 Methodology article
 Open Access
 Published:
Penalized partial least squares for pleiotropy
BMC Bioinformatics volume 22, Article number: 86 (2021)
Abstract
Background
The increasing number of genomewide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these crossphenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene and a pathwaylevel approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called jointsgPLS, is able to convincingly detect signal at the variable level and at the group level.
Results
Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers.
Conclusion
The jointsgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.
Background
Genomewide association studies (GWAS) have identified numerous genetic markers linked to multiple phenotypes, suggesting the existence of pleiotropy that occurs when a single variant or gene can influence several phenotype traits [1,2,3,4]. Highlighting pleiotropy provides opportunities for understanding the shared genetic underpinnings among associated diseases. However genetic information may be spread among different studies (a) because the signal is small and larger sample sizes can increase the ability of detection (b) because in the case of rare phenotype, analyses require to study distinct data sets corresponding to different phenotypes. Therefore combining data across studies is necessary for crossphenotype or pleiotropic association analyses. Combining data across studies on different phenotypes could also permit to increase statistical power to detect new signals weakly associated to several phenotypes. This leads to consider data sets from different sources, having common genotype data, but which phenotype traits may differ from one study to another.
In this article, we are interested in metaanalysis methods dealing with data from independent studies. Genetic information comes from single nucleotide polymorphisms (SNP). Genes are defined by a set of SNPs grouped in the same location in the genetic sequence. Pathways are groups of genes involved in a common biological mechanism. Genetic analyses aim at testing the association between genetic variants and phenotypes at the SNP, gene or pathwaylevel. Hence, information about independent data sets gives an architecture in terms of observation sets while information about either genes or pathways gives an architecture in term of groups of variables. The challenge of pleiotropy is then to take advantage of these architectures.
In addition, possible biases between observation sets can be induced in genetic studies especially due to differences of studied population, used technologies or experimented protocols. Those called “batch effects” are a common problem for metaanalyses [5], and methods for pleiotropy must take it into account. Furthermore, such methods must cope with the case where a genetic variable have a positive effect on one trait and a negative effect on another traits. Those opposite effects cannot be highlighted by standard metaanalysis methods [6, 7].
Various statistical methods were proposed for gene set analysis or to analyze pleiotropy. Recent pleiotropy analyses rely on statistical methods coming from gene set analysis combined with a metaanalysis [1, 8,9,10]. A nonexhaustive list of gene set methods can be given. Burden test and variance component tests have been developed to analyse rare variants [7, 11,12,13]. Alternatively, dimensionality reduction methods [14, 15] and Bayesian models have also been largely exploited [16, 17]. We can also cite pairwise similarity based model [18], Ustatistic models [19, 20], linear model family methods [21, 22] and networkbased methods [23]. Furthermore, other omics fields are rising [24,25,26,27] and methods for genomics are often reused in those analyses [28].
We aim at integrating the metaanalysis perspective in cases of distinct data set to a gene set method framework. An extension of the sparse Partial Least Square (sPLS) method suited for metaanalysis for pleiotropy is proposed. It deals with observation sets and group of variables information while taking into account the possibility of opposite effects, i.e cases where a genetic variable has a positive effect on one trait and negative effect on other trait. As a sPLS family method, it can cope with the high number of variables. The method formulates at the same time a grouplasso resp. a jointlasso penalization to represent the group of variables resp. the sets of observations.
PLS is a dimensionality reduction method developed by Wold [29] and that has been widely used for the analysis of data with large number of variables [30]. Applications have been done outside of genetic studies, for instance in chemometry [31] or for neuroimaging [32]. Unlike, its cousin method (PCA), the Principal Component Analysis (PCA) [33], the PLS deals with two blocks of data and this is used for genotypephenotype analyses. Moreover its sparse extension using Lasso penalization has been successful at providing readable models [34]. Especially sparse group Partial Least Square can take into account group of variables as a priori information [35, 36]. For different group of studies an alternative Lasso penalization has been proposed by Obozinski [37] for a linear regression to deal with data made of different sets of observations. An adaptation of the Lasso penalization, the jointsgPLS, has recently been proposed for the PLS [38], answering the specific of both groups of variables and sets of observations. In this article, we exploit the same idea to leverage pleiotropy effects, especially because the method copes with the challenge of detecting small possible opposite effects.
The method is compared to two well established statistical methods in genetic studies. The first one, ASSET [6] extends standard fixedeffects metaanalysis methods for detecting effects in opposite directions from a same genetic trait. The second one metaSKAT [7] permits to carry out genebased metaanalysis extends SKAT and SKATo methods for metaanalyses.
The developed statistical approaches will be applied to real dataset for enriching our insights about the genetic mechanisms of thyroid and breast cancer types. We are interested into exploring genelevel and pathwaylevel associations for each cancer type as well as for both cancer types together.
Methods
Notations
