Archive for the ‘SciLit’ Category

Parental influence on human germline de novo mutations in 1,548 trios from Iceland. – PubMed – NCBI

Monday, August 19th, 2019

https://www.ncbi.nlm.nih.gov/pubmed/28959963

Genome-wide analysis of polymerase III-transcribed Alu elements suggests cell-type-specific enhancer function.

Sunday, August 18th, 2019

https://genome.cshlp.org/content/early/2019/08/14/gr.249789.119.long

A structural transition in physical networks | Nature

Monday, August 12th, 2019

https://www.nature.com/articles/s41586-018-0726-6

A structural transition in physical networks

Nima Dehmamy, Soodabeh Milanlouei & Albert-László Barabási

Naturevolume 563, pages676–680 (2018)

Quantifying the impact of public omics data.

Sunday, August 11th, 2019

similar idea to quantifying the value of the data
https://www.ncbi.nlm.nih.gov/pubmed/31383865

GTEx somatic mosaicism from RNA-Seq in Science

Saturday, June 8th, 2019

https://science.sciencemag.org/content/364/6444/eaaw0726

Genetic susceptibility to lung cancer and co-morbidities

Friday, April 26th, 2019

https://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).
“]]

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

Monday, April 22nd, 2019

https://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

Sunday, April 14th, 2019

https://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…

Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia | Nature Communications

Sunday, April 7th, 2019

https://www.nature.com/articles/s41467-018-05379-y

Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. – PubMed – NCBI

Sunday, April 7th, 2019

https://www.ncbi.nlm.nih.gov/pubmed/30545852
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