| 1. |
- Do, Ron, et al.
(författare)
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Common variants associated with plasma triglycerides and risk for coronary artery disease
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:11, s. 1345-
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Tidskriftsartikel (refereegranskat)abstract
- Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 x 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
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| 2. |
- Willer, Cristen J., et al.
(författare)
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Discovery and refinement of loci associated with lipid levels
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:11, s. 1274-1283
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Tidskriftsartikel (refereegranskat)abstract
- Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 x 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.
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| 3. |
- Nalule Muhumuza, Rebecca, et al.
(författare)
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Determining Influential Factors in Spatio-temporal Models
- 2020
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Ingår i: Demography of Population Health, Aging and Health Expenditures. - Cham : Springer International Publishing. - 9783030446956 - 9783030446949 ; , s. 347-357
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Bokkapitel (refereegranskat)abstract
- In various areas of modern statistical applications such as in Environmetrics, Image Processing, Epidemiology, Biology, Astronomy, Industrial Mathematics, and many others, we encounter challenges of analyzing massive data sets which are spatially observable, often presented as maps, and temporally correlated. The analysis of such data is usually performed with the goal to obtain both the spatial interpolation and the temporal prediction. In both cases, the data-generating process has to be fitted by an appropriate stochastic model which should have two main properties: (i) it should provide a good fit to the true underlying model; (ii) its structure could not be too complicated avoiding considerable estimation error that appears by fitting the model to real data. Consequently, achieving the reasonable trade-off between the model uncertainty and the parameter uncertainty is one of the most difficult questions of modern statistical theory.We deal with this problem in the case of general spatio-temporal models by applying the LOESS predictor for both the spatial interpolation and the temporal prediction. The number of closest neighboring regions to be used in its construction is determined by cross-validation. We also discuss the computational aspects in the case of large-dimensional data and apply the theoretical findings to real data consisting of the number of influenza cases observed in the south of Germany.
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