Inferring Crohn’s disease association from exome sequences by integrating biological knowledge
© The Author(s) 2016
Published: 12 August 2016
Exome sequencing has been emerged as a primary method to identify detailed sequence variants associated with complex diseases including Crohn’s disease in the protein-coding regions of human genome. However, constructing an interpretable model for exome sequencing data is challenging because of the huge diversity of genomic variation. In addition, it has been known that utilizing biologically relevant information in a rigorous manner is essential for effectively extracting disease-associated information.
In this paper, we incorporate three different types of biological knowledge such as predicted pathogenicity, disease gene annotation, and functional interaction network of human genes, and integrate them with exome sequence data in non-negative matrix tri-factorization framework. Based on the proposed method, we successfully identified Crohn’s disease patients from exome sequencing data and achieved the area under the receiver operating characteristics curve (AUC) of 0.816, while other clustering methods not using biological information achieved the AUC of 0.786. Moreover, the disease association score derived from our method showed higher correlation with Crohn’s disease genes than other unrelated genes.
As a consequence, by integrating biological information across multiple levels such as variant, gene, and systems, our method could be useful for identifying disease susceptibility and its associated genes from exome sequencing data.
The advent of high-throughput sequencing technologies has enabled determining detailed catalogues of genomic sequence variants. Especially, cost-effective exome sequencing has been emerged for extending variant association studies to include rare variants . In Crohn’s disease (CD), exome sequencing was adopted to identify the causative variants and the genes affected by them . Despite that some studies have successfully identified CD associated variants and genes [3–5], the genetic heterogeneity and environmental effects on CD still obscure the interpretation of CD exome sequencing data. Particularly, since most of pathogenic variants are enriched for rare variants , a large amount of samples more than 10,000 exome sequences are required for the association study . Furthermore, predicting disease susceptibility of exome sequence for clinical applications is still challenging.
To efficiently investigate the relationship between sequence variants and disease susceptibility, integrating variant-level and gene-level information is important . Analogously, Na et al.  carried out ranking susceptible diseases for personal genome sequence by comparing gene-level pathogenicity vectors derived from genome sequence variants and disease-gene association knowledge, respectively.
In this study, we predict CD susceptibility from 56 exome sequences by integrating biological knowledge described at variant-level, gene-level, and systems-level. For the integrative analysis, we adopt the computational framework called non-negative matrix tri-factorization (NMTF) [10, 11], and introduce the constraints for deriving biologically relevant solution. This approach distinguishes the exomes of CD patients, and simultaneously prioritizes the corresponding CD associated genes. This unique feature could be beneficial for clinical applications based on personal genome interpretation.
We obtained exome sequencing data from the Crohn’s disease challenge of CAGI 2011 (https://genomeinterpretation.org). The purpose of the CAGI challenge was to distinguish exomes of Crohn’s patients and healthy individuals. The data is formatted in a variant call format (VCF), and the exome samples are randomly numbered. Besides the exome sequences, any other information is not given. The exomes were obtained from 56 individuals, consisting of 42 patients with Crohn’s disease and 14 healthy individuals. From the exome sequences, a total of 155,019 coding DNA sequence variants, resulting in 1202 nonsense, 79,448 nonsynonymous, and 74,577 synonymous mutations, are identified. For the present work, we used the nonsynonymous mutations of 33,948 amino acid substitutions of 11,435 human genes.
To distinguish Crohn’s disease patients from the exomes, we utilized various biological information. First, pathogenicity of amino acid substitutions predicted using PolyPhen-2 . Second, knowledge on disease-related genes was collected from DGA database . We obtained 189 genes associated with Crohn’s disease (DOID: 8778) on March 2013. Third, knowledge on functional interactions between human genes was collected from HumanNet . We downloaded a functional gene network from the HumanNet website, and selected only the genes corresponding to the genes of the above exome data set. Consequently, 151,440 gene-gene interactions of 9597 human genes were obtained.
Non-negative matrix tri-factorization
Number of amino acid substitutions
Number of exomes
Number of genes
Number of known Crohn’s disease genes
Exome matrix (n×m)
Pathogenicity of amino acid substitutions (n×l)
Disease-gene association matrix (l×2)
Disease-exome association matrix (2×m)
Gene-gene interaction network (l×l)
Pathogenicity prediction by PolyPhen-2 (n×l)
Annotated Crohn’s disease-gene association matrix (l×2)
Indicator vector for known Crohn’s disease genes (l)
Crohn’s disease association vector derived from W (l)
Constraints for integrating biological knowledges
To find the optimal solution for the objective function, we used the multiplicative update algorithm , because it is simple to implement and usually performs well. Our optimization algorithm is described in Algorithm 1. The algorithm initializes the factorized matrices P, W, and H with random non-negative values. Then, each matrix is iteratively updated with fixing the other matrices, until the algorithm converges. Since the multiplicative update algorithm achieves a local optimum, we repeated the computation 100 times with different initial matrices, and selected 30 solutions with smallest squared errors. Then, the final solution was obtained by averaging them over the replicas.
