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Genomewide sparse canonical correlation of gene expression with genotypes
BMC Proceedings volume 1, Article number: S119 (2007)
Abstract
There is a growing interest in studying natural variation in human gene expression. Studies mapping genetic determinants of expression profiles are often carried out considering the expression of one gene at a time, an approach that is computationally intensive and may be prone to high falsediscovery rate because the number of genes under consideration often exceeds tens of thousands. We present an exploratory method for investigating such data and apply it to the data provided as Problem 1 of Genetic Analysis Workshop 15 (GAW15). In multivariate analysis, canonical correlation analysis is a common way to inspect the relationship between two sets of variables based on their correlation. It determines linear combinations of all variables from each data set such that the correlation between the two linear combinations is maximized. However, due to the large number of genes, linear combinations involving all singlenucleotide polymorphism (SNP) loci and gene expression phenotypes lack biological plausibility and interpretability. We introduce sparse canonical correlation analysis, which examines the relationships of many genetic loci and gene expression phenotypes by providing sparse linear combinations that include only a small subset of loci and gene expression phenotypes. These correlated sets of variables are sufficiently small for biological interpretability and further investigation. Applying this method to the GAW15 Problem 1 data, we identified groups of 41 loci and 150 gene expressions with the highest betweengroup correlation of 43%.
Background
Several studies have demonstrated that there is variation in baseline gene expression levels in humans that has a genotypic component [1, 2]. Genomewide analyses mapping genetic determinants of gene expression are carried out for expression of one gene at a time, which may be prone to a high falsediscovery rate and computationally intensive because the number of genes under consideration often exceeds tens of thousands. In this paper we present an exploratory multivariate method for initial investigation of such data and apply it to the data provided as Problem 1 of Genetic Analysis Workshop 15 (GAW15). The linkages between the set of all singlenucleotide polymorphism (SNP) loci and the set of all gene expression phenotypes can be characterized by a type of correlation matrix based on the linkage analysis methodologies introduced by Tritchler et al. [3] and Commenges [4]. In multivariate analysis, a common way to inspect the relationship between two sets of variables based on their correlation is canonical correlation analysis, which determines linear combinations of variables for each data set such that the two linear combinations have maximum correlation. However, due to the large number of genes, linear combinations involving all of the genotypes or gene expression phenotypes lack biological plausibility and interpretability and may not able to be generalized. We have developed a new method, sparse canonical correlation analysis (SCCA), which examines the relationships between many genetic loci and gene expression phenotypes. SCCA provides sparse linear combinations. That is, only small subsets of the loci and the gene expression phenotypes have nonzero loadings so the solution provides correlated sets of variables that are sufficiently small for biological interpretation and further investigation. The method can help generate new hypotheses and guide further investigation.
Materials and methods
Data
The data consist of microarray gene expression measurements that are treated as quantitative traits and a large number of genotypes for 14 Centre d'Etude du Polymorphisme Humain (CEPH) families from Utah. Each pedigree contains three generations with approximately eight offspring per sibship. There are 194 individuals, 56 of which are founders. Phenotypes are measured by microarray gene expression profiles obtained from lymphoblastoid cells using the Affymetrix Human Genome Focus Arrays. Morley et al. [2] selected 3554 genes among the available 8793 probes on the basis of higher variation among unrelated individuals than between replicate arrays for the same individual. In this paper, we used a preprocessed and normalized data provided for these genes. Additional phenotypic data obtained for CEPH families includes age and gender.
Genotypes are measured by genetic markers provided by The SNP Consortium and are available for 2882 autosomal and Xlinked SNPs. The physical map for SNP locations is also available.
The statistical model
In this study we are interested in identifying linear combinations of measures based on gene expressions and SNPbased measures that have the largest correlation. Canonical correlation analysis (CCA) establishes such relationships between the two types of variables [5]. Suppose that x is a random vector of the first type of variables and y is a random vector of the second type of variables. We are looking for vectors a and b which maximize the following correlation:
where Σ_{11}, Σ_{22}, and Σ_{12} are the variance and covariance matrices. The solution is obtained from the singular value decomposition (SVD) of a matrix K given by:
Here k, is the rank of K. The highest canonical correlation is obtained by considering the best rank 1 approximation to the matrix K [6], K = d_{1}α_{1}β_{1}', where d_{1} is the positive square root of the first eigenvalue of K'K or KK', and α_{1} and β_{1} are first singular vectors. Then the canonical vectors or weights in the linear combinations of the two sets of variables that have the largest correlation are given by
In conventional CCA, all variables are included in the fitted linear combinations. However, in microarray and genomewide data, the number of genes under consideration often exceeds tens of thousands. In these cases linear combinations of all features may not be easily interpreted. Sparse canonical correlation analysis (SCCA) enhances biological interpretability and provides sets of variables with sparse loadings. This is consistent with the belief that only a small proportion of genes are expressed under a certain set of conditions, and that expressed genes are regulated at a subset of genetic locations. We propose obtaining sparse linear combinations of features by considering a sparse singular value decomposition of K where singular vectors α and β in Eq. (3) have sparse loadings. We developed an iterative algorithm that alternately approximates the left and right singular vectors of the SVD using softthresholding for feature selection. This approach is related to the sparse principal component analysis method of Zou et al. [7] and partial least squares methods described by Wegelin [8].
Sparse canonical correlation analysis algorithm
Assume that variables in both data sets have been standardized to have zero means and unit variances and K is the matrix in Eq. (2). Our algorithm works as follows:

