introduced by allowing the parameters to be functions of past observations, exogenous variables and lagged dependent variables. We consider the Generalized Autoregressive Score (GAS) model introduced by Harvey (2013) [START_REF] Harvey | Dynamic models for volatility and heavy tails: With applications to financial and economic time series, Dynamic Models for Volatility and Heavy Tails With Applications to Financial and Economic Time Series[END_REF], which belongs to the latter class, and presents a set of advantages. The parameters can be predicted given past information of the data, and so the likelihood eval-

y t = σ t ε t (25)
where ε t is a Gaussian disturbance with a unit variance and zero mean and σ t is the standard deviation. In this case the GAS(1,1) is equivalent to the GARCH(1,1) and the variance can be written as the following

σ 2 t+1 = α 0 + α 1 y 2 t + β 1 σ 2 t ( 26
)
For the GAS(1,1) model combined with Student-t or a Skewed-Student distribution as suggested by Harvey and Chakravarty (2008), the conditional variance is given by:

σ 2 t = ω 1 + α 1 u t-1 σ 2 t-1 + φ 1 σ 2 t-1 (27)
with

u t = (ν 1 + 1)ε 2 t ν 1 -2 + ε 2 t -1, if ε t ∼ St(0, 1, ν 1 ) (28)
and

u t = (ν 1 + 1)ε t ε * t (ν 1 -2)g t ζ I t -1, if ε t ∼ SKSt(0, 1, ζ, ν 1 ) (29)
3. Empirical results

Data description

The data set is composed of the CO 2 emission spot prices 2 , and by dif- For the energy commodities considered, we take crude Brent oil 3 , natural gas (Natural Gas-Henry Hub /MMBTU) 4 and Coal (Coal ICE API2 CIF ARA). 5 Then we consider the variable SP Goldman Sachs Energy Total returns index and covers around 45% of the EU's greenhouse gas emissions 3 it is one of the major classifications of oil and can serve as a major benchmark price for purchases of oil worldwide and typically refined oil in Northwest Europe. In fact, most Brent oil is destined for European markets and large parts of Europe now receive their oil from the brent oil. Petroleum production from Europe tends to be priced relatively to this oil. 4 The Henry Hub pipeline is the pricing point for natural gas price on the New York Mercantile Exchange. This index is considered as a benchmark for the entire North American natural gas market and denominated in $/mmbtu (millions of British thermal units) 5 Here we consider the coal prices from the Intercontinental Exchange ICE, a market based in Atlanta, Georgia, that facilitates the electronic exchange of energy commodities. The API By referring to figure 2 in the appendix, the series in levels are non stationary and are integrated of order one. Then we consider r t = ln(P t ) -ln(P t-1 ), the returns 7 of each variable with P t being the price at time t (Figure 3 in Annex).

Referring to the plots of the different returns, there is evidence of time varying and clustering volatility. Meaning that high (low) values of volatility tend to be followed by high (low) values.

Table 1 presents a summary statistics of the variables. All returns have a mean close to zero. Neither of the series had a significant trend over the sample, since means are very small relative to the standard deviations. In order to detect a unit root in any of them, Augmented Dickey Fuller test was performed. We do reject the null-hypothesis of a unit root for all returns, all of them are stationary since the p-value is less than the critical level of 5% and integrated of order 0.

6 The S&P GSCI (the Goldman Sachs Commodity Index) is the benchmark for investment in commodity markets and a measure of commodity performance over time. It is a tradable index that is readily available to market participants of the Chicago Mercantile Exchange. The index comprises 24 commodities from all commodity sectors and is a measure of commodity returns comparable to the S&P 500. In our case, we only focus on the energy component of the S&P GSCI that contains: Crude Oil, Brent Crude Oil, RBOB Gas, Heating Oil, GasOil and Natural Gas. For all series except natural gas, the returns distributions display negative skewness, which is evidence of non symmetric distribution, meaning, the probability of loss is superior to that of gain. Moreover, the data indicates an excess kurtosis, hence the tails of the distribution contain more observations than a Gaussian distribution. The Ljung Box statistic for autocorrelation indicates that the null hypothesis of no autocorrelation for the returns, and their squared returns can be rejected. There are exceptions concerning the SP-GSCI energy and Brent oil index where only their squared returns are auto-correlated. We also apply tests for ARCH effects by using the ARCH LM test of [START_REF] Engle | Autoregressive conditional heteroskedasticity with estimates of the variance of u.k. inflation[END_REF] [START_REF] Engle | Autoregressive conditional heteroskedasticity with estimates of the variance of u.k. inflation[END_REF]. All series exhibit strong evidence of serial correlation and indicate the presence of ARCH effects in their returns. The examination of the autocorrelation functions of the series, the ACF of squares' returns, shows a similitude with a random walk, which means that prices cannot be predictable, strictly speaking. Their prices behave rather randomly and do not follow a certain trend or pattern, thus they verify the hypothesis of the efficient market (EHM) (see fig 3 in the Annex).

As mentioned in the introductory section, the processes are generated by Geometric Brownian motion:

dp(t) = µ(t)dt + σ(t)dW (t), t ≥ 0 ( 30
)
where W (t) is a standard Wiener process. The main limitation of the generalized Wiener process is that both drift and volatility parameters are constant over time while it is well known that daily returns for instance are heteroscedastic. Ito's process allowed drift and volatility parameters to be time varying.

Empirical studies have shown that a continuous diffusion model fails to explain some characteristics of returns. To overcome this inadequacy, we consider the jump diffusion process

dp(t) = µ(t)dt + σ(t)dW (t) + κ(t)dq(t), t ≥ 0 ( 31
)
where dq(t) is a counting process equal to one when a jump happens in time t, otherwise 0. We must make a distinction between a shift and a random walk type variability. A random walk model involves a gradual random change over time, there is no mean average state, and in the absence of negative feedback, variance increases with time. A jump suggests an abrupt change in relation to the duration of a regime. And that's why we use the GAS model instead of a GARCH model to characterize the marginals, since it prevents and reduces the effect of jumps and temporary changes better than the latter.

The estimation

As stated above, our aim is to analyze the dynamics of the dependence linking conditional volatility over time, between CO 2 emission spot prices and a set of commodities prices: the energy commodities based on their volatilities. However, we do not consider the relationship between CO 2 emissions and the industrial production which is found to be strong, as stated by [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF] equal to 1%, except the constant in the mean and the variance equations, and a small value of standard error. All coefficients are significant. We find that the GAS(1, 1) with a student distribution for the innovations is the best fitted for for out of five variables. The coal is the only variable for which we choose the GAS model with skewed student distribution. Following Harvey and Chakravarty (2008) [START_REF] Harvey | Beta-t-(e)garch[END_REF], we call the GAS(1, 1) model with a t distribution 'Beta-t-GARCH'.

Since |φ 1 | < 1, the processes are stationary and the results suggest that the student distribution of the innovations do take into account the fat tailed character of all the returns (ν is between 2 and 10).

Having estimated the marginal distributions and transformed the standardized residuals into U (0, 1) random variables by the probability integral transform (PIT), the last step is to model the dependence structures between the CO 2 emission price returns and the other returns. Naturally, the estimations are performed by pairs. The SCAR or the time varying stochastic copula is then estimated using the method of EIS. 9 We present the copulas estimates in table tures. One of the questions commonly asked is "which copula should we use?"