Selecting NMTF models
Distinguishing Crohn’s disease patients and healthy individuals
In exome sequencing studies, SIFT  is one of the most highly used tools, as well as PolyPhen-2, to predict the functional consequences of nonsynonymous variants. Thus, we performed the NMTF analysis by replacing the predicted pathogenicity of PolyPhen-2 with that of SIFT. Because the prediction of SIFT web-server was available for 15,810 amino acid substitutions among our data set, we only used those variants. As shown in Fig. 4 b, NMTF showed better AUC of 0.806, while k-means and fuzzy clusterings show AUCs of 0.786 and 0.793, respectively. Also, the prediction values of NMTF for CD patients are significantly higher than those for healthy individuals (p-value = 4.09×10−4). Although the use of PolyPhen-2 achieved higher AUC value than the use of SIFT, it may be caused by better performance of PolyPhen-2 . As a consequence, the predicted pathogenicity utilized in NMTF framework could be derived from various predictors such as MutationTaster , FATHMM , PANTHER , GERP++ , PhyloP , and so on.
Analysis of Crohn’s disease-associated genes
To investigate the correlation of CDA score and CD gene, we collected CD genes from the DGA database on July 2015, and selected newly annotated CD genes, not used in W 0. We obtained 53 newly annotated CD genes, and examined their CDA scores in comparison with those of the other unannotated genes, as shown in Fig. 6 c. The distribution of CD genes were shifted close to 1. For the newly annotated CD genes, 15.1 % and 30.2 % of genes showed the CDA scores greater than 0.99 and 0.95, respectively. Whereas, for the other genes, only the 8.9 % and 11.6 % of genes showed the CDA scores in the same ranges, respectively. Therefore, CDA score derived by NMTF could be informative for inferring disease-gene relationship.
Discussion and conclusion
In this study, we developed a computational framework called NMTF for analyzing exome sequencing data, and integrated biological knowledge relevant to the disease susceptibility. By applying the proposed method to 56 exome sequences, we discriminated the exomes of CD patients and healthy individuals, and demonstrated the correlation between CD genes and CDA scores.
This study makes two major contributions to the exome sequencing data analysis. First, our method, in which disease-associated individuals and genes are interconnected by co-clustering, provides an interpretable analysis for cliniical decision making. For example, an additional information connecting the disease susceptibility to the evident genes can be derived. Although the compared clustering methods showed a certain degree of predictive performance for the CD data set, they lack the interpretability. On the other hand, in our method, co-clustered genes in our method could support the genetic basis determining the CD susceptibility of exomes. This would be beneficial for understanding the heterogeneity of genetic effects in genetically complex disease, and designing effective personalized treatments.
Second, we demonstrated that integrating multi-level information could be useful for understanding genetically complex diseases. Based on the NMTF framework, we combined a wide range of biological information including the predicted pathogenicity of single amino acid substitution, the annotation of disease-gene association, and the functional interaction between human genes. By doing so, we inferred the disease information from the variant-level data. Although Na et al.’s study  showed the integration of variant-level and gene-level information, their approach requires well-curated knowledge on disease-gene association. However, our approach is designed to complement imperfect prior-knowledge on disease-gene association, by using the systems-level information, functional interaction of human genes, as disease-associated genes often share common biological functions . The integration of multi-level information may be effective because CD susceptibility is affected by complicated genetic regulations and interactions. Similarly, this approach would be useful for other complex diseases in the same manner with CD.
We thank all Bioinformatics and Computational Biology Laboratory (BCBL) members for helpful discussion. We would like to thank the CAGI organizer and Andre Franke for kindly providing data on Crohn’s disease challenge. This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI12C0014).
Publication of this article has been funded by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI12C0014). The full contents of the supplement are available online https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-9-supplement-1.
Availability of data and materials
The data will not be shared because of the restriction in CAGI data use agreement (https://genomeinterpretation.org/data-use-agreement).
CSJ designed the study, implemented the methods, performed the experiments and data analysis, and drafted the manuscript. DK participated in the study design and the data analysis, and helped to draft the manuscript. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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