1.
Select sparseness parameters λ_{ α }and λ_{ β }for left and right singular vectors, respectively. Select initial values for singular vectors α^{0} and β^{0}, set i = 0.

2.
Update left singular vector α as follows:

a.
Set α^{i+1 }= Kβ^{i}and normalize it to have unit length

b.
Softthresholding: ${\alpha}^{i+1}={\left(\left{\alpha}^{i+1}\right\frac{1}{2}{\lambda}_{\alpha}\right)}_{+}Sign({\alpha}^{i+1})$

c.
Normalize α^{i+1 }again

3.
Update right singular vector β as follows:

a.
Set β^{i+1 }= Kα^{i+1 }and normalize it to have unit length

b.
Softthresholding: ${\beta}^{i+1}={\left(\left{\beta}^{i+1}\right\frac{1}{2}{\lambda}_{\beta}\right)}_{+}Sign({\beta}^{i+1})$

c.
Normalize β^{i+1 }again
4. i = i + 1, repeat steps 2 and 3 until convergence.
The algorithm converges to the first singular vectors α_{1} and β_{1} in Eqs. (2) and (3). Other canonical vectors can be obtained by considering the residual of the correlation matrix. This is a very computationally efficient algorithm because it usually converges in less than ten iterations and requires only matrix multiplication. Optimal combination of sparseness parameters for softthresholding steps can be selected using crossvalidation. A subset of the data is repeatedly used to identify linear combinations of variables for the specific pair of sparseness parameters and then the correlation between the obtained canonical vectors is calculated in the remainder of the data set. The optimal combination of λ_{ α }and λ_{ β }corresponds to the highest average correlation over the repetitions.
Analysis approach
In this study one type of variables is based on gene expression levels and the other type of information relates to SNP genotypes and pedigree structure. An immediate challenge in this context is how to define correlation between these two types of data. We adopted a measure of covariance of genetic similarity with phenotypic similarity as in the linkage analysis methodologies of Tritchler et al. [3] and Commenges [4]. Consider the offspring generation in all available pedigrees and take all possible sib pairs. Let y_{ ij }and y_{ ik }be the phenotypes for the siblings j and k in family i for a particular gene expression and let w_{ ijk }represent identitybydescent (IBD) value for these siblings for some specific SNP. Then, for the given gene expression and SNP, the test statistic for linkage in Tritchler et al. [3] is
which is used for computation of covariance matrix between the phenotypic similarity and genotypic similarity. Note the similarity of the above expression to HasemanElston regression. In fact, Tritchler et al. [3] show that the correlation statistic subsumes both the original HasemanElston regression analysis and the later HasemanElston (revisited).
Phenotypic similarity
The phenotypes in this study are the gene expression values for siblings in the last generation of the pedigrees (i.e., the offspring generation). Previous studies have shown that there is variation in human gene expression according to age and gender [2]. Therefore, we limit the analysis to the last generation in all pedigrees and also correct for the effects of gender and age by fitting a linear model
y_{ ij }= α + β_{ gender }gender_{ ij }+ β_{ age }age_{ ij }+ e_{ ij }= E(y_{ ij }) + e_{ ij }.
Gender and age information were not available for all individuals in pedigree 1454 and for three individuals in pedigree 1340. Therefore, these data were excluded from the analysis. In the 13 remaining pedigrees, there were 344 distinct sib pairs with sibship size varying between 15 and 28. Although sib pairs are correlated within pedigrees, this does not affect the results because this is an exploratory study and no assumption of independence is made.
The phenotypic similarity for siblings j and k in pedigree i is computed as sibpair crossproduct of mean corrected gene expression phenotypes:
{y_{ ij } E(y_{ ij })}{y_{ ik } E(y_{ ik })}.
Genotypic similarity
For each sib pair, the probabilities of sharing 0, 1, and 2 alleles identically by descent (IBD) were estimated using MERLIN. This is an exploratory analysis of all SNPs and gene expressions, so great precision in the estimation of the IBD values is not required and the provided physical distance map of the SNP locations is a suitable approximation to the genetic distances required by MERLIN. Given the incomplete genetic marker information for some individuals, exact IBD values could not be computed. We estimated the number of alleles shared IBD by two siblings as a posterior expected value based on the probabilities estimated using MERLIN. Expected IBD values E(w_{ ijk }) were computed as the sample means over all sib pairs.