Data are represented by \(X \in \mathbb {R}^{n \times p}\) and \(Y \in \mathbb {R}^{n \times q}\), two matrices, representing n observations of p predictors and q independent variables. The Frobenius norm on matrices is denoted \(\left\ ~ ~ \right\ _F\). We note \(X^T\) the transpose matrix of X and the cardinal of a set S is noted \(\# S\). The positive value of a real number x is noted \((x)_+ = \frac{x+x}{2}\) and is equal to the number if the number is positive and equal to zero otherwise. In general, observation sets can represent the fact that different sets of observations come from different sources and must be analyzed accordingly. For instance, data coming from different studies may present biases. Variables groups can represent a set of variables that are known or suspected to be part of a same signal. For instance, in genetics a gene defines an established group of SNP variables and pathways define established group of genes. Let us consider M different sets of observations in the data. Noting, for \(m \in \mathbb {N}\), \(\mathbb {M}_m\) a subset of \(\{1, \dots , n\}\), let \(\mathbb {M}=(\mathbb {M}_m)_{m=1..M}\) be a partition of \(\{ 1,...,n \}\) corresponding to the observation sets. We note \(\# \mathbb {M}_m=n_m\). Row blocks are defined by this partition. Let us consider that variables are gathered in K groups. Let \(\mathbb {P}=(\mathbb {P}_k)_{k=1..K}\) be a partition of \(\{ 1,...,p \}\) corresponding to this variable group architecture. We note \(\# \mathbb {P}_k=p_k\). We then we have \(\sum _{k=1}^K p_k =p\). Column blocks are defined by this partitions. Both observation set architecture and variable group architecture can be defined at the same time as shown in Fig. 1. For matrices, the notation \(\cdot\) is used to refer to blocks of matrices. For instance \(X_{\cdot , \mathbb {P}_k}\) is the block of matrix of X corresponding the columns of the kth group of variables and \(X_{ \mathbb {M}_m,\cdot }\) is the block of matrix of X corresponding the columns of the mth set of observations.
Sparse Partial Least Square for structured data
In the literature, several formulations of the PLS exist [39]. While they can have similar performances [40], PLS1 [41] has prevailed in last developments [35, 40, 41]. In the scope of this article, this formulation has been chosen in order to be able to pursue the path of previous methods. PLS finds successively couples of vector \(\{u_1,v_1 \} , \dots , \{u_r,v_r \}\) for \(r < \text{ rank }(X)\), where the couples are composed of vectors of length resp. p and q, maximizing \(Cov(Xu_i,Yv_i) \text{ for } \text{ any } i \in \{1,\dots ,r\}\), under the constraint that \(u_1, \dots , u_r\) are related to orthogonal families of components [29]. It can be solved considering successive maximization problems [42], for \(h \in \{1, \dots , r\}\)
where \(X_0 =X\), \(Y_0=Y\) and \(X^{(h1)}\), \(Y^{(h1)}\) are deflated matrices computed from \(u^{(h1)}\),\(v^{(h1)}\), \(X^{(h2)}\), \(Y^{(h2)}\) for \(h \in \{2, \dots , r \}\). The deflation depends on the PLS mode that is chosen [29, 43]. In the following, the notation h is removed in order to simplify the formulation because we are interested in only one of the r steps of the PLS.
The sparse PLS (sPLS) propose to add a penalization to the loading vectors u and v. The following equivalence is used:
and the proof can be found in [35].
The sPLS [42] can be written as
The sparse PLS introduces a penalization in this formulation of the problem. The penalty \(P( \cdot )\) forces smallest participation to u to be set to zero. The parameter controlling the degree of sparsity in the model is \(\lambda\). In the presented formula the sparsity is applied only to the vector u, but a similar penalization can be defined for v. In the context of this article we treat only the penalization of u but all the results stand also for a v penalization.
Remark 1
Before analysis, the X and Y matrices are transformed by subtracting their column averages. Scaling each column by their mean and standard deviation is also often recommended [44]. Thus, the crossproduct matrix \(X^T Y\) is proportional to the empirical covariance between X and Yvariables when the columns of X and Y are centered. When the columns are standardized, \(X^T Y\) is proportional to the empirical correlations between X and Yvariables. In this article the standardization is an important step to overcome the issue of the “batch effect” or to aggregate observations from different studies. The point has been discussed in [38].
Remark 2
Presented framework deals with the estimation of a pair of weight vectors (u, v), which is the main contribution of the method in terms of methodology. This estimation step can then be included in the global framework of PLS with the deflation steps for modeling several components.
Extensions of the sparse Partial Least Square
In the following, extensions of the sPLS taking into account an observation or/and variable set architectures are presented. The last method has been recently developed [38] and deals with both kinds of architecture. It is the main topic of the article. Proposed model is an extension of the multigroup sPLS proposed by Eslami et al. [40].
In order to cope with the architectures, sgPLS has been proposed [35]:
where the loading vectors u and v are composed of resp. p and q elements. Penalization \(P_{variable}\) shrinks variables individually towards zero whereas penalization \(P_{group}\) shrinks whole groups of variables towards zero. The parameter driving the degree of sparsity of the model is \(\lambda\) whereas the parameter controlling the balance between both kinds of sparsity is \(\alpha\). In this model elements of u corresponding to least relevant variables and least relevant groups of variables are set to zero.
An extension using the joint Lasso penalization from Obozinski ( [37]) has been proposed [38]. This method is the object of study of this article. Its formulation for the sgPLS is:
where the set of loadings U is composed of \(p \times m\) elements (p elements per \(U_{\cdot ,m}\)). The set of loadings V is composed of \(q \times m\) elements (q elements per \(V_{\cdot ,m}\)). In this model elements of U corresponding to least relevant variables and least relevant group of variables are set to zero. Variables and groups of variables corresponding to least participating variables are set to zero for all \(U_{\cdot ,m}\), \(m \in \{1,\dots ,M \}\) at the same time.
The solution of Eq. 5 is:
where the positive value of a real number x is noted \((x)_+ = \frac{x+x}{2}\).
The solution is computed in 3 steps. First step (Eq. 6a) represents the solution of simple PLS for each M studies separately. Second step (Eq. 6b) applies sparsity on each variable for all studies at once. Third step (Eq. 6c) sets a sparsity on each group of variables for all studies at once. For all sparse methods, optimal parameters driving the penalization (\(\lambda\) and \(\alpha\)) must be chosen. A Kfold crossvalidation is used here. For each set of penalization parameters that must be tested:

Observations are split into a partition of L samples: \(\{\mathbb {S}_1 , \cdots , \mathbb {S}_{L}\}\). For a qualitative outcome, samples are chosen respecting the proportion of population of the outcome. For \(l \in \{1 , \cdots , L \}\), the subset of \(\{1, \cdots , n\}\) where \(\mathbb {S}_{l}\) is omitted is noted \(\mathbb {S}_{l}\).

For \(l \in \{1, \cdots , L\}\), a model is performed on \(X_{\mathbb {S}_{l},\cdot }\) and \(Y_{\mathbb {S}_{l},\cdot }\). From this model a prediction is performed on \(X_{\mathbb {S}_{l},\cdot }\) which gives a prediction \({\hat{Y}}_{\mathbb {S}_{l},\cdot }\). Prediction error is computed comparing \({\hat{Y}}_{\mathbb {S}_{l},\cdot }\) and \({Y}_{\mathbb {S}_{l},\cdot }\). For qualitative outcome, a missclassification rate is computed. For a quantitative outcome a L2norm is computed. For multivariate outcome, the mean prediction over each variable outcome is computed.

The mean of prediction errors over the L models is computed.
The set of parameters corresponding to the lowest error of prediction over the procedure above is selected. An example of the procedure can be found in the implementation of many extensions of the sPLS [35, 36, 40].
The Kfold procedure relies on the prediction performances. However, if the signal is too small, prediction can be poor and the calculation of optimal parameters can be problematic in a crossvalidation framework. Other Lasso penalization methods have struggled when the number of variables is large [42, 45]. Due to the large number of variables in genomic data, the difference in term of prediction performance is not large enough to highlight one clear choice of penalization parameter. In this article, an alternative bootstrap strategy is proposed: sgPLS and jointsgPLS are evaluated with given parameters on the data. Then, a bootstraps procedure is performed B times. The methods sgPLS and jointsgPLS are then implemented on each bootstrap. The selection rate for variables (resp. group of variables) over the bootstraps are calculated. Rates are considered depending on whether or not the variable is selected by the model computed on true data. Selected variables (resp. group of variable) whose rate is higher than any nonselected variables are kept in the final selection.
Remark 3
The proposed joint penalization is biconvex but not convex, and thus multiple local minima may exist. The method can then be sensible to the starting point of its algorithm. Some development using several starting points can enhance the probability of reaching a global optimum and some can even ensure it. In dimensionality reduction methods a semidefinite relaxation has been proposed which ensures the convergence [46] at the cost of computational efficiency. Methods relying on random initialization have increased the chances of finding the global optimum but with lower theoretical guarantees. Inheriting such developments for the jointsgPLS would be interesting for future developments.
Remark 4
Group sparse dimensionality reduction methods such as sgPLS and jointsgPLS need to be extended in case of overlapping groups of variables [47]. In the scope of this article, groups of variables are supposed to be disjointed.
Benchmark methods
Both ASSET and metaSKAT are considered as benchmark methods.
ASSET is a method suited for metaanalysis providing a pvalue across studies [6]. The input of the method are single variables summary statistics which are combined by the method. ASSET exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions.
For a given variable \(i \in \{1, \cdots ,p\}\) and a given set of studies \(m \in \{1, \cdots ,M\}\) the estimate parameters \(\{ \beta _{i,m},s_{i,m} \}\) of a linear model on data \(X_{\mathbb {M}_m,\cdot }\) and \(Y_{\mathbb {M}_m,\cdot }\) and the corresponding statistic \(Z_{i,m} = \frac{\beta _{i,m}}{s_{i,m} }\) are computed. Then for each possible subset \(S \subset \{1, \cdots ,M\}\), the mean statistic \(Z_i(S) = \sum _{l \in S} \sqrt{\pi _l(S)} Z_{i,l}\) is evaluated with \(\pi _l(S) = \frac{n_l}{\sum _{l \in S} n_l}\). ASSET seeks for the optimal subset of observations following the criteria \(\underset{S \in \mathbb {S}}{\max } Z(S)\). A pvalue is computed from this final statistic. ASSET relies on statistics at variable level and hence do not propose gene or pathwaylevel information. Further, the current version of ASSET provides pleiotropy result for each variant which should be corrected using a FDR correction in order to control possible false positive pleiotropy effect.
SKAT is a method to detect association between rare variants in a region and a phenotype (continuous or binary). It is a supervised test for joint effects of multiple variants in a region on a phenotype. The metaSKAT method can do the same but aggregating several studies. This method outputs a pvalue corresponding to a set of variables, for instance a gene or a pathway. The method is based on a weighted sum of SKAT statistics of the different studies [7].