We use an information criteria given by the maximization of the log-likelihood value. In table 3, we present only the best-fitted copula model. Further estimation concerning all the copulas considered in each pair are presented in the annex (tables 6-9 in Annex). Before evaluating and analyzing the estimates of the SCAR copula obtained by the ML-EIS technique we remind that the latent process λ t describing the dependence is the following:

λ t = α + βλ t-1 + κε t
to which we apply a function Ψ depending on the choice of the copula. We notice that β varies according to the variable and the copula family. It is also important to mention that the dynamic aspect of the dependence between the variables in each pair is confirmed since κ is different than 0 as stated by Hafner and Manner [START_REF] Hafner | Dynamic stochastic copula models: estimation, inference and applications[END_REF]. Having estimated the copulas parameters, we could then obtain the dependence path over time of each pair of variables. However, before discussing the outcomes of the SCAR model, a brief examination of the correlation matrix of the CO 2 emissions and the commodities prices (tables 4 and 5) show us that there is a significant difference between correlations before and after mid-2011.

On the whole sample, there are positive but low correlations between coal prices and carbon emission prices (0.19) and coal prices and natural gas prices (0.05).

Also, there is a strong negative correlation between Brent oil price and carbon emission price. In this case, the CO 2 emissions are mainly explained by the behavior of the Brent oil and the natural gas. Now if we only consider the second part of the sample after the turning point (mid-2011), the landscape is totally different. The CO 2 emissions prices are mainly correlated to the coal and the Brent oil prices. The correlation with the natural gas is close to zero.

The correlation between the coal and the CO 2 emissions has almost quadrupled (0.77), and the correlation of Brent oil with CO 2 emissions is now positive and decreased from 0.58 to 0.39. The most important impact on the correlation for CO 2 emissions comes from the behavior of the prices of coal. The strong decrease of the coal prices has an impact on the size of the demand. We can see this aspect in figure 4 For the pair CO 2 emissions/SP-energy, the normal copula is the best one based on the Log-likelihood criteria (LogL) value. The value of β (0.9884), indicates a high persistence in the dependence process. For the pair CO 2 emissions/Brent we retain the Rotated Gumbel copula with a high persistence level and a small value for κ. For CO 2 /Natural gas, we notice that the LogL's of Frank and Gumbel copulas are very close (1.5 and 1.3 respectively), thus we should not just discard the Gumbel copula since its given dependence pattern is more informative than the one given by the former copula. For the last pair of variables, that is CO 2 emission prices with coal prices, the Frank copula is the best-fitting model. Let's mention that all the pairs of variables have a highly persistent dependence process. In terms of the log-likelihood value, the Gaussian copula is the most appropriate for the pair: CO 2 emissions with the SP-energy prices. For the CO 2 emissions and Brent oil, the rotated Gumbel is the best fitting model, the Gumbel copula for CO 2 emissions with natural gas and the Frank for the coal and CO 2 emissions. The dependence path between the two variables is not linear and vary over time.

In the beginning of the study, it is positive and is increasing, although not very strong, between 1.15 and 1.12, giving us a coefficient of correlation between 0.11 and 0.14. Nevertheless this shows the positive impact of oil prices on the CO 2 allowances both being in high demand, especially the former one. Also, it is important to mention that when considering this period of time from 2009 until the beginning 2011, the European economy still suffered from the harmful effects of the financial crisis and was still marked by a slowdown in the beginning of the period. We find a positive relation between the two mentioned variables in this stressful period of time, since the prices of fuel consumption in particular the Brent oil started to recover from their lowest level since 2009 contrarily to financial stock market returns and so did the CO 2 emission prices, a fact confirmed by Duc Khuong and al (2013) [START_REF] Alouia | A time-varying copula approach to oil and stock market dependence: The case of transition economies[END_REF] among others. The strongest rise in dependence in the period between mid 2011 and the beginning of 2012 shows the positive impact of the Brent oil prices on the CO 2 allowances and emission prices that reached its highest level. In fact, both prices reached their maximum value in this period of time. However, the cause may differ from the previous period since both the European economy and world wide economy in general suffered from some crisis and instability caused by the wars and revolutions in the Middle East and North Africa especially Libya and Bahrein on the one hand, and the intensified debt crisis in the euro zone on the other hand. In fact, this finding confirms the increase of the correlation between the assets in an unstable and unsteady period. In the remaining period, the dependence decreased considerably. Oil prices remained high although they did not increase and consequently the CO 2 allowances spot prices decreased significantly. This can be attributed to the decrease in oil demand as well as some regulations and policies established by European countries to encourage and expand the use of renewable and clean energy. The dependence structure of CO 2 allowance prices, and the SP-GSCI Energy prices is given in the bottom right of figure 1. We can determine different phases of the time dependence between the pair of variables: prior to February 2011, this relationship is positive, dynamic, although not much, with values between 0.17 and 0.25. And then we have an expansion of the relation of the two variables in 2011, caused by the same factors stated above, instability and crisis, where the correlation attained a max of 0.4 in this period, followed naturally by a decrease until the beginning of 2013 but remaining positive nevertheless, and reaching almost 0.03 as a minimum correlation, to continue rising over the rest of the period of study. By studying the relationship between these two variables, we notice that the main driver of the correlation in the SP-Energy index is indeed the Brent oil (correlation equal to 0.2 compared to 0.04 for natural gas), a fact pointed out by Chevallier [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF].

The dynamics of the dependence between natural gas prices and CO 2 emission prices vary over time and is positive, however little, compared to the relation between the latter one and the Brent oil. We notice the rise of the dependence over the same period of turmoil mentioned above. The dependence path of the Coal/CO 2 emissions presented in the figure 1 emphasizes the dynamic nature of the correlation between these variables. From the beginning of the period of study to mid-2011 the correlation was relatively low (it didn't exceed 0.3) compared to the after mid-2011 period, where it increased considerably and reached the value of 0.75. These results coincide with the results in table 4 and table 5 where we studied the correlation of the coal/CO 2 emissions on the whole sample, then by focusing only on the period from May 2011 to January 2014.

Since people substituted oil and natural gas by coal, the demand and use for coal increased and so did the prices and the contracts to emit the CO 2 . This is why the dependence between the Coal and CO 2 emissions is the highest in the second period. So Brent oil, natural gas and coal contribute to the evolution of the CO 2 emission prices, although Brent oil and coal has the most part of it.

Conclusion

The market dynamics of carbon dioxide emission prices have important policy implications. In this paper we study the relationship or the dependence between CO 2 prices and the energy prices through the dynamic copula case, the SCAR model. The returns are modeled by a GAS(1,1) model and can deal with the jumps and the temporary changes better than a GARCH type model.

By doing so, we are able to take into account the non linearity of the data characterized by excess kurtosis, negative skewness and fat tails. We do have a significant impact of energy prices on the CO 2 emission prices, the dependence does vary over time and is not constant. It rises considerably when facing a period of turmoils, instability and wars, since the rise of energy prices encourage and push firms to substitute it by other type of energy and resources which imply a decrease in its demand and so the same goes for the CO 2 allowances, and that explains its high prices. Also we find that Brent oil as well as coal are the major factors driving the dependence between the aggregated energy prices and the CO 2 emission prices. Our results highlight that energy price volatility has a significant impact on CO 2 allowance prices. However, one should not disregard the fact that other factors should be taken into account in this matter. In fact, dioxide carbon prices volatility, can be affected by R & D in clean energy technologies and renewable energy sources. Also other measures could be introduced to reduce CO 2 emissions, like encouraging the development and use of alternative activities less intensive producers of the greenhouse.