Standardization
We standardize the phenotypic and genotypic similarity variables by subtracting the means and dividing by the standard deviations. Simulations show that after data standardization, the analysis can be simplified by replacing Σ_{11} and Σ_{22} in Eq. (2) with an identity matrix while yielding satisfactory results. Then K in Eq. (2) is the covariance between the two standardized data sets and the first canonical vectors in Eq. (3) are just α_{1} and β_{1}.
Evaluation
We evaluated the performance of SCCA using the leaveoneout crossvalidation (LOOCV) analysis, treating a pedigree as one unit. In this study, assessment of performance is based on the estimated test sample correlation. It shows correlation between linear combinations of identified loci and gene expressions in the independent sample. We used pedigree as the unit in LOOCV because it represents a statistically independent unit. Leaving out one whole pedigree preserves dependence structure in the familybased study and ensures independence between training and testing samples. We carried out an analogue of 13fold CV where foldsize was dictated by the complex structure of the data.
Results
Using crossvalidation, we obtained softthreshold value of 0.07 for gene expressions and 0.13 for SNPs, corresponding to the maximal test sample correlation. We carried out SCCA using this optimal combination of sparseness parameters and identified groups of 41 SNPs and 150 gene expressions with betweengroup correlation of 43%. All obtained SNPs are uniformly distributed over a region on chromosome 9 between 86.80 megabases (Mb) and 120.09 Mb. Expressed genes selected by SCCA are distributed over different chromosomes. Six of the identified gene expressions are located on chromosome 9. Three other chromosomes that have more than 15 gene expressions each are 1, 2, and 6. No cisacting genetic regulators were found, where cisregulators are defined as those that map within a 5Mb region, as was previously defined in Morley et al. [2].
Table 1 summarizes the results of the crossvalidation study comparing the performance of SCCA to the complete SVD solution that includes all 3554 gene expressions and 2882 SNPs. Average overlap between the group of 150 gene expressions selected using SCCA on the complete data and the groups of gene expressions selected in CV steps is 46 genes; the average intersection of the 41 SNPs with the results in CV steps is 34 SNPs. Inspecting CV iterations shows pedigrees to be heterogeneous, with two pedigrees, 1416 and 1418, being outliers.
Discussion
In this study we presented sparse canonical correlation analysis and demonstrated the application of this new multivariate method to the simultaneous analysis of gene expression levels and SNPs. Due to complex interaction between genes, a set of several loci may be associated with several gene expressions possibly belonging to the same regulatory pathway or genetic network. SCCA discovers such sets of SNP loci and gene expression phenotypes while keeping the size of groups sufficiently small for biological interpretation.
We identified a specific region on chromosome 9 that regulates a group of gene expression profiles. Selected sets of loci should be interpreted as whole in relation to the whole set of selected gene expressions. This sparse solution may help to generate new hypotheses and isolate groups of loci and gene expressions for future biological experimentation. For instance, selection of a specific region on chromosome 9 by SCCA is particularly interesting, and a possible interpretation could be that we found a regulatory region.
The same region on chromosome 9 was also identified in other GAW15 contributions. For instance, considering a small set of genes associated with the development of the enteric nervous system (ENS), Lantieri et al. [9] also found evidence of linkage for two genes, 201387_s_at and 209034_at, to a unique common regulator located on chromosome 9 at 109 centimorgans (cM). Similarly, Wang et al. [10] found 10 gene expressions mapped to a "hot spot" on chromosome 9, however, gene names and specific chromosomal locations were not provided. One of the 41 SNPs selected by SCCA, rs1355620, was found to be linked to a composite score for "cell surface receptor linked to signal transduction" by Liu et al. [11].