The statistics \(S_{m,k} = X_{\mathbb {M}_m,\mathbb {P}_k}^T \tilde{Y}_{\mathbb {M}_m,\cdot }\) is computed where \(\tilde{Y}\) of a generalized linear model performed on Y with respect to covariates. Then a weighted sum is computed on these statistics summing among the studies and then following the variables: \(Q = \sum _{m=1}^{M} \sum _{k=1}^{K} (w_{m,k} S_{m,k})^2\) where \(w_{\cdot ,\cdot }\) are weights that must be chosen. Next, a pvalue is computed. The method relies on the square of the statistic and then can detect opposite effects from one study to another.
Unlike metaSKAT, sgPLS and jointsgPLS ASSET gives one result per variables, and does not give information for a whole group of variables. We can note that both ASSET and metaSKAT are pvalue oriented method which allow them to select variables. However, they cannot propose predictions whereas jointsgPLS can.
Simulated data
Presented methods are illustrated on simulated data presenting the architecture given in Fig. 1. From one side, SNP genotypes are coded as minor allele counting \(\{ 0, 1, 2 \}\) and a certain correlation is expected within a group of SNP from the same linkage disequilibrium block. From the other side, phenotype data are binary and have a true effect from one or more genetic markers. In order to simulate the correlation between SNPs, for a group of variables \(\mathbb {P}_k\), a multivariate normal distribution with n observations \(\varvec{x}^{(continuous)}_k \thicksim \mathcal {N}_{p_k}(\mu _k,\Sigma _k)\) is simulated where \(\mu _k\) is a null vector of size \(p_k\) and \(\Sigma _k\) is a \(p_k \times p_k\) matrix with 1 on the diagonal and \(\rho _k\), coefficients controlling the correlation between SNPs within a group, outside of the diagonal. A simulation of this variable gives a matrix which represents simulated observations for group of variables k. Those blocks are concatenated in a \(n \times p\) matrix, \(X^{(continuous)}\) that represents the whole data.
In order to have \(\{ 0, 1, 2 \}\) genotype data, a discretization is performed. For a given variable \(j \in \mathbb {P}_k\), we aim at simulating a SNP variable with a Minor Allele Frequency (MAF), which we note \(\text{ MAF}_j\). This MAF means that:
To this aim, for a given \(\text{ MAF}_j\), quantiles \(q_1^{(j)}\) and \(q_2^{(j)}\) are chosen such as \(P(x_j \le q_1) = (1MAF_j)^2\) and \(P(x_j \le q_2) = (1MAF_j)^2 + 2 MAF_j(1MAF_j)\)
A discrete genotype, \(X^{(discrete)}\), is computed such that
where \(i \in \{ 1 ,\cdots , n \}\) are simulated observations and \(j \in \mathbb {P}_k\) is a variable of kth group of variables.
For each observation i, a binary phenotype \(y_i\) is simulated with a logit model
where \(\pi _i=P(y_i=1 \text{ data})\), \(\beta _j\) for \(j \in \{1, \cdots , p \}\) is a regression parameter.
Then different simulations of the process can be performed successively in order to simulate several studies.
Results
The code used for running the methods is available on github (https://github.com/camilobroc/BMC_joint_sgPLS).
Simulation
In this article, simulated genotype has 25 groups of 20 variables. There are then 500 variables and data is composed of two studies with equal number of observations. Combinations of parameters are considered to study a variation of (i) the existence of opposite effects from one observation set to another (ii) the portion of SNPs of the groups having effects (iii) the sample size. Values choice are given in Table 1. Variation (i) permits to see the ability of method at detecting a signal even when opposite effects occur. Variation (ii) allows to observe the influence of intragroup sparsity on the performances of the methods. Variation (iii) shows cases where the signal is easier or harder to retrieve due to the different sample sizes.
The intragroup correlation parameters \(\rho _k\) are equal to 0.5 and the MAF is equal to 0.3 for each variable. The first 5 groups have an effect in the model of the simulations. For each group, half of the nonnull regression parameters are positives (taken at random) while the other half is negative. In cases where all SNPs have effects (cases 1, 2, 5 and 6), the absolute value of those parameters is set to \(\text{ exp }(0.1)\) whereas in cases with half of SNPs having effects (cases 3, 4, 7 and 8), the absolute value of those parameters is set to \(\text{ exp }(0.5)\).