Examples of Copulas

Below we give some types of copulas functions along with the expressions of their density c and parameter θ. We also specify the exact and appropriate transformations Ψ we use for the SCAR specification depending on our choice of copulas.

• Frank Copula

The density of Frank copula is given by c(u, v; θ) = exp((1 + u + v)θ)(exp(θ) -1)θ {exp(θ) + exp((u + v)θ) -exp(θ + uθ) -exp(θ + vθ)} 2 [START_REF] Davis | Observation-driven models for poisson counts[END_REF] Kendalls τ can be derived through the following expression depending on the parameter θ: τ = 1 -4(1-D1)(θ)

θ
where D is the Debye function

D k (x) = k x k x 0
t k e t -1 dt. Frank copula belongs to the family of Archimedean copulas and it does not exhibit tail dependence since it displays a symmetric dependence. Finally, this copula allows for both positive and negative dependence.

• Clayton Copula

The density of the Clayton copula is: c(u, v; θ) = u (-1-θ) v -1-θ (u -θ + v -theta -1) -2-1/θ (1 + θ) [START_REF] Harvey | Egarch models with fat tails, skewness and leverage[END_REF] θ ∈ (o, +∞). The functional form of P si is given by Ψ(x) = exp(x)

meaning that the dependence or the copula parameter has a log-normal distribution. The expressions of the tau kendall, the upper tail dependence and the lower tail dependence are respectively:τ = θ θ+2 , λ L = 2 -1/θ , and λ U = 0 for all θ. The Clayton copula is also Archimedean, it displays asymmetry and it only allows positive dependence.

• Gumbel Copula

The density of the Gumbel copula is given by: c(u, v; θ) = {log(u)log(v)} • Gaussian Copula Defining x = Φ -1 (u) and y = Φ -1 (v) where Φ is the CDF of a standard normal random variable, the density of the Gaussian copula is the following:

c(u, v; θ) = 1 √ 1 -θ 2 exp( 2θxy -x 2 -y 2 2(1 -θ 2 ) + x 2 + y 2 2 ) (35)
with θ ∈ (-1, 1).We use the inverse Fisher transform to obtain the function P si: Ψ(x) = (exp(2x)-1)/(exp(2x)+1).The expression of the Kendall tau is τ = 2 π arcsin(θ). The Gaussian/normal copula has no tail dependence.

• Survival (Rotated) Copulas

For a copula with a density c and a distribution C, the distribution of its survival copula C for a couple of variables(u, v) as well as its density c are given respectively by: C=C θ (1-u, 1-v)+u+v -1 and c=c θ (1-u, 1-v).It actually represents the original copula c but with a 180 • rotaion. The survival copula can be associated with the Gumbel and the Clayton family.

  uation becomes more simple and straightforward; also there is the possibility of extensions to asymmetric, long memory, and other dynamics. Many examples of models are also observation driven, such as the generalized autoregressive conditional heteroskedasticity (GARCH) model[START_REF] Engle | Autoregressive conditional heteroskedasticity with estimates of the variance of u.k. inflation[END_REF],[START_REF] Bollerslev | Generalized autoregressive conditional heteroskedasticity[END_REF] and[START_REF] Engle | Modelling the persistence of conditional variances[END_REF])[START_REF] Engle | Autoregressive conditional heteroskedasticity with estimates of the variance of u.k. inflation[END_REF][28][START_REF] Engle | Modelling the persistence of conditional variances[END_REF], the autoregressive conditional duration (ACD) model and the autoregressive conditional intensity (ACI) model by Engle and Russell (1998)[START_REF] Engle | Autoregressive conditional duration: A new model for irregularly spaced transaction data[END_REF], the dynamic conditional correlation (DCC) model(Engle, 2002)[START_REF] Engle | Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models[END_REF], the Poisson count model by[START_REF] Davis | Observation-driven models for poisson counts[END_REF][START_REF] Davis | Observation-driven models for poisson counts[END_REF]. We refer to the observation driven model based on the score function as Generalized auto-regression score model. The mechanism that drives the dynamics of the time-varying parameters is introduced by the score function. The GAS models are more general and encompass all the listed models above. It also overcomes some limitations in the GARCH one. First, it is ro-bust to jumps and outliers and can model better heavy-tailed financial returns and distributions compared to other GARCH models (Harvey and Sucarrat (2014))[START_REF] Harvey | Egarch models with fat tails, skewness and leverage[END_REF]. Second, GARCH models are unable to capture the leverage effect since in the conditional variance they ignore the signs of the lagged residuals, unlike the GAS model that accomodate very well the most important characteristics of time-varying financial volatility: leverage, conditional fat-tailedness, conditional skewness and the decomposition of volatility into a short-term and a long-term component. Third, the asymptotic properties are much easier to obtain than with GARCH models. Finally, since the conditional score drives the dynamics of the model, GAS models acquire some attractive theoretical properties. In particular, a simple transformation of the score would allow for another type of model. If we consider the following model:

  ferent other energy commodities, and we want to examine their relationship or dependence in their conditional volatility over time. Let's note that CO 2 emission prices are not only driven by energy prices but also by seasonalities, weather related factors as well as industrial production. In this paper we do not study the relationship between the latter (industrial production) and CO 2 allowances, because many papers and work have confirmed the link between the two.[START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF][START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF], among others, studied the effect of industrial production on carbon dioxide emission prices between 2005 and 2007. They only considered sectors covered by the EU ETS.

2

  Carbon emission prices correspond to the price of these permits emissions. It is traded in the The European Union Emissions Trading System (EU ETS) which was the first large greenhouse gas emissions trading scheme in the world, and remains the biggest. It Operates in the 28 EU countries and the three EEA-EFTA states (Iceland, Liechtenstein and Norway)

2

  index is the benchmark price reference for coal imported into Northwest Europe. The major coal importing ports in this region are Antwerp/Rotterdam/Amsterdam (ARA) and the term CIF (Cost,Insurance and Freight) indicates that insurance and all other charges up to the named port of destination are already included in the price quoted by the seller.(SP-energy)6 All price series are quoted in US dollars and were extracted from Datastream International in daily frequency, motivated by the fact that these indexes are products in financial markets and are traded each day except on nonworking days (5 days a week). The sample begins in 16/01/2009 to 23/01/2014 giving us 1309 observations. It corresponds to the second phase of trading with carbon allowances parallel to the Kyoto protocol.

Figure 1 :Figure 1

 11 Figure 1: Time dependence path of the pairs of variables

θ- 1 (

 1 -log(u)) θ + (-log(v)) θ 1/θ + θ -1 [(-log(u)) θ + (-log(v)) θ ] 2-1/θ uv × exp (log(u)) θ + (-log(v)) θ + (-log(v)) θ 1/θ(34)with θ ∈ (1, +∞) and the transformation Ψ = exp(x) + 1. The Gumbel copula belongs to the Archimedean family. τ , λ U and λ L = 0 can be expressed as the following: τ = 1 -1 θ , λ U = 2 -2 1/θ and λ L = 0. And it only allows for positive dependence.

