| 1. |
- Henningsson, Susanne, 1977, et al.
(författare)
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Sex steroid-related genes and male-to-female transsexualism
- 2005
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Ingår i: Psychoneuroendocrinology. - Oxford : Pergamon Press. - 0306-4530 .- 1873-3360. ; 59:5, s. 412-412
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Tidskriftsartikel (refereegranskat)abstract
- Transsexualism is characterised by Lifelong discomfort with the assigned sex and a strong identification with the opposite sex. The cause of transsexualism is unknown, but it has been suggested that an aberration in the early sexual differentiation of various brain structures may be involved. Animal experiments have revealed that the sexual differentiation of the brain is mainly due to an influence of testosterone, acting both via androgen receptors (ARs) and-after aromatase-catalyzed conversion to estradiol-via estrogen receptors (ERs). The present study examined the possible importance of three polymorphisms and their pairwise interactions for the development of male-to-female transsexualism: a CAG repeat sequence in the first exon of the AR gene, a tetra nucleotide repeat polymorphism in intron 4 of the aromatase gene, and a CA repeat polymorphism in intron 5 of the ER beta gene. Subjects were 29 Caucasian male-to-female transsexuals and 229 healthy mate controls. Transsexuals differed from controls with respect to the mean Length of the ER repeat polymorphism, but not with respect to the length of the other two studied polymorphisms. However, binary logistic regression analysis revealed significant partial effects for all three polymorphisms, as well as for the interaction between the AR and aromatase gene polymorphisms, on the risk of developing transsexualism. Given the small number of transsexuals in the study, the results should be interpreted with the utmost caution. Further study of the putative role of these and other sex steroid-related genes for the development of transsexualism may, however, be worthwhile.
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| 2. |
- Hägg, Sara, 1977-, et al.
(författare)
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Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2 : The Stockholm Atherosclerosis Gene Expression (STAGE) Study
- 2009
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Ingår i: PLoS Genetics. - : PLoS Genetics. - 1553-7390 .- 1553-7404. ; 5:12, s. e1000754-
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Tidskriftsartikel (refereegranskat)abstract
- Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n=16/17, P<10-27and-30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, cellular and lesion expression of LDB2, and the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and together with LDB2 merits further attention in CAD research.
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| 3. |
- Kovacs, Alexander, et al.
(författare)
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Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia
- 2007
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 104:34, s. 13768-13773
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Tidskriftsartikel (refereegranskat)abstract
- Increased baseline values of the acute-phase reactant C-reactive protein (CRP) are significantly associated with future cardiovascular disease, and some in vitro studies have claimed that human CRP (hCRP) has proatherogenic effects. In vivo studies in apolipoprotein E-deficient mouse models, however, have given conflicting results. We bred atherosclerosis-prone mice (Apob 100/100Ldlr-/-), which have human-like hypercholesterolemia, with hCRP transgenic mice (hCRP+/0) and studied lesion development at 15, 30, 40, and 50 weeks of age. Atherosclerotic lesions were smaller in hCRP+/0 Apob100/100Ldlr-/- mice than in hCRP0/0Apob100/100Ldlr-/- controls, as judged from the lesion surface areas of pinned-out aortas from mice at 40 and 50 weeks of age. In lesions from 40-week-old mice, mRNA expression levels of several genes in the proteasome degradation pathway were higher in hCRP +/0Apob100/100Ldlr-/- mice than in littermate controls, as shown by global gene expression profiles. These results were confirmed by real-time PCR, which also indicated that the activities of those genes were the same at 30 and 40 weeks in hCRP+/0Apob 100/100Ldlr-/- mice but were significantly lower at 40 weeks than at 30 weeks in controls. Our results show that hCRP is not proatherogenic but instead slows atherogenesis, possibly through proteasome-mediated protein degradation. © 2007 by The National Academy of Sciences of the USA.
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| 5. |
- Nilsson, Roland, 1977-, et al.
(författare)
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Consistent feature selection for pattern recognition in polynomial time
- 2007
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Ingår i: Journal of machine learning research. - 1532-4435 .- 1533-7928. ; 8, s. 589-612
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Tidskriftsartikel (refereegranskat)abstract
- We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.
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| 6. |
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| 7. |
- Nilsson, Roland, 1977-, et al.
(författare)
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Evaluating feature selection for SVMs in high dimensions
- 2006
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Ingår i: MACHINE LEARNING: ECML 2006, PROCEEDINGS. - Berlin : Springer. - 0302-9743 .- 1611-3349. ; 4212, s. 719-726, s. 719-
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 8. |
- Nilsson, Roland, 1977-
(författare)
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Statistical Feature Selection : With Applications in Life Science
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The sequencing of the human genome has changed life science research in many ways. Novel measurement technologies such as microarray expression analysis, genome-wide SNP typing and mass spectrometry are now producing experimental data of extremely high dimensions. While these techniques provide unprecedented opportunities for exploratory data analysis, the increase in dimensionality also introduces many difficulties. A key problem is to discover the most relevant variables, or features, among the tens of thousands of parallel measurements in a particular experiment. This is referred to as feature selection.For feature selection to be principled, one needs to decide exactly what it means for a feature to be ”relevant”. This thesis considers relevance from a statistical viewpoint, as a measure of statistical dependence on a given target variable. The target variable might be continuous, such as a patient’s blood glucose level, or categorical, such as ”smoker” vs. ”non-smoker”. Several forms of relevance are examined and related to each other to form a coherent theory. Each form of relevance then defines a different feature selection problem.The predictive features are those that allow an accurate predictive model, for example for disease diagnosis. I prove that finding redictive features is a tractable problem, in that consistent estimates can be computed in polynomial time. This is a substantial improvement upon current theory. However, I also demonstrate that selecting features to optimize prediction accuracy does not control feature error rates. This is a severe drawback in life science, where the selected features per se are important, for example as candidate drug targets. To address this problem, I propose a statistical method which to my knowledge is the first to achieve error control. Moreover, I show that in high dimensions, feature sets can be impossible to replicate in independent experiments even with controlled error rates. This finding may explain the lack of agreement among genome-wide association studies and molecular signatures of disease.The most predictive features may not always be the most relevant ones from a biological perspective, since the predictive power of a given feature may depend on measurement noise rather than biological properties. I therefore consider a wider definition of relevance that avoids this problem. The resulting feature selection problem is shown to be asymptotically intractable in the general case; however, I derive a set of simplifying assumptions which admit an intuitive, consistent polynomial-time algorithm. Moreover, I present a method that controls error rates also for this problem. This algorithm is evaluated on microarray data from case studies in diabetes and cancer.In some cases however, I find that these statistical relevance concepts are insufficient to prioritize among candidate features in a biologically reasonable manner. Therefore, effective feature selection for life science requires both a careful definition of relevance and a principled integration of existing biological knowledge.
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