For all methods 50 replications of the data are performed. For the implementation of the sgPLS and jointsgPLS, penalisation parameters must be chosen similarly to [35]. The penalization parameter \(\lambda\) and \(\alpha\) are optimized through a Kfold penalization procedure with an error of prediction as criteria. Choosing a parameter \(\lambda\) is equivalent to set a number of selected groups [35]. In this simulation the grid of number of selected groups \(\{ 1,\cdots , 25 \}\) is used and the grid for \(\alpha\) is \(\{ 0.1 , 0.5, 0.9 \}\). Figures 2 and 3 show the error of prediction performances through a crossvalidation procedure of the sgPLS and jointsgPLS in a simulation of case 1, for different levels for \(\alpha\) and different levels of group selection. The observed mean and the variance of the error rate over 50 replications are presented. In the framework of the method the set of parameters corresponding to the lowest error of prediction rate is kept for the model.
For ASSET, sgPLS and jointsgPLS, the variables selected by the models are compared to the variable having an effect on the true model. For metaSKAT, sgPLS and jointsgPLS, the group of variables selected by the models are compared to the group of variables having an effect on the true model.
Results of the simulations are presented in Table 1 for sgPLS, jointsgPLS, ASSET and metaSKAT. The measures of performance are the True Positives (TP), False positives (FP), False Negatives (FN) and True Negatives (TN) (Table 2).
Considering cases 1 and 2, we can see that ASSET and metaSKAT have FP lower than TP in opposition to sgPLS and jointsgPLS. They are then more conservative than the two later methods. We can see that overall, each model performs better when the number of observations is higher (200 against 400). We can see that when the intrasparisity is set to 50 % (cases 3, 4, 7 and 8) rather than 100 % (cases 1, 2, 5 and 6), variablelevel results for ASSET sgPLS and jointsgPLS are inflated by more than a half. This may be due to the fact that the methods struggle to differentiate the effect of variables within a same group. The genelevel results are similar whichever the intrasparsity is for metaSKAT, sgPLS and jointsgPLS. Cases 1, 2, 3 and 4 have effect in the same direction among studies while cases 5,6,7 and 8 show effects in opposite directions. We can see that when effects are in the same direction or opposite direction, sgPLS can compete with jointsgPLS and with other benchmark methods while being the least conservative. On the other hand, when effects are in different directions, sgPLS performances fumble whereas other methods keep a similar TP/FP ratio. Comparing closely ASSET to jointsgPLS, we can see that jointsgPLS have always a higher TP and the largest difference can be seen when all variables are involved within a group (cases 1, 2, 5, 6). This is probably due to the fact that jointsgPLS can draw information at the grouplevel to infer single variable results. Comparing closely metaSKAT to jointsgPLS, we can see that both methods can retrieve a large amount of groups participating to the effect. The method jointsgPLS have always a higher TP in each cases. In cases 1 and 5, metaSKAT TP is especially low. Those are cases with the smallest number of observations and with small regression parameters \(\beta _j\) and hence where the intensity of the signal is the lowest.
Overall, we can see that sgPLS and jointsgPLS have competitive performances for detecting effect in the same direction while jointsgPLS is the method with the best performance for detecting opposite effects. Furthermore sgPLS and presented jointsgPLS have the merit of giving single variable results and group results in the same model. This allow variablelevel results to be enhanced by the group a priori information.
Pleiotropy investigation on breast and thyroid cancer
The developed statistical approaches were applied to real data in order to enrich our insights about the genetic mechanisms involved in carcinogenesis of thyroid and breast cancers. Thyroid and breast cancers share some similarities in their biology: both are more frequent in women, are influenced by hormonal and reproductive factors and are hormonallymediated. Moreover, individuals diagnosed with breast cancer are more likely to develop thyroid cancer as a secondary malignancy than patient diagnosed with other cancer types, and viceversa [48]. Genetic factor contributing to the incidence of breast cancer have been extensively studied, and it is known that genetic variants explain approximately 49 percent of the familial risk to develop this disease. Using GWAS, 313 risk variants were identified for breast cancer [49]. On the other hand, GWAS studies on thyroid cancer have been scarce, due to the lesser incidence of this disease as well as the lack of data. However, it has been shown that thyroid cancer is the only cancer for which genetic factors contribute more than environmental factors [50]. Only 4 loci have been associated with thyroid cancer risk and have been replicated in other studies [51]. One of them, 2q35, was also previously reported to increase risk of breast cancer [52]. To date, no study has been conducted to identify common genetic factors between breast and thyroid cancer. Exploring the genetic relationship between the two cancers would help to elucidate the common mechanisms between both disease and could permit to improve their diagnostic and therapeutic management.