Figure 3 :

 3 Figure 3: Plots of the Returns and some stylised facts

7

  Using returns or the first difference of log-asset price process, removes mean non-

		CO 2 emissions	Gas	Coal	Oil	SP-energy
	Mean	-0.0006	-2.7525e-05 4.2118e-06 0.0006	0.00022
	SD	0.0341	0.0340	0.011946 0.0178	0.0168
	Min	-0.4465	-0.2408	-0.12895 -0.1197	-0.088
	Max	0.2106	0.2619	0.083766 0.0946	0.0773
	Kurtosis	28.6329	12.5663	20.237	6.3991	6.3462
	Skewness	-1.2725	0.5763	-0.11873 -0.0674	-0.2886
	ADF stat	-10.5679	-10.8643	-17.79 -11.6788 -11.4241
	ADF p-val	0.001	0.001	0.01	00.01	0.001
	Jarque Bera stat	36162.25	5059.982	12250 630.6941 628.431
	Jarque Bera p-val	2.2e-16	2.2e-16	0	2.2e-16	2.2e-16
	Q(20) stat	75.2516	103.9918	56.2686 24.0575	20.326
	Q(20) p-val	2.473e-08	2.415e-13 0.0000265 0.2399	0.4377
	Q(20) 2 stat	115.0913	544.7371	86.5206 433.0139 675.891
	Q(20) 2 p-val	2.331e-15	2.2e-16	0	2.2e-16	2.2e-16
	Arch LM stat	79.8655	263.5155	3.6239 180.6845 246.5221
	Arch LM p-val	4.138e-09	2.2e-16	0.0001	2.2e-16	2.2e-16
	stationarity and facilitates measuring all variables in a comparable metric, thus enabling
	evaluation of analytic relationships amongst two or more variables despite originating from
	price series of unequal values. Third, exhibits the main statistical features and characteristics
	of financial data like stationarity, conditional volatility, autocorrelation of returns close to
	white noise and strong persistence in the squared returns, fat tailed distributions (see table1
	for further details).

Table 1 :

 1 Descriptive statistics of the return series

  [START_REF] Alberolaa | Price drivers and structural breaks in european carbon prices[END_REF],Choi et al(2010). . . ). For that, we consider four pairs of bivariate time series, the CO 2 allowances price being a variable in each of these pair. Those pairs are 8 : CO 2 allowance/Natural gas, CO 2 allowances /Brent oil, CO 2 allowances/coal, CO 2 allowances/SP energy. First, we specify the marginal distributions, second the one of the copula. We estimate separately for each variable the GAS model, introduced by Creal, Koopman, and Lucas (2013)[START_REF] Creal | Generalized autoregressive score models with applications[END_REF], best fitted for heavy tailed and skewed distributions with Student's t or Skewed Student innovations to account for marginal time dependence of each variable.

A summary of the marginal estimates is given in the table 2. The parameter estimates are positive, significantly different from zero with the level of error

Table 2 :

 2 Parameters estimates of the marginal distributions

	3, and we are interested in investigating the dynamics of the dependence struc-

Table 3 :

 3 Parameters estimates of the best fitted SCAR copula

Table 4 :

 4 in Annex, where the level of the trend of the coal price and the carbon emissions price are represented. The trend components are obtained by a classical decomposition of the process in a state space representation. correlations between the returns for the whole sample

		Lcoal	LCO 2	Lbrent	Lnatgas	LSP E
	Lcoal	1	0.20	0.58	0.03	0.48
	LCO 2	0.20	1	-0.45	0.31	-0.37
	Lbrent	0.58	-0.45	1	-0.32	0.91
	Lnatgas	0.042	0.31	-0.32	1	-0.25
	LSP -E	0.48	-0.37	0.91	-0.25	1
		Lcoal	LCO 2	Lbrent	Lnatgas	LSP-E
	Lcoal	1	0.77	0.39	-0.05	0.09
	LCO 2	0.77	1	0.35	0.0002	0.015
	Lbrent	0.39	0.35	1	-0.19	0.82
	Lnatgas	-0.05	0.0002	-0.19	1	-0.087
	LSP-E	0.09	0.015	0.82	-0.08	1

Table 5 :

 5 correlations between the returns from May 2011 to the end of period

All series are taken in returns

To reduce the Monte Carlo variation, N , the number of simulated trajectories in the EIS sampler, can take the value of 200.

ACF-dlspe-sq

Table 7: Parameters estimates of the SCAR copula: CO 2 and the Natural Gas

Table 9: Parameters estimates of the SCAR copula: CO 2 and SP energy


Harriet Corvol

Kristin E Thompson

Olivier Tabary

Philippe Le Rouzic

Guillot Loïc
email: loic.guillot@inserm.fr

L Guillot

Translating the Genetics of Cystic fibrosis to Personalized Medicine

Cystic fibrosis (CF) is the most common life-threatening recessive genetic disease in the Caucasian population. This multi-organ disease is caused by mutations in the gene encoding the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) protein, a chloride channel recognized as regulating several apical ion channels. The gene mutations result either in the lack of the protein at the apical surface, or in an improperly functioning protein.

Morbidity and mortality due to the mutation of CFTR are mainly attributable to lung disease resulting from chronic infection/inflammation. Since its discovery as the causative gene in 1989, much progress has been achieved not only in clinical genetics but also in basic science studies. Recently, combinations of these efforts have been successfully translated into development and availability for patients of new therapies targeting specific CFTR mutations to correct the CFTR at the protein level. Current technologies such as Next Gene Sequencing (NGS) and novel genomic editing tools may offer new strategies to identify new CFTR variants and modifier genes, and to correct CFTR to pursue personalized medicine, which is already developed in some patient subsets. Personalized medicine, or P4 medicine for Personalized, ''Predictive'', ''Preventive'' and ''Participatory'', is currently booming for

Introduction

Cystic fibrosis (CF) is the most common life-threatening recessive genetic disease in the Caucasian population, affecting approximately 70,000 individuals worldwide. This genetic disorder, caused by a mutation of the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene, is mostly characterized by recurrent lower respiratory infections, exocrine pancreatic insufficiency (85% of patients), and increased electrolyte concentration in sweat. A subset of patients also presents with a variety of symptoms which less commonly result in a CF diagnosis, such as meconium ileus, liver disease and pancreatitis [START_REF] Robinson | Cystic fibrosis[END_REF]. Additional CF complications may also be observed in these patients, such as CF related diabetes. When CF was initially described in the 1930's, the patients only survived for a few months or years.

While still associated with fatality, the median predicted life expectancy in CF has dramatically increased to around 40 years of age. However, analysis of demographic data of the European CF population highlights disease outcome disparities within Europe [START_REF] Mccormick | Comparative demographics of the European cystic fibrosis population: a cross-sectional database analysis[END_REF]. This improvement of CF survival within specific European populations is multifactorial, including better diagnosis, phenotype characterization and treatment (e.g. antibiotics). To extend the survival and quality of life for CF patients, specific therapies targeting CFTR have been developed highlighting the translational research pipeline from basic science to clinical trials [START_REF] Chignard | Basic research funding by philanthropic organizations: a case in point[END_REF]. These specific therapies have directly affected the current progress in cystic fibrosis of the use of P4 medicine (for ''Personalized'', ''Predictive'', ''Preventive'' and ''Participatory''). Medicine is per se ''Personalized'', i.e. individualized. Indeed, the best healthcare for each patient is chosen depending on his or her pathology and overall environment (allergic, psychological, socioeconomic). The concept of personalized medicine was developed in the 1990's by the pharmaceutical company Roche ® , in regards to the fact

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

that one specific drug can induce various reactions in different individuals; and, for one individual, some drugs are highly effective whereas others are ineffective. Thus, anticipating which drug will be effective in one patient versus another is the basis for personalized medicine. This concept has been extended to the ''4P medicine'' which takes into account the patient as a whole (4) with:

-''Personalized medicine'', founded on how the individual genetic background could guide each patient's healthcare.