We used leaveoneout crossvalidation to evaluate the performance of SCCA method. The results showed a slightly lower average test sample correlation for SCCA compared to full SVD solution as shown in Table 1. For this particular data set, a possible explanation is that outliers among the results in the CV steps due to the two incongruous pedigrees. This indicates that using stringent constraints for subsetting variables may result in greater vulnerability to outliers. In simulations we have carried out to assess the performance of SSCA (results not shown) our method demonstrates better performance compared to standard CCA based on full SVD in terms of test sample correlation (EP, unpublished data). Thus, SCCA may potentially provide a more robust solution. Additional empirical studies using more homogeneous sets of pedigrees and larger sample size would be useful.
References
 1.
Cheung V, Spielman R, Ewens K, Weber T, Morley M, Burdick J: Mapping determinants of human gene expression by regional and genomewide association. Nature. 2005, 437: 13651369. 10.1038/nature04244.
 2.
Morley M, Molony C, Weber T, Devlin J, Ewens K, Spielman R, Cheung V: Genetic analysis of genomewide variation in human gene expression. Nature. 2004, 430: 743747. 10.1038/nature02797.
 3.
Tritchler D, Liu Y, Fallah S: A test of linkage for complex discrete and continuous traits in nuclear families. Biometrics. 2003, 59: 382392. 10.1111/15410420.00045.
 4.
Commenges D: Robust genetic linkage analysis based on a score test of homogeneity: the weighted pairwise correlation statistic. Genet Epidemiol. 1994, 11: 189200. 10.1002/gepi.1370110208.
 5.
Mardia K, Kent J, Bibby J: Multivariate Analysis. 1979, New York: Academic Press
 6.
Good I: Some applications of the singular decomposition of a matrix. Technometrics. 1969, 11: 823831. 10.2307/1266902.
 7.
Zou H, Hastie T, Tibshirani R: Sparse principal component analysis. Technical report. 2004, Stanford, CA: Statistics Department, Stanford University
 8.
Wegelin J: A survey of partial least squares methods, with emphasis on the twoblock case. Technical report. 2000, Seattle: University of Washington
 9.
Lantieri F, Rydbeck H, Griseri P, Ceccherini I, Devoto M: Incorporating prior biological information in linkage studies increases power and limits multiple testing. BMC Proc. 2007, 1 (Suppl 1): S89
 10.
Wang Y, Fang Y, Wang S: Clustering and principal components approach based on heritability for mapping multiple gene expressions. BMC Proc. 2007, 1 (Suppl 1): S121
 11.
Liu Y, Duan W, Paschall J, Saccone NL: Artificial neural networks for linkage analysis of quantitative gene expression phenotypes and evaluation of gene × gene interactions. BMC Proc. 2007, 1 (Suppl 1): S47
Acknowledgements
This research was supported in part by funding from the Mathematics of Information Technology and Complex Systems (MITACS), the Natural Sciences and Engineering Research Council of Canada (NSERC), and Genome Canada through the Ontario Genomics Institute. We acknowledge helpful comments of the senior editor and two anonymous referees, which led to substantial improvement of the manuscript.
This article has been published as part of BMC Proceedings Volume 1 Supplement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci. The full contents of the supplement are available online at http://www.biomedcentral.com/17536561/1?issue=S1.
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The author(s) declare that they have no competing interests.
David Tritchler and Joseph Beyene contributed equally to this work.
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Parkhomenko, E., Tritchler, D. & Beyene, J. Genomewide sparse canonical correlation of gene expression with genotypes. BMC Proc 1, S119 (2007). https://doi.org/10.1186/175365611S1S119
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Keywords
 Singular Value Decomposition
 Canonical Correlation Analysis
 Singular Vector
 Canonical Vector
 Sparse Principal Component Analysis