We propose to illustrate the methods on real datasets, by investigating the pleiotropic effect of genetic variants from candidate pathways in breast and thyroid cancers.
Beluhca dataset includes data from CECILE, a french casecontrol study on breast cancer (1 125 cases, 1 172 controls) and from CATHY a french casecontrol study on thyroid cancer (463 female cases and 482 female controls). All these individuals were genotyped using a customized microarray including 8 716 genetic variants from 28 candidate pathways (648 genes) selected from KEGG database and from a literature review (SNPs are located at +/− 50 kb from the gene boundaries). After quality controls, we retained 6 677 SNPs available for both type of cancers. Missing values were imputed using the median among cases or controls and data were centered to \(\mu = 0\). When 2 SNPs were correlated at \(r^2=1\), one of the SNP was removed and couple of extremely correlated (\(r^2 > 0.98\)) SNPs belonging to same genes were eliminated.
As group sparse dimensionality reduction methods such as sgPLS and jointsgPLS need to be extended in case of overlapping groups of variables [47], 10 nonoverlapping pathways were selected and only the 3766 SNPs related to those groups were kept in the final database. After all these preprocessing, the new dataset is composed of 3766 SNPs, grouped in 337 nonoverlapping genes and 10 nonoverlapping pathways. The list of the pathways and genes is displayed in Tables 5 and 6 in Appendix 1.
The methods implemented in this article are: ASSET, metaSKAT, sgPLS and jointsgPLS. For metaSKAT, sgPLS and jointPLS, SNPlevel, genelevel and pathwaylevel results are given by the methods whereas ASSET gives only SNPlevel results. Hence, in the case of ASSET, genes corresponding to selected SNPs are considered. For each SNP i, an univariate logistic model for genedisease association can be considered separately for thyroid data and breast data (thyroid and breast cancer, Fig. 4).
As it has been presented before, for sgPLS and jointsgPLS, a calibration of the parameters is generally performed through a crossvalidation procedure. This process relies on the definition of a measure of performance: the error of prediction of the model. However, in genetic studies, the effects are small and the prediction performances based on genetic units are usually very low. The prediction performance of sgPLS and jointsgPLS are not different enough from one set of penalization parameters to another. In order to facilitate the interpretation, we present the results for calibration parameters set to 20 genes and 3 pathways and \(\alpha =0.5\). We explore the stability of the methods using the bootstrap strategy described in the section method. Figures 5 and 6 present this rate for preselected and nonpreselected features. A gene and resp. a pathway is kept in the final selection if and only if it is preselected and its rate of selection among the bootstraps is higher than any other gene (resp. pathway) that is not preselected. We can see that for jointsgPLS less genes are selected than for other methods (4 against resp. 20 and 18 for metaSKAT and sgPLS on both data).
Results of the selection are presented in Table 3 where the name of genes and pathways is presented. “sgPLS single” stand for the use of the sgPLS on thyroid and breast data separately while “sgPLS both” stands for the use of the method on a concatenation of both data set standardizing by study. Only genes that are selected by at least one method are presented. No genes from metabolism of xenobiotics pathway have been selected through all methods. We can see that methods focusing on SNPlevel information select gene from one of the study but never both studies at the same time except for INSR which is selected for both studies for SKAT. This genes is not selected by metaanalysis methods. Genes selected by grouplevel methods (ASSET, metaSKAT, sgPLS, jointsgPLS)) that are not selected by variablelevel methods are: PTEN, RORA, MSH3, IL18RAP,GNPDA2, LRRN6C, NEGR1, NR3C1, SEC16B, HEXA, HEXB, MAN2B2, NEU2, TGBR3, NMNAT2, CYP2C18, CYP2C19, MGST1. Those genes are good candidates for further investigations as they are not selected by study by study analyses but are selected by meta analyses. We can note that 5 out the 8 genes selected for Obesity and obesityrelated phenotypes pathway and