-''Predictive medicine", that allows the physician to assess the risk for one person to develop a disease or a disease's complication, leading to consider an individualized strategy to avoid disease appearance and/or progression.

-''Preventive medicine", that promotes a proactive approach, allowing to switch from a curative approach to a method based on the overall state of health and well-being.

-''Participative medicine", that allows the patient to take informed decisions and to be responsible for his own health. This concept, well known and described in cancer care, is relevant to almost all other diseases, and particularly for genetic diseases, for which large scale genomics studies have been performed, such as CF [START_REF] Bauer | Genomics and personalised whole-of-life healthcare[END_REF].

CFTR

CFTR (OMIM: 602421) was identified in 1989 as the abnormal gene for CF [START_REF] Riordan | Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA[END_REF][START_REF] Rommens | Identification of the cystic fibrosis gene: chromosome walking and jumping[END_REF][START_REF] Kerem | Identification of the cystic fibrosis gene: genetic analysis[END_REF]. It is located on chromosome 7 (7q31.2), composed of 27 exons, and encodes for a protein, an ion channel that belongs to the ATP-binding cassette (ABC) transporter family of proteins.

Consistent with the ABC transporters, the CFTR structure has 2 transmembrane domains (TMDs), and 2 nucleotide-binding domains (NBDs), but uniquely possesses a regulatory [START_REF] Cant | CFTR structure and cystic fibrosis[END_REF]. CFTR has several functions acting primarily as a chloride channel and regulating several other apical ion channels.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT domain (R)
To date, almost 2,000 mutations have been identified and collected from the international CF genetics research community (http://www.genet.sickkids.on.ca/app; http://www.cftr2.org) [START_REF] Sosnay | Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene[END_REF]. CFTR mutations have been found mostly in European-derived populations, and, with much less frequency, in African and East Asian populations [START_REF] Castellani | Consensus on the use and interpretation of cystic fibrosis mutation analysis in clinical practice[END_REF].

Missense mutations are the most prevalent (40%), compared to frame shift (16%), Splicing (12%) and non-sense mutations (8%) [START_REF] Castellani | Consensus on the use and interpretation of cystic fibrosis mutation analysis in clinical practice[END_REF][START_REF] Ferec | Genetics and modifier genes, atypical and rare forms[END_REF]. The most common mutation (70% of patients) is a three base deletion (c.1521_1523delCTT; Refseq transcript ID: NM_000492.3) that induces a deletion of a phenylalanine in position 508 (p.phe508del (p.F508del); Refseq protein ID NP_000483.3).

CFTR mutations are classified into five classes (sometimes six), according to their resulting damaging effect on the protein (reviewed in ( 13)) (Table 1). These classes are not exclusive, given that one mutation could have both a defective channel regulation ("class III") and a defective channel conduction ("class IV"), such as the c.350G>A/p.Arg117His (formerly R117H) mutation. New high throughput sequencing approaches will generate many genetic variations both in CF patients and in the general population. For CF and other monogenic diseases, annotating variants (i.e. assessing the disease liability of these rare variants) is a considerable challenge for the future [START_REF] Cutting | Cystic fibrosis genetics: from molecular understanding to clinical application[END_REF]. For CFTR, the disease liability has been validated and established for only a few dozen variants, in comparison to the almost 2,000 reported. In this context, a phenotypic-driven approach was used in the CFTR2 project to assess the disease liability of CFTR variants [START_REF] Sosnay | Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene[END_REF]. This project has succeeded thanks to the cooperation of geneticists, clinicians and basic scientists. This strategy includes phenotypic and genomic data analyses, as well as biochemical measurements (Mean Sweat Chloride) and molecular biology studies (CFTR RNA and protein expression). Starting with 39,696 subjects, 159 variants were selected (seen at ≥0.01% in CFTR2). Finally, 127 variants have been assigned as "CF causing", 20 as "indeterminate", and 12 as "non-CF causing". Annotating these CFTR variants will benefit both clinical diagnosis and personalized therapies. In fact, the disease liability of the 23 mutations included in the AMCG testing panel [START_REF] Grody | Laboratory standards and guidelines for population-based cystic fibrosis carrier screening[END_REF][START_REF] Watson | Cystic fibrosis population carrier screening: 2004 revision of American College of Medical Genetics mutation panel[END_REF] (table 1) has been established for many years. However, the 85% sensitivity of this North-American panel left resulted in almost 30% of newborns with insufficient evidence to confirm the diagnosis of CF.

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Indeed, sensitivity of the mutation tests varies between populations. In non-Caucasian groups

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and Hispanic, the sensitivity is lower compared to Caucasians [START_REF] Palomaki | Clinical sensitivity of prenatal screening for cystic fibrosis via CFTR carrier testing in a United States panethnic population[END_REF]. In Europe, extensive heterogeneity in the distribution of CFTR is seen across the different European countries.

Thus, best practices guidelines for CF molecular genetic diagnosis have been established, especially to take into account the regional disparities. Inclusion of the 127 disease-causing variants from CFTR2 increased the sensitivity for detection of CF alleles in white European individuals up to 95 % [START_REF] Sosnay | Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene[END_REF]. The non-Caucasian inventory of CFTR variants will be achieved due to the future CFTR2 recruitment that will include affected individuals from East Asia, Middle East, Africa and South-America [START_REF] Cutting | Cystic fibrosis genetics: from molecular understanding to clinical application[END_REF].

The biological consequences (direct or indirect) of CFTR mutations are numerous and have been extensively studied, particularly for the p.F508del mutation. Not all the relevant publications can be included here. Briefly, the defective ion transport due to CFTR mutations results in reduced apical airway surface liquid (ASL), which leads to impaired mucociliary clearance that progressively induces obstruction of the airways with thickened mucus. This in turn creates a favorable environment for bacterial contamination and colonization.

Interestingly, recent observations using a piglet model of CF suggest that mucociliary transport is a primary defect in CF [START_REF] Hoegger | Cystic fibrosis. Impaired mucus detachment disrupts mucociliary transport in a piglet model of cystic fibrosis[END_REF]. Patients with CF have an impaired host immune response especially against Pseudomonas aeruginosa, which is characterized by an exaggerated and ineffective airway inflammatory response. There is evidence suggesting that CFTR dysfunction affects innate immunity pathways, notably those associated with Toll Like

Receptors (TLRs) [START_REF] Sallenave | Phagocytic and signaling innate immune receptors: are they dysregulated in cystic fibrosis in the fight against Pseudomonas aeruginosa?[END_REF] and that the initial predisposition to infection in infants with CF may represent a primary defect in local mucosal immunity [START_REF] Cohen | Cystic fibrosis: a mucosal immunodeficiency syndrome[END_REF]. The temporal and causal interplay between infection and inflammation (chicken or egg) is still in debate. However, it has recently been shown that cftr -/-newborn piglets, a model which well reflects human CF lung disease, have an impaired host response without inflammation, suggesting that bacterial infection may initiate inflammation [START_REF] Stoltz | Cystic fibrosis pigs develop lung disease and exhibit defective bacterial eradication at birth[END_REF].

