References of "Manichaikul, Ani"
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See detailPublisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.
Malik, Rainer; Chauhan, Ganesh; Traylor, Matthew et al

in Nature genetics (2019), 51(7), 1192-1193

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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See detailMultiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.
Malik, Rainer; Chauhan, Ganesh; Traylor, Matthew et al

in Nature genetics (2018), 50(4), 524-537

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454 ... [more ▼]

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy. [less ▲]

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See detailMetabolic network analysis integrated with transcript verification for sequenced genomes.
Manichaikul, Ani; Ghamsari, Lila; Hom, Erik F. Y. et al

in Nature Methods (2009), 6(8), 589-592

With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we ... [more ▼]

With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering. [less ▲]

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