http://grigoriefflab.janelia.org/node/6031
https://www.sciencedirect.com/science/article/abs/pii/S0304399118302663?via%3Dihub
Archive for the 'SciLit' Category
Analysis of discrete local variability and structural covariance in macromolecular assemblies using cryo-EM and focused classification
June 21, 2019A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens. – PubMed – NCBI
June 15, 2019https://www.ncbi.nlm.nih.gov/pubmed/30612741
crisprQTL
crisprQTL mapping as a genome-wide association framework for cellular genetic screens
Molly Gasperini, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Cole Trapnell, Nadav Ahituv, Jay Shendure
doi: https://doi.org/10.1101/314344
GTEx somatic mosaicism from RNA-Seq in Science
June 8, 2019CACNA1C: Association With Psychiatric Disorders, Behavior, and Neurogenesis | Schizophrenia Bulletin | Oxford Academic
May 19, 2019Calcium Voltage-Gated Channel Subunit Alpha1 C
Genetic susceptibility to lung cancer and co-morbidities
April 26, 2019https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3804872/
QT:[[”
Genome-wide association studies (GWAS) have enabled significant progress in the past 5 years in investigating genetic susceptibility to lung cancer. Large scale, multi-cohort GWAS of mainly Caucasian, smoking, populations have identified strong associations for lung cancer mapped to chromosomal regions 15q [nicotinic acetylcholine receptor (nAChR) subunits: CHRNA3, CHRNA5], 5p (TERT-CLPTM1L locus) and 6p (BAT3-MSH5). Some studies in Asian populations of smokers have found similar risk loci, whereas GWAS in never smoking Asian females have identified associations in other chromosomal regions, e.g., 3q (TP63), that are distinct from smoking-related lung cancer risk loci. GWAS of smoking behaviour have identified risk loci for smoking quantity at 15q (similar genes to lung cancer susceptibility: CHRNA3, CHRNA5) and 19q (CYP2A6).
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Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology | Nature Genetics
April 21, 2019https://www.nature.com/articles/ng.3968
Commentary | Published: 27 October 2017
Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology
Jennifer A Brody, Alanna C Morrison, Joshua C Bis, Jeffrey R O’Connell, Michael R Brown, Jennifer E Huffman, Darren C Ames, Andrew Carroll, Matthew P Conomos, Stacey Gabriel, Richard A Gibbs, Stephanie M Gogarten, Namrata Gupta, Cashell E Jaquish, Andrew D Johnson, Joshua P Lewis, Xiaoming Liu, Alisa K Manning, George J Papanicolaou, Achilleas N Pitsillides, Kenneth M Rice, William Salerno, Colleen M Sitlani, Nicholas L Smith, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, TOPMed Hematology and Hemostasis Working Group, CHARGE Analysis and Bioinformatics Working Group, Susan R Heckbert, Cathy C Laurie, Braxton D Mitchell, Ramachandran S Vasan, Stephen S Rich, Jerome I Rotter, James G Wilson, Eric Boerwinkle, Bruce M Psaty & L Adrienne Cupples- Show fewer authors
Nature Genetics volume 49, pages1560–1563 (2017)
NEJM: Record-Breaking Performance in a 70-Year-Old Marathoner
April 13, 2019https://www.nejm.org/doi/full/10.1056/NEJMc1900771?query=featured_secondary
We determined the physiological profile of a 70-year-old male marathoner who ran the event in 2:54:23…
LDL 84mg/dL and HDL 66mg/dL, quite impressive…
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