80 research outputs found
Fitness effects of new mutations and adaptive evolution in house mice
No full textKnowledge of the distribution of fitness effects of new mutations (DFE) can enable
us to quantify the amount of genetic change between species that is driven by natural
selection and contributes to adaptive evolution. The primary focus of this thesis is the
study of methods to infer the DFE and the study of adaptive evolution in the house
mouse subspecies Mus musculus castaneus.
Firstly, I extended previous methodology to model the DFE based on
polymorphism data. Methods that have previously been used to infer the DFE from
polymorphism data have relied on the assumption of a unimodal distribution. I
developed new models that can be used to fit DFEs of arbitrary complexity, and
found that multimodality can be detected by these models given enough data. I used
these new models to analyse polymorphism data from Drosophila melanogaster and
M. m. castaneus, and found evidence for a unimodal DFE for D. melanogaster and a
bimodal DFE for M. m. castaneus.
Secondly, I investigated the contribution of change in coding and non-coding
DNA to evolutionary adaptation. I used a polymorphism dataset of ~80 loci from M.
m. castaneus sequenced in 15 individuals to investigate selection in protein-coding
genes and putatively regulatory DNA close to these genes. I found that, although
protein-coding genes are much more selectively constrained than non-coding DNA,
they experience similar rates of adaptive substitution. These results suggest that
change in functional non-coding DNA sequences might be as important as
protein-coding genes to evolutionary adaptation.
Thirdly, I used whole genome data from 10 M. m. castaneus individuals to
compare the rate of adaptive substitution in autosomal and X-linked genes. I found
that, on average, X-linked genes have a 1.8 times faster rate of adaptive substitution
than autosomal genes. I also found that faster-X evolution is more pronounced for
male-specific genes. I used previously developed theory to show that these
observations can be explained if new advantageous mutations are recessive, with an
average dominance coefficient less than or equal to 0.25. These results can help to
explain the long-studied phenomenon of the large effect of the X chromosome in
speciation
GenOMICC WGS summary statistics
No full textGWAS summary statistics from 7,491 critically ill patients from COVID-19 and 48,400 population controls: European(EUR) 5,989/42,891; South Asian(SAS) 788/3,793; African(AFR) 440/1,350; East Asian(EAS) 274/366. GWAS models were calculated with SAIGE using a logisitic mixed-model regression. A trans-ancestry meta-analysis was performed using inverse-variant weighted fixed-effects. Ancestry-specific and trans-ancestry summary statistics are available with MAF>0.01. TWAS was performed using GTEx v8 gene expression data for lung and blood and an all-tissue meta-analysis. Summary statistics for tissue-specific and meta-TWAS are availablePairo-Castineira, Erola; Baillie, J; Kousathanas, Athanasios; Caufield, Mark J.. (2022). GenOMICC WGS summary statistics, 2020-2021 [dataset]. University of Edinburgh. Roslin Institute. https://doi.org/10.7488/ds/3411
Whole-genome sequencing reveals host factors underlying critical COVID-19
No full textCritical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.</p
Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - BayesR+ GWAS Proteins
No full textThis dataset represents one of five datasets which correspond to the study: "Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults". These datasets represent association studies on the levels of the same set of 70 inflammatory proteins. Each dataset represents one of five distinct methods used to perform genome-wide and epigenome-wide association studies on these protein levels. These methods are: Linear Regression GWAS, Linear Regression EWAS, OSCA EWAS, BayesR+ GWAS and BayesR+ EWAS. These analyses were performed as part of the Lothian Birth Cohort 1936 Study. This data relates to summary statistics for GWAS of 70 Olink inflammation proteins - performed by BayesR+.Hillary, Robert; Trejo Banos, Daniel; Kousathanas, Athanasios; McCartney, Daniel; Harris, Sarah; Stevenson, Anna; Patxot, Marion; Ojavee, Sven Erik; Zhang, Qian; Liewald, David; Ritchie, Craig; Evans, Kathryn; Tucker-Drob, Elliot; Wray, Naomi; McRae, Allan; Visscher, Peter; Deary, Ian; Robinson, Matthew; Marioni, Riccardo. (2020). Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - BayesR+ GWAS Proteins, [dataset]. University of Edinburgh. Centre for Cognitive Ageing and Cognitive Epidemiology. https://doi.org/10.7488/ds/285
Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - Linear Regression EWAS Proteins
