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Discovering gene-environment interactions with Lasso
KTH, School of Electrical Engineering and Computer Science (EECS). KTH, Centres, Science for Life Laboratory, SciLifeLab. Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden ; Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden ; .ORCID iD: 0000-0002-0749-9903
Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden ; Unit of Genomics of Complex Diseases, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.
Cardiovascular Medicine Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS).
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-250728OAI: oai:DiVA.org:kth-250728DiVA, id: diva2:1313583
Note

QC 20190507

Available from: 2019-05-03 Created: 2019-05-03 Last updated: 2019-05-07Bibliographically approved
In thesis
1. Statistical methods for detecting gene-gene and gene-environment interactions in genome-wide association studies
Open this publication in new window or tab >>Statistical methods for detecting gene-gene and gene-environment interactions in genome-wide association studies
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Despite considerable effort to elucidate the genetic architecture of multi-factorial traits and diseases, there remains a gap between the estimated heritability (e.g., from twin studies) and the heritability explained by discovered genetic variants. The existence of interactions between different genes, and between genes and the environment, has frequently been hypothesized as a likely cause of this discrepancy. However, the statistical inference of interactions is plagued by limited sample sizes, high computational requirements, and incomplete knowledge of how the measurement scale and parameterization affect the analysis.

This thesis addresses the major statistical, computational, and modeling issues that hamper large-scale interaction studies today. Furthermore, it investigates whether gene-gene and gene-environment interactions are significantly involved in the development of diseases linked to atherosclerosis. Firstly, I develop two statistical methods that can be used to study of gene-gene interactions: the first is tailored for limited sample size situations, and the second enables multiple analyses to be combined into large meta-analyses. I perform comprehensive simulation studies to determine that these methods have higher or equal statistical power than contemporary methods, scale-invariance is required to guard against false positives, and that saturated parameterizations perform well in terms of statistical power. In two studies, I apply the two proposed methods to case/control data from myocardial infarction and associated phenotypes. In both studies, we identify putative interactions for myocardial infarction but are unable to replicate the interactions in a separate cohort. In the second study, however, we identify and replicate a putative interaction involved in Lp(a) plasma levels between two variants rs3103353 and rs9458157. Secondly, I develop a multivariate statistical method that simultaneously estimates the effects of genetic variants, environmental variables, and their interactions. I show by extensive simulations that this method achieves statistical power close to the optimal oracle method. We use this method to study the involvement of gene-environment interactions in intima-media thickness, a phenotype relevant for coronary artery disease. We identify a putative interaction between a genetic variant in the KCTD8 gene and alcohol use, thus suggesting an influence on intima-media thickness. The methods developed to support the analyses in this thesis as well as a selection of other prominent methods in the field is implemented in a software package called besiq.

In conclusion, this thesis presents statistical methods, and the associated software, that allows large-scale studies of gene-gene and gene-environment interactions to be effortlessly undertaken.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 56
Series
TRITA-EECS-AVL ; 2019:46
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-250730 (URN)978-91-7873-189-3 (ISBN)
Public defence
2019-05-28, Fire, Science for Life Laboratory, Tomtebodavägen 23A, Solna, 10:00 (English)
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Supervisors
Note

QC 20190507

Available from: 2019-05-07 Created: 2019-05-03 Last updated: 2019-05-07Bibliographically approved

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Frånberg, Mattias
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School of Electrical Engineering and Computer Science (EECS)Science for Life Laboratory, SciLifeLabSeRC - Swedish e-Science Research Centre
Bioinformatics (Computational Biology)

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