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Lung modifiers

Due to the intensive work on CFTR, the diversity of the clinical phenotype of lung disease among patients harboring the same genotype (e.g p.F508del homozygous) suggests that other environmental and genetic factors influence the clinical phenotype. Interestingly, discordant phenotypes are observed in CF siblings carrying the same CFTR mutations and sharing a similar environment, thusly supporting the need to search for CF modifier genes [START_REF] Bronsveld | Clinical presentation of exclusive cystic fibrosis lung disease[END_REF][START_REF] Bronsveld | Chloride conductance and genetic background modulate the cystic fibrosis phenotype of Delta F508 homozygous twins and siblings[END_REF][START_REF] Bronsveld | Residual chloride secretion in intestinal tissue of deltaF508 homozygous twins and siblings with cystic fibrosis. The European CF Twin and Sibling Study Consortium[END_REF][START_REF] Mekus | Categories of deltaF508 homozygous cystic fibrosis twin and sibling pairs with distinct phenotypic characteristics[END_REF][START_REF] Mekus | Cystic-fibrosis-like disease unrelated to the cystic fibrosis transmembrane conductance regulator[END_REF][START_REF] Mekus | Genes in the vicinity of CFTR modulate the cystic fibrosis phenotype in highly concordant or discordant F508del homozygous sib pairs[END_REF][START_REF] Cutting | Modifier genes in Mendelian disorders: the example of cystic fibrosis[END_REF]. To date the most studied respiratory phenotypes are lung function (FEV 1 ) decline and the susceptibility to Pseudomonas aeruginosa infection. This pathogen is known to be associated with a more rapid decline of the lung function and with increased mortality.

Various strategies have been employed in order to discover these modifiers such as Genome Wide Association Studies (GWAS), candidate gene approaches, and, more recently, exome sequencing. These different strategies have identified many potential modifiers (reviewed in [START_REF] Guillot | Lung disease modifier genes in cystic fibrosis[END_REF][START_REF] Weiler | Genetic influences on cystic fibrosis lung disease severity[END_REF]). Briefly, the genes that have been mostly studied (replicated, large cohorts of patients) are the transforming growth factor beta 1 (TGFB1), mannose binding lectin 2 (MBL2), and the ancestral haplotype (AH) 8.1. TGFβ is a multifunctional protein involved in the regulation and the proliferation of numerous cell types. The most studied variants are in the promoter region (-509) and in codon 10 (2500 patients). Although some discrepancies exist between the various studies, an association has been observed with the variants inducing an increased expression of the protein (-509TT and codon 10CC) and the severity of the lung phenotype in CF patients [START_REF] Corvol | Genetic variations in inflammatory mediators influence lung disease progression in cystic fibrosis[END_REF][START_REF] Drumm | Genetic modifiers of lung disease in cystic fibrosis[END_REF]. MBL is an innate protein produced by the liver. During the acute inflammatory process, it accumulates in the lung and has a pivotal role in infectious diseases, specifically due to its binding properties to CF pathogens such as P. aeruginosa, S. needs and an increased mortality [START_REF] Chalmers | Impact of mannose-binding lectin insufficiency on the course of cystic fibrosis: A review and meta-analysis[END_REF]. The haplotype 8.1AH is called ancestral as it is highly conserved. It is frequent in Caucasians (25% in the general population) and is localized on the short arm of chromosome 6, in the coding region of the MHC (major histocompatibility complex). It includes major genes of the inflammatory response such as lymphotoxin a,

Tumor Necrosis Factor (TNF), Heat Shock Protein A1B (HSPA1B) and RAGE (Receptor for Advanced Glycation Endproducts) [START_REF] Beucher | AGER -429T/C is associated with an increased lung disease severity in cystic fibrosis[END_REF]. This haplotype has been shown to be associated with the severity of the lung disease in 404 CF European patients; the 8.1AH carriers having a faster FEV 1 decline [START_REF] Corvol | Ancestral haplotype 8.1 and lung disease severity in European cystic fibrosis patients[END_REF]. Finally, a GWAS performed in a large CF cohort, of more than 3000 patients [START_REF] Wright | Genome-wide association and linkage identify modifier loci of lung disease severity in cystic fibrosis at 11p13 and 20q13.2[END_REF], has identified 7 regions associated with the respiratory phenotype near the genes APIP/EHF, AGTR2, HLA-DRA, EEA1, SLC8A3, AHRR and CDH8. The region around the EHF and APIP genes, involved in apoptosis, was highly significant. Moreover, a linkage analysis in related patients showed another interesting region on chromosome 20q13.2, that remains to be fully studied.

Other phenotypes have been studied inorder to discover modifier genes and have been extensively reviewed elsewhere [START_REF] Guillot | Lung disease modifier genes in cystic fibrosis[END_REF][START_REF] Weiler | Genetic influences on cystic fibrosis lung disease severity[END_REF].

Identification and characterization of these modifiers would allow a ''predictive'' and ''preventive'' medicine. Predictive medicine underlines the concept of the physician possessing the knowledge of the patient's whole genome in order to predict the onset of complications before they start. Preventive medicine may then be applied, and therapies may be provided prior to the onset of these complications. This would be a new area for the patients care, "proactive" rather than the current "reactive" care.

Cystic fibrosis therapy

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New personalized therapies are under development, targeting either the dysfunctional gene or protein. This new era allows hoping for curative strategies in a very next future, whereas current treatments mostly focus on end-stage disease of the affected organs.

Gene therapy

Gene therapy would be the hoped for gold standard treatment for CF patients.

Compared to CF protein therapy, gene therapy is expected to be broadly prescribable to CF patients, without restriction to a specific class of mutations. However, this strategy faces important challenges. The concept of gene therapy in CF consists of adding a correct sequence of CFTR in a way that it is incorporated into the patients' cells and is able to circumvent the CFTR mutation. An important step in creating this correct version of the gene is choosing a transporter. Two types of vectors are currently available, either synthetic or viral. The viruses studied for gene transfer are adenovirus, adeno associated virus (AAV), lentivirus and retrovirus. Inconveniently, these vectors induced an excessive inflammatory response or showed poor efficacy in CF patients [START_REF] Griesenbach | Moving forward: cystic fibrosis gene therapy[END_REF]. Thus, synthetic vectors that do not or weakly induce immunogenicity would be an interesting alternative. Currently, a phase III trial is ongoing in the UK with a synthetic lipidic vector. It will include 130 patients older than 12 years old, who will be given the normal gene by inhalation once a month for one year [START_REF] Alton | A randomised, doubleblind, placebo-controlled phase IIB clinical trial of repeated application of gene therapy in patients with cystic fibrosis[END_REF].

Effects will be evaluated by recording the respiratory symptoms, the lung function (FEV 1 ), blood markers (inflammation and immunity) and sputum characteristics.

It is important to highlight that so far gene therapy agents have been delivered only to the lung, i.e. by inhalation, and would have no benefit for other organs. Moreover, another challenging issue is access to the site of delivery, as the thick respiratory mucus may prevent a good bioavailability. Chronic administration may also be problematic even with a low immunogenic system.

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New genome editing tools including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and RNA guide engineered nucleases derived from the bacterial clustered regulatory interspaced short palindromic repeat (CRISPR)-cas (CRISPR-associated) system associated with stem cell therapy offer attractive alternatives.

