Archive for the 'SciLit' Category

Genomics is failing on diversity : Nature News & Comment

June 24, 2019

Analysis of discrete local variability and structural covariance in macromolecular assemblies using cryo-EM and focused classification

June 21, 2019

Single-protein detection in crowded molecular environments in cryo-EM images | eLife

June 21, 2019

A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens. – PubMed – NCBI

June 15, 2019


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

GTEx somatic mosaicism from RNA-Seq in Science

June 8, 2019

CACNA1C: Association With Psychiatric Disorders, Behavior, and Neurogenesis | Schizophrenia Bulletin | Oxford Academic

May 19, 2019

Calcium Voltage-Gated Channel Subunit Alpha1 C

Single-cell genomics of autism

May 18, 2019

H3K27ac, H3K4me1, and H3K4me3 for (healthy) individuals > 300

May 3, 2019

Genetic susceptibility to lung cancer and co-morbidities

April 26, 2019

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).

Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology | Nature Genetics

April 21, 2019

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)