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Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
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2015 (English)In: PLoS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 11, no 9, article id e1005502Article in journal (Refereed) Published
Abstract [en]

Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PRO-CARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.

Place, publisher, year, edition, pages
2015. Vol. 11, no 9, article id e1005502
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-175933DOI: 10.1371/journal.pgen.1005502ISI: 000362269000023PubMedID: 26402789Scopus ID: 2-s2.0-84943520457OAI: oai:DiVA.org:kth-175933DiVA, id: diva2:866645
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish e‐Science Research CenterSwedish Heart Lung Foundation, 20140433Magnus Bergvall FoundationStiftelsen Gamla Tjänarinnor, 2014-00090Swedish Research Council, 2013-4993
Note

QC 20151103

Available from: 2015-11-03 Created: 2015-10-26 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)
Opponent
Supervisors
Note

QC 20190507

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

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