Briefly, these nucleases cleave DNA at a specific site of interest and allow genome modifications of the gene [START_REF] Kim | A guide to genome engineering with programmable nucleases[END_REF]. Compared to TALENs and ZNFs, the CRISPR/Cas9 system which requires short programmable gRNA for DNA targeting is cheap and easy to design and produce. However much work remains to be done, in particular enhancing the specificity to avoid deleterious off target effects and to further develop the delivery methods [START_REF] Zhang | CRISPR/Cas9 for genome editing: progress, implications and challenges[END_REF]. In primary adult stem cells, intestinal cell organoids of CF patients, the CRISPR/Cas9 system was used to restore the CFTR locus and successfully restore CFTR function (organoid swelling) [START_REF] Schwank | Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients[END_REF]. Using an iPS (induced pluripotent stem cell) cell line homozygous for the p.F508del mutation, candidate TALENs were generated to correct the CFTR defect.

Generation of mature airway epithelia with a functional CFTR from human embryonic stem cells have been recently realized [START_REF] Wong | Directed differentiation of human pluripotent stem cells into mature airway epithelia expressing functional CFTR protein[END_REF]. Also, in this study, CF-induced pluripotent stem cells (iPSCs)-derived from epithelial cells treated by the CFTR corrector compound C18, showed an increased amount of CFTR in the plasma membrane. This is promising for the future of a "personalized" regenerative medicine. In this context, iPSC-derived epithelial cells may be also used to test therapeutics in a patient-specific manner, taking into account the whole genome of a patient, especially the CFTR allelic heterogeneity and the modifier genes.

Protein personalized Therapy

Protein therapies are aimed at correcting the dysfunction of CFTR. As mentioned previously, many CFTR mutations alter the structure/function of the protein. In this context, various therapies have been developed targeting specific mutation classes, in order to

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personalize the treatment. Here, the different classes will be explained and the current strategies to correct the mutations within those classes will be discussed.

Class I mutations are nonsense, frameshift and splice mutations resulting in the absence or little expression of CFTR (Figure 1.). Nonsense mutations induce a premature termination codon (PTC) in the mRNA, which results in a premature stop of translation, and the absence of the protein. To correct this type of mutation, molecules that would promote readthrough of the PTCs and, thus, allow the production of functional CFTR, are needed. The first studies have tested antibiotics of the aminoglycoside family, as gentamycin [START_REF] Du | Aminoglycoside suppression of a premature stop mutation in a Cftr-/-mouse carrying a human CFTR-G542X transgene[END_REF][START_REF] Sermet-Gaudelus | In vitro prediction of stop-codon suppression by intravenous gentamicin in patients with cystic fibrosis: a pilot study[END_REF]. The several clinical trials with gentamycin have indeed shown benefits in the CF patients, with a normalization of the chloride transport in the nasal epithelium [START_REF] Clancy | Evidence that systemic gentamicin suppresses premature stop mutations in patients with cystic fibrosis[END_REF][START_REF] Wilschanski | A pilot study of the effect of gentamicin on nasal potential difference measurements in cystic fibrosis patients carrying stop mutations[END_REF][START_REF] Wilschanski | Gentamicin-induced correction of CFTR function in patients with cystic fibrosis and CFTR stop mutations[END_REF]. However, due to adverse drug-effects of long-term use of aminosides (nephrotoxicity and ototoxicity), a molecule without antibiotic properties was identified and developed by PTC Therapeutics ® known as PTC124 or Ataluren [START_REF] Du | PTC124 is an orally bioavailable compound that promotes suppression of the human CFTR-G542X nonsense allele in a CF mouse model[END_REF][START_REF] Hirawat | Safety, tolerability, and pharmacokinetics of PTC124, a nonaminoglycoside nonsense mutation suppressor, following single-and multiple-dose administration to healthy male and female adult volunteers[END_REF]. Clinical trials with this molecule have shown better transport of chloride in the nasal epithelium, but no real clinical benefit [START_REF] Kerem | Effectiveness of PTC124 treatment of cystic fibrosis caused by nonsense mutations: a prospective phase II trial[END_REF][START_REF] Sermet-Gaudelus | Ataluren (PTC124) induces cystic fibrosis transmembrane conductance regulator protein expression and activity in children with nonsense mutation cystic fibrosis[END_REF][START_REF] Wilschanski | Chronic ataluren (PTC124) treatment of nonsense mutation cystic fibrosis[END_REF]. Additionally, the efficiency is variable among patients, mostly due to the causal CFTR mutation. In fact, patients with the p.Trp1282X (p.W1282X) were better responders than those carrying other class I mutations [START_REF] Wilschanski | Chronic ataluren (PTC124) treatment of nonsense mutation cystic fibrosis[END_REF]. In the last international phase III clinical trial, 238 patients older than 6 years of age were enrolled. Patients received Ataluren or placebo for 48 weeks [START_REF] Kerem | Ataluren for the treatment of nonsense-mutation cystic fibrosis: a randomised, double-blind, placebo-controlled phase 3 trial[END_REF]. No significant respiratory benefits were observed for the whole cohort, either for the evolution of the percent-predicted FEV 1 , or for the numbers of exacerbations. However, in the subgroup of patients not chronically treated with inhaled aminosides, an increase of the percent-predicted FEV 1 (+5,7%), and a reduction in the number of exacerbations were observed. In the Ataluren group, a significant increase of the creatinine was detected in comparison to patients treated with the placebo, without other adverse effects. The clinical benefit seems modest, and will be important to confirm in larger cohorts before approving this drug as a standard of care. Thus,

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an additional phase III international trial is underway, that excludes patients receiving chronic inhaled aminoglycosides (ClinicalTrials.gov, NCT02139306).

In class III mutations, CFTR is expressed at the cell surface but is non-functional. To correct this class of mutations, work is ongoing to identify molecules able to restore the CFTR chloride transport function at least partially, known as ''potentiators.'' In this field, Vertex ® Laboratories were pioneers. Highthrough-put screening methodology investigating more than 200,000 molecules identified a potentiator named Vertex 770 (VX-770, Ivacaftor). VX-770 was indeed able to potentiate the mutated CFTR protein, to increase the open probability of the chloride channel, to decrease the excessive absorption of sodium, and to restore the ciliary beat. This was initially demonstrated in the CFTR p.Gly551Asp (p.G551D) mutation, which is the most frequent class III mutation, occuring in 2% (heterozygous) of CF patients worldwide [START_REF] Van Goor | Rescue of CF airway epithelial cell function in vitro by a CFTR potentiator, VX-770[END_REF]. The clinical trial leading to commercialization was a phase III study (Strive ® study) including 84 patients treated, against 83 with placebo, older than 12 years old and carrying at least one p.G551D CFTR allele [START_REF] Ramsey | A CFTR potentiator in patients with cystic fibrosis and the G551D mutation[END_REF]. Significant clinical benefits have been shown both for respiratory status (10 % of increase for the percent-predicted FEV 1 with a decrease of the number of exacerbations), and for the general status (gain of weight, quality of life). A significant decrease in the sweat chloride levels was also demonstrated. The extended clinical study (Envision ® , children between 6-11 years old) showed similar improvements [START_REF] Davies | Efficacy and safety of ivacaftor in patients aged 6 to 11 years with cystic fibrosis with a G551D mutation[END_REF] Although the efficacy of Ivacaftor has been proven, the question of the costeffectiveness, i.e. affordability compared to long-term benefit of this personalized treatment, for life, must be taken into account and is debatable [START_REF] Balfour-Lynn | Personalised medicine in cystic fibrosis is unaffordable[END_REF][START_REF] Bilton | Personalised medicine in cystic fibrosis must be made affordable[END_REF]. This drug is indeed highly expensive (around 311,000 US $/year) and the actual knowledge of its efficacy would support a long-term (all life-long) prescription. However, to corroborate that the benefits seen in the clinical trials are indeed sustained long-term, a prospective follow-up of the treated patients should be realized and reported. Then, if the long-term benefit is ascertained, as a society, we would be both ethically and morally obliged to assure the access to these personalized therapies for all CF patients that would benefit.

