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Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
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2013 (English)In: PLoS ONE, ISSN 1932-6203, Vol. 8, no 11, e80080- p.Article in journal (Refereed) Published
Abstract [en]

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological mechanisms and may also strengthen the basis for risk assessment and prevention.

Place, publisher, year, edition, pages
2013. Vol. 8, no 11, e80080- p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-213817DOI: 10.1371/journal.pone.0080080ISI: 000327311900057OAI: oai:DiVA.org:uu-213817DiVA: diva2:683601
Available from: 2014-01-05 Created: 2014-01-04 Last updated: 2015-01-22Bibliographically approved
In thesis
1. Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
Open this publication in new window or tab >>Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As we gain increased understanding of genetic disorders and gene regulation more focus has turned towards complex interactions. Combinations of genes or gene and environmental factors have been suggested to explain the missing heritability behind complex diseases. Furthermore, gene activation and splicing seem to be governed by a complex machinery of histone modification (HM), transcription factor (TF), and DNA sequence signals. This thesis aimed to apply and develop multivariate machine learning methods for use on such biological problems. Monte Carlo feature selection was combined with rule-based classification to identify interactions between HMs and to study the interplay of factors with importance for asthma and allergy.

Firstly, publicly available ChIP-seq data (Paper I) for 38 HMs was studied. We trained a classifier for predicting exon inclusion levels based on the HMs signals. We identified HMs important for splicing and illustrated that splicing could be predicted from the HM patterns. Next, we applied a similar methodology on data from two large birth cohorts describing asthma and allergy in children (Paper II). We identified genetic and environmental factors with importance for allergic diseases which confirmed earlier results and found candidate gene-gene and gene-environment interactions.

In order to interpret and present the classifiers we developed Ciruvis, a web-based tool for network visualization of classification rules (Paper III). We applied Ciruvis on classifiers trained on both simulated and real data and compared our tool to another methodology for interaction detection using classification. Finally, we continued the earlier study on epigenetics by analyzing HM and TF signals in genes with or without evidence of bidirectional transcription (Paper IV). We identified several HMs and TFs with different signals between unidirectional and bidirectional genes. Among these, the CTCF TF was shown to have a well-positioned peak 60-80 bp upstream of the transcription start site in unidirectional genes.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 69 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1167
Histone modification, Transcription factor, Transcriptional regulation, Next-generation sequencing, Feature selection, Machine learning, Rule-based classification, Asthma, Allergy
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
urn:nbn:se:uu:diva-230159 (URN)978-91-554-9005-8 (ISBN)
Public defence
2014-10-03, BMC C8:301, Husargatan 3, Uppsala, 13:15 (English)
Available from: 2014-09-12 Created: 2014-08-19 Last updated: 2015-01-22

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Bornelöv, SusanneMoghadam, Behrooz TorabiKomorowski, Jan
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