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- Frånberg, Mattias, 1985-
(författare)
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Statistical methods for detecting gene-gene and gene-environment interactions in genome-wide association studies
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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- Grüning, Björn, et al.
(författare)
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Bioconda: A sustainable and comprehensive software distribution for the life sciences
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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- Muhammad, Sayyed Auwn, 1980-, et al.
(författare)
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Species tree aware simultaneous reconstruction of gene and domain evolution
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Most genes are composed of multiple domains with a common evolutionary history that typically perform a specific function in the resulting protein. As witnessed by many studies of key gene families, it is important to understand how domains have been duplicated, lost, transferred between genes, and rearranged. Similarly to the case of evolutionary events affecting entire genes, these domain events have large consequences for phylogenetic reconstruction and, in addition, they create considerable obstacles for gene sequence alignment algorithms, a prerequisite for phylogenetic reconstruction.We introduce the Domain-DLRS model, a hierarchical, generative probabilistic model containing three levels corresponding to species, genes, and domains, respectively. From a dated species tree, a gene tree is generated according to the DL model, which is a birth-death model generalized to occur in a dated tree. Then, from the dated gene tree, a pre-specified number of dated domain trees are generated using the DL model and the molecular clock is relaxed, effectively converting edge times to edge lengths. Finally, for each domain tree and its lengths, domain sequences are generated for the leaves based on a selected model of sequence evolution.For this model, we present a MCMC based inference framework called Domain-DLRS that as input takes a dates species tree together with a multiple sequence alignment for each domain family, while it as output provids an estimated posterior distribution over reconciled gene and domain trees. By requiring aligned domains rather than genes, our framework evades the problem of aligning genes that have been exposed to domain duplications, in particular non-tandem domain duplications. We show that Domain-DLRS performs better than MrBayes on synthetic data and that it outperforms MrBayes on biological data. We analyse several zinc-finger genes and show that most domain duplications have been tandem duplications, of which some have involved two or more domains, but non-tandem duplications have also been common, in particular in gene families of complex evolutionary history such as PRDM9.
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- Tofigh, Ali, et al.
(författare)
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Detecting LGTs using a novel probabilistic modelintegrating duplications, LGTs, losses, rate variation,and sequence evolution
- 2009
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The debate over the prevalence of lateral gene transfers (LGTs) has been intense.There is now to a large extent consensus around the view that LGT is an important evolutionary force as well as regarding its relative importance across species. This consensus relies, however, mainly on studies of individual gene families.Up until now, the gold standard for identifying LGTs has been phylogenetic methods where LGTs are inferred from incongruities between a species tree andan associated gene tree. Even in cases where there is evidence of LGT, several concerns have often been raised regarding the significance of the evidence. One common concern has been the possibility that other evolutionary events have caused the incongruities. Another has been the significance of the gene trees involved in the inference; there may for instance be alternative, almost equally likely, gene trees that do not provide evidence for LGT. Independently of these concerns, there has been a need for methods that can be used to quantitatively characterize the level of LGT among sets of species, but also for methods able to pinpoint where in the species tree LGTs have occurred.Here, we provide the first probabilistic model capturing gene duplication, LGT,gene loss, and point mutations with a relaxed molecular clock. We also provide allfundamental algorithms required to analyze a gene family relative to a given speciestree under this model. Our algorithms are based on Markov chain Monte Carlo(MCMC) methodology but build also on techniques from numerical analysis and involve dynamic programming (DP).
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