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| 2. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 3. |
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| 4. |
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| 5. |
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| 6. |
- Aragam, KG, et al.
(author)
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Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants
- 2022
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:12, s. 1803-1815
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Journal article (peer-reviewed)abstract
- The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR–Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.
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| 7. |
- Aragam, KG, et al.
(author)
-
Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants
- 2022
-
In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:12, s. 1803-1815
-
Journal article (peer-reviewed)abstract
- The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR–Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.
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| 8. |
- Blacker, CJ, et al.
(author)
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EAAT2 as a Research Target in Bipolar Disorder and Unipolar Depression: A Systematic Review
- 2020
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In: Molecular neuropsychiatry. - : S. Karger AG. - 2296-9209. ; 5:Suppl 1, s. 44-59
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Journal article (peer-reviewed)abstract
- Glutamate is implicated in the neuropathology of both major depressive disorder and bipolar disorder. Excitatory amino acid transporter 2 (EAAT2) is the major glutamate transporter in the mammalian brain, removing glutamate from the synaptic cleft and transporting it into glia for recycling. It is thereby the principal regulator of extracellular glutamate levels and prevents neuronal excitotoxicity. EAAT2 is a promising target for elucidating the mechanisms by which the glutamate-glutamine cycle interacts with neuronal systems in mood disorders. Forty EAAT2 studies (published January 1992–January 2018) were identified via a systematic literature search. The studies demonstrated that chronic stress/steroids were most commonly associated with decreased EAAT2. In rodents, EAAT2 inhibition worsened depressive behaviors. Human EAAT2 expression usually decreased in depression, with some regional brain differences. Fewer data have been collected regarding the roles and regulation of EAAT2 in bipolar disorder. Future directions for research include correlating EAAT2 and glutamate levels<i></i>in vivo, elucidating genetic variability and epigenetic regulation, clarifying intracellular protein and pharmacologic interactions, and examining EAAT2 in different bipolar mood states. As part of a macromolecular complex within glia, EAAT2 may contribute significantly to intracellular signaling, energy regulation, and cellular homeostasis. An enhanced understanding of this system is needed.
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| 9. |
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| 10. |
- de Jong, S, et al.
(author)
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Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder
- 2018
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In: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 1, s. 163-
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Journal article (peer-reviewed)abstract
- Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.
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