all genes selected for Other glycan degradation are part of those genes. Those pathways would not have been as much hightlighted without metaanalyses. Genes selected for thyroid data sets and selected by metaanalyses are: MAP2K2, GTF2H1 and CYP2F1. Those genes are then related to thyroid cancer but metaanalyses suggest they may be involved with breast cancer in a common effect. Genes selected for breast data and selected by metaanalyses are: PLAG2G6, ERCC3, ERCC6, MUTYH, MTHFD2, IL13, NAT2. Metaanalyses suggest that these genes may also be involved with thyroid cancer in a common effect. We can see that jointsgPLS selects a lower number of genes (resp.4) compared to ASSET, metaSKAT and sgPLS (resp. 19, 20, 18). Method sgPLS and jointsgPLS select the glycan pathway and folate metabolism pathway and sgPLS selects also cell cycle pathway. PLS methods suggest that pathwaylevel effect could be involved.
Remark 5
Results based on different choice of calibration parameters for sgPLS and jointsgPLS (50, 100 genes and 5 pathways) showed similar patterns.
Computational performances
Computation performances are presented on simulation cases 1 and 2 which represent data having 500 predictors and 1 output (Table 4). The number of observations is respectively \(n = 200\) and \(n = 400\). The methods sgPLS and jointPLS penalization hyperparameters are estimated with the grid used for the simulation. Mean running times over 50 replications are given.
We can see overall that has the smallest running time. The methods sgPLS and jointsgPLS have the most expensive computational. This is due to the estimation of the penalization parameters as hyperparameters. However, this calculus consist in successive applications of the same method. It can then be paralellized.
Discussion
In this article, the properties of the jointsgPLS are presented and are compared to the classical sgPLS, the ASSET method and metaSKAT. The methods ASSET, metaSKAT and jointsgPLS are suited for metaanalyses whereas sgPLS is not. ASSET only gives variablelevel results whereas metaSKAT and jointsgPLS can assess grouplevel results. However, jointsgPLS is the only method proposing to link in a same model variable results and group results. The method have then more interpretability while have competitive or superior performances over simulations compared to benchmark methods. Hence, jointsgPLS seems perfectly suited for metaanalysis where effects in opposite directions can exist which invite us to pursue further investigation with it in complex studies for genetic epidemiology such as pleiotropy.
Conclusion
We do believe that further investigation can be done on the same subject. In this article, sgPLS and jointsgPLS have been applied with one component, but several components could be considered. This could lead to the selection of variables that are orthogonal to the selection of the first component but that have still a large participation to the covariance matrix.
We acknowledge that on the application the stability of the method is an important point due to the fact that the crossvalidation procedure is not satisfying for choosing the parameters of penalization. One improvement could consist in exploiting different the criteria of the procedure (the error prediction) with, for instance, stability measures [53]. Another improvement could consist in adaptating the adaptative Lasso [54] for our method which could bypass the stability questions.
Presented method uses a group architecture, but adding groupsubgroup architecture is an interesting path of investigation for taking into account gene and pathwaylevel information at the same time. The methods sgsPLS ( [36]) already offers a sparse partial least squares framework with group and subgroup architecture which is an extension of the sgPLS. A similar work could lead to a promising jointsgsPLS.
In order to advance on the application, this study should be replicated on a larger data base. Particularly, thyroid cancer has been less studied than breast cancer, and data for thyroid are still scarce in this application. Other cases of pleiotropy could be investigated, for instance for the case where the phenotype is multivariate for each subject. The jointsgPLS is suitable for any kind of phenotype, continuous or qualitative. R code is available from the author to reproduce the results and is available on github (https://github.com/camilobroc/BMC_joint_sgPLS).
Availability of data and materials
Code generating simulated data which has been analysed in this article is available at https://github.com/camilobroc/BMC_joint_sgPLS. It is designed for R software (version 3.6.3 and higher). The application dataset “Beluhca” that has been used during the current study is available from the authors on reasonable request.
Abbreviations
 GWAS:

Genomewide association study
 SNP:

Single nucleotide polymorphism
 MAF:

Minor allele frequency
 PLS:

Partial least squares
 sPLS:

sparse partial least squares
 sgPLS:

sparse group partial least squares
 sgsPLS:

sparse group sparse partial least squares
 SKAT:

SNP Kernel Association Test
 TP:

True positive
 FP:

False positive
 FN:

False negative
 TN:

True negative
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Acknowledgements
The authors acknowledge Pascal Guénel for providing the breast and thyroid cancer data. The authors acknowledge also the calculus center MCIA (Mésocentre de Calcul Intensif Aquitain) for providing its facilities. The “Ligue contre le Cancer” is acknowledged as well for its support for Cross Cancer Genomic Investigation of Pleiotropy project.
Funding
This study was supported by the“Ligue contre le Cancer” for its Cross Cancer Genomic Investigation of Pleiotropy project.
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CB and BL designed the novel method. CB implemented the analysis. TT perfomed interpretation on real data. CB, TT and BL wrote the manuscript. All authors read and approved the final manuscript.
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Written informed consent for the present study was obtained from all participants. Study protocols were approved by the French ethic committees (CNIL, CCPPRB) (reference numbers 053144 for CATHY study and 0453 for CECILE study).
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Broc, C., Truong, T. & Liquet, B. Penalized partial least squares for pleiotropy. BMC Bioinformatics 22, 86 (2021). https://doi.org/10.1186/s12859021039681
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DOI: https://doi.org/10.1186/s12859021039681
Keywords
 Genetic epidemiology
 High dimensional data
 Lasso Penalization
 Metaanalysis
 Oncology
 Partial Least Square
 Pathway analysis
 Pleiotropy
 Sparse methods
 Variable selection