Pharmacogenomics

Even if specific CF protein therapies are emerging, inter-individual variation to drug treatment is unfortunately expected and has to be considered in relation to the 4P medicine. In this context, the future upsurge of highthrough-put genomic data concerning and metabolites have been shown to be able to inhibit CYP3A, and other cytochromes (CYP2C8, CY2C9 and CYP2D6). A recent drug-drug interaction (DDI) study examined the effect of Kalydeco ® on sensitive substrates of CYP2C8 (rosiglitazone), CYP3A (midazolam), CYP2D6 (desipramine), and P-glycoprotein (P-gp) (digoxin). Also, a DDI study of Kalydeco ® combined with oral contraceptives was realized that indicated that there was no need for dose adjustment. Kalydeco ® is a weak inhibitor of CYP3A and P-gp, but has no effect on CYP2C8 and CYP2D6 [START_REF] Robertson | Clinical drug-drug interaction assessment of ivacaftor as a potential inhibitor of cytochrome P450 and P-glycoprotein[END_REF]. Thus, caution must be exercised with co-administration of CYP2C9, CYP3A, and/or Pgp substrates. Although CYP SNPs may affect the level of enzyme production, to date, no genomic studies have been performed to assess the effect of CYP variants on the pharmacokinetics of Kalydeco ® in CF patients. Interestingly, CYP3A also interacts with widely used drugs for CF patients, such as macrolides such as azithromycin [START_REF] Westphal | Macrolide -induced clinically relevant drug interactions with cytochrome P-450A (CYP) 3A4: an update focused on clarithromycin, azithromycin and dirithromycin[END_REF] or Tacrolimus [START_REF] Monchaud | Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation[END_REF]. A recent study has shown that Tacrolimus clearance was 40% faster in CYP3A5 expressers compared to non-expressers. Pharmacogenetics testing is a growing current and future challenge that would be predictive for efficacy and for adverse drugs effects.

Conclusion

  aureus and B. cepacia. The haplotype in the first exon (A/O), known to modulate the serum level of MBL, has been investigated by several independent teams. A meta-analysis has shown that this haplotype, corresponding to a MBL deficiency, is associated with the severity M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT of CF lung disease with an early acquisition of P. aeruginosa, more frequent airway colonization by B. cepacia, a decreased lung function in adults, more frequent transplantation

  may contribute to the development of personalized treatment across specific subgroups of CF patients. Importantly, Kalydeco ® is mainly metabolized by the cytochrome P450 family 3, subfamily A (CYP3A) and early in its development, Kalydeco ®

35 years following the

  discovery of CFTR, personalized medicine is not merely just a concept for the treatment of cystic fibrosis but a reality for the patients. Protein specific therapy is a clear step-ahead in terms of therapeutic options for CF patients, particularly in regards to the new correctors, potentiators and readthrough drugs. Finally, the highthroughput genomic data, new genome editing tools, and stem cell regenerative medicine are excitingM A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPTand will hopefully further evolve into new affordable therapeutics and rise to meet the needs of CF patients.

  . Consequently, in 2012, Ivacaftor was approved in the US and in Europe by the Food and 70% of the CF patients carry at least one p.F508del CFTR allele, and 50% are homozygotes for this mutation. It results in the misfolding of the protein leading to rapid degradation of the protein in the cell, preventing its expression at the cell surface.

	ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT
	(p.S549R), p.Gly551Ser (p.G551S), p.Gly1244Glu (p.G1244E), p.Ser1251Asn (p.S1251N), chloride level (63). However, no FEV 1 improvement was observed for the p.F508del
	p.Ser1255Pro (pS1255P) and p.Gly1349Asp (p.G1349D) (59). Recently, Ivacaftor has also heterozygous patients. Two international phase III studies encompassing ~1100 patients
	been shown to benefit patients carrying the c.350G>A/p.Arg117His (formerly R117H) homozygous for p.F508del ended this year, and promising results have been shown at the last
	mutation, that is relatively common in the CF population, and leads to both defective channel North-American CF Conference (NACFC 2014, Atlanta) but are unpublished as of yet
	Drug Administration (FDA) and the European Medical Agency (EMA) respectively, and commercialized under the name of Kalydeco ® for patients older than 6 years old, and with at M A N U S C R I P T A C C E P T E D regulation (sharing similarities with the class III mutations) and conduction. Thus, the FDA also approved Ivacaftor as a treatment for CF subjects aged greater than 6 years old with at least one copy of R117H-CFTR (60). Additionally, Ivacaftor is currently being tested in patients carrying non-R117H residual function mutations. The phase II clinical trial showed encouraging results with improvement of lung function and a decreased sweat chloride level; a phase III clinical trial will surely be planned shortly (Vertex release 852372). As introduced above, the p.Phe508del (p.F508del) mutation is the most common class II to be sufficiently expressed at the cell surface. Vertex developed the VX-809 (or Lumacaftor), a corrector which may achieve this goal. In fact, using bronchial epithelial cells harboring the p.F508del homozygous mutation, VX-809 increased the chloride transport (55-58, 61). A phase II clinical trial has shown a modest beneficial effect on the sweat chloride levels in patients homozygous for the p.F508del mutation (62). This suggested that merely restoring CFTR expression was not sufficient, and that the function of CFTR had to be promoted by a A C C E P T E D mutation. Worldwide, Molecules that could prevent this degradation are called "correctors", and would allow CFTR M (Vertex press release) (64, 65). Interestingly, another promising compound similar to Lumacaftor, VX-661, has also been developed by Vertex, and clinical trials are underway. Interim results of a phase II, double-blind, placebo-controlled study of VX-661 (various daily doses) alone or in combination with Ivacaftor have been reported for p.F508del homozygous patients (66). The treatment was well tolerated and led to an improvement of the percent-predicted FEV 1 (maximum 9% of gain with a combination VX-661 and Ivacaftor), and a A N U S C R I P T decreased sweat chloride level.
	least one p.G551D CFTR allele. Further, Kalydeco ® has been tested in patients carrying other potentiator. Consequently, a phase II trial in adults homozygous or heterozygous for the
	class III mutations, or targeted class IV and V mutations (sharing functional similarities with p.F508del mutation was realized using the combination of Ivacaftor and Lumafactor. This
	the class III) (58). The new trials led to an extension of the FDA and EMA approval to 8 association was well tolerated, and, in the homozygous p.F508del patients, improved lung
	additional gating mutations: p.Gly178Arg (p.G178R), p.Ser549Asn (p.S549N), p.Ser549Arg function (gain of 8.6% percent-predicted FEV 1 ), and significantly decreased the sweat

Acknowledgements

LG received a research grant from the French cystic-fibrosis nonprofit organization Vaincre la Mucoviscidose. KT received a postdoc fellow grant from the French cystic-fibrosis nonprofit organization Vaincre la Mucoviscidose. All authors have read the journal's policy on conflicts of interest, have no conflict of interest to declare and have read the journal's authorship agreement. The manuscript has been reviewed by and approved by all named authors.

Figure legends

Figure 1. CFTR mutations classes (I to V), their functional consequences and their potential specific therapies. CFTR is composed of 2 nucleotide-binding domains (NBD1 and NBD2), two transmembrane domains (TMD1 and TMD2) and one regulatory (R) domain.