http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2001402
#mHealth: Tracking Physiomes & Activity w/ Wearable Biosensors, by @SnyderShot et al http://journals.PLoS.org/plosbiology/article?id=10.1371/journal.pbio.2001402 >250K/day data pts on 43 people
Eval of Chromatin Accessibility [via #ATACSeq] in DLPFC of SCZ Cases/Ctrls, by @JulienBryois et al.
http://www.BiorXiv.org/content/early/2017/05/25/141986 List of cQTLs
WGS & Social-#Network Analysis of a TB Outbreak http://www.NEJM.org/doi/full/10.1056/NEJMoa1003176 Nice tech combo but not sure transmission & phylogeny are consistent
http://www.nature.com/nature/journal/v546/n7658/full/nature22402.html
#Zika virus evolution & spread in the Americas, by @sabeti_lab http://www.Nature.com/nature/journal/v546/n7658/full/nature22402.html #Phylogeny reconstruction of 110 new + 64 known seqs.
Zhou, J. and Troyanskaya, O.G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12, 931–934.
Predicting (& prioritizing) effects of noncoding variants w. [DeepSEA] #DeepLearning…model
https://www.Nature.com/nmeth/journal/v12/n10/full/nmeth.3547.html Trained w #ENCODE data
https://www.nature.com/ng/journal/v47/n3/full/ng.3214.html
Big Bang model of…tumor growth, v. slow #evolution under selection https://www.Nature.com/ng/journal/v47/n3/full/ng.3214.html #Cancer is born w/ key mutations all there
Andrea Sottoriva,
Haeyoun Kang,
Zhicheng Ma,
Trevor A Graham,
Matthew P Salomon,
Junsong Zhao,
Paul Marjoram,
Kimberly Siegmund,
Michael F Press,
Darryl Shibata
& Christina Curtis
Nature Genetics 47, 209–216 (2015) doi:10.1038/ng.3214
Common SNPs explain a large proportion (45%) of heritability for…height (85%)
http://www.Nature.com/ng/journal/v42/n7/abs/ng.608.html Cf 2010 GWASes could only explain 5%
Jian Yang,
Beben Benyamin,
Brian P McEvoy,
Scott Gordon,
Anjali K Henders,
Dale R Nyholt,
Pamela A Madden,
Andrew C Heath,
Nicholas G Martin,
Grant W Montgomery,
Michael E Goddard
& Peter M Visscher
Nature Genetics 42, 565–569 (2010) doi:10.1038/ng.608
QT:{{”
…conveniently implemented with a mathematically equivalent model that uses the SNPs to calculate the genomic relationship between pairs of subjects). Using this approach, we estimated the proportion of phenotypic variance explained by the SNPs as 0.45 (s.e. = 0.08, Table 1), a nearly tenfold increase relative to the 5% explained by published and validated individual SNPs
“}}
http://www.nature.com/ng/journal/v42/n7/abs/ng.608.html
Common SNPs explain a large proportion of the heritability for human height
Jian Yang,
Beben Benyamin,
Brian P McEvoy,
Scott Gordon,
Anjali K Henders,
Dale R Nyholt,
Pamela A Madden,
Andrew C Heath,
Nicholas G Martin,
Grant W Montgomery,
Michael E Goddard
& Peter M Visscher
Nature Genetics 42, 565–569 (2010) doi:10.1038/ng.608
QT:{{"
…conveniently implemented with a mathematically equivalent model
that uses the SNPs to calculate the genomic relationship between
pairs of subjects). Using this approach, we estimated the proportion
of phenotypic variance explained by the SNPs as 0.45 (s.e. = 0.08,
Table 1), a nearly tenfold increase relative to the 5% explained by
published and validated individual SNPs
"}}
Common SNPs explain a large proportion (45%) of heritability for…height (80%) http://www.Nature.com/ng/journal/v42/n7/abs/ng.608.html Vs ’10 GWAS SNPs could only expl. 5%
A #circadian gene-expr atlas in mammals by @jbhclock lab
http://www.PNAS.org/content/111/45/16219.abstract 43% of genes have a daily rhythm in at least 1 tissue [1/2]
.@jbhclock Fewest circadian genes in brain; most in liver. Perhaps this more reflects daily feeding cycle than true light-dark cycle? [2/2]
A circadian gene expression atlas in mammals: Implications for biology and medicine
Ray Zhanga,1,
Nicholas F. Lahensa,1,
Heather I. Ballancea,
Michael E. Hughesb,2, and
John B. Hogenescha,2
* Interestingly brain regions have the fewest circ genes(only ~3%), liver has most
* Diseases assoc with circadian genes correlate with NIH funding
* Genes can have up to a 6-hour phase diff. Between diff. organs (eg Vegfa betw. Heart & fat)
* 56 of the top 100 drugs incl. Top 7, targeted the product of a circadian gene. Related to the half-life of drugs.
* Could the liver genes be more reflective of feeding rhythm rather than true circadian clock.
#RNA Struc. Determinants of Optimal Codons…by MAGESeq
http://www.cell.com/cell-systems/fulltext/S2405-4712(16)30368-4 Probing effect of synonymous changes; towards a better dN/dS
RNA Structural Determinants of Optimal Codons Revealed by MAGE-Seq http://www.cell.com/cell-systems/fulltext/S2405-4