No full textThis dataset represents one of five datasets which correspond to the study: "Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults". These datasets represent association studies on the levels of the same set of 70 inflammatory proteins. Each dataset represents one of five distinct methods used to perform genome-wide and epigenome-wide association studies on these protein levels. These methods are: Linear Regression GWAS, Linear Regression EWAS, OSCA EWAS, BayesR+ GWAS and BayesR+ EWAS. These analyses were performed as part of the Lothian Birth Cohort 1936 Study. This data relates to summary statistics for EWAS of 70 Olink inflammation proteins - performed by OLS regression EWAS.Hillary, Robert; Trejo Banos, Daniel; Kousathanas, Athanasios; McCartney, Daniel; Harris, Sarah; Stevenson, Anna; Patxot, Marion; Ojavee, Sven Erik; Zhang, Qian; Liewald, David; Ritchie, Craig; Evans, Kathryn; Tucker-Drob, Elliot; Wray, Naomi; McRae, Allan; Visscher, Peter; Deary, Ian; Robinson, Matthew; Marioni, Riccardo. (2020). Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - Linear Regression EWAS Proteins, [dataset]. University of Edinburgh. Centre for Cognitive Ageing and Cognitive Epidemiology. https://doi.org/10.7488/ds/281
Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - Linear Regression GWAS Proteins
No full textThis dataset represents one of five datasets which correspond to the study: "Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults". These datasets represent association studies on the levels of the same set of 70 inflammatory proteins. Each dataset represents one of five distinct methods used to perform genome-wide and epigenome-wide association studies on these protein levels. These methods are: Linear Regression GWAS, Linear Regression EWAS, OSCA EWAS, BayesR+ GWAS and BayesR+ EWAS. These analyses were performed as part of the Lothian Birth Cohort 1936 Study. This data relates to summary statistics for GWAS of 70 Olink inflammation proteins - performed by OLS regression GWAS.Hillary, Robert; Trejo Banos, Daniel; Kousathanas, Athanasios; McCartney, Daniel; Harris, Sarah; Stevenson, Anna; Patxot, Marion; Ojavee, Sven Erik; Zhang, Qian; Liewald, David; Ritchie, Craig; Evans, Kathryn; Tucker-Drob, Elliot; Wray, Naomi; McRae, Allan; Visscher, Peter; Deary, Ian; Robinson, Matthew; Marioni, Riccardo. (2020). Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - Linear Regression GWAS Proteins, [dataset]. University of Edinburgh. Centre for Cognitive Ageing and Cognitive Epidemiology. https://doi.org/10.7488/ds/281
Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - BayesR+ EWAS Proteins
No full textThis dataset represents one of five datasets which correspond to the study: "Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults". These datasets represent association studies on the levels of the same set of 70 inflammatory proteins. Each dataset represents one of five distinct methods used to perform genome-wide and epigenome-wide association studies on these protein levels. These methods are: Linear Regression GWAS, Linear Regression EWAS, OSCA EWAS, BayesR+ GWAS and BayesR+ EWAS. These analyses were performed as part of the Lothian Birth Cohort 1936 Study. This data relates to summary statistics for EWAS of 70 Olink inflammation proteins - performed by BayesR+.Hillary, Robert; Trejo Banos, Daniel; Kousathanas, Athanasios; McCartney, Daniel; Harris, Sarah; Stevenson, Anna; Patxot, Marion; Ojavee, Sven Erik; Zhang, Qian; Liewald, David; Ritchie, Craig; Evans, Kathryn; Tucker-Drob, Elliot; Wray, Naomi; McRae, Allan; Visscher, Peter; Deary, Ian; Robinson, Matthew; Marioni, Riccardo. (2020). Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - BayesR+ EWAS Proteins, [dataset]. University of Edinburgh. Centre for Cognitive Ageing and Cognitive Epidemiology
Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - OSCA EWAS Proteins
No full textThis dataset represents one of five datasets which correspond to the study: "Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults". These datasets represent association studies on the levels of the same set of 70 inflammatory proteins. Each dataset represents one of five distinct methods used to perform genome-wide and epigenome-wide association studies on these protein levels. These methods are: Linear Regression GWAS, Linear Regression EWAS, OSCA EWAS, BayesR+ GWAS and BayesR+ EWAS. These analyses were performed as part of the Lothian Birth Cohort 1936 Study. This data relates to summary statistics for EWAS of 70 Olink inflammation proteins - performed by OSCA EWAS.Hillary, Robert; Trejo Banos, Daniel; Kousathanas, Athanasios; McCartney, Daniel; Harris, Sarah; Stevenson, Anna; Patxot, Marion; Ojavee, Sven Erik; Zhang, Qian; Liewald, David; Ritchie, Craig; Evans, Kathryn; Tucker-Drob, Elliot; Wray, Naomi; McRae, Allan; Visscher, Peter; Deary, Ian; Robinson, Matthew; Marioni, Riccardo. (2020). Multi-method genome and epigenome wide studies of inflammatory protein levels in healthy older adults - OSCA EWAS Proteins, [dataset]. University of Edinburgh. Centre for Cognitive Ageing and Cognitive Epidemiology. https://doi.org/10.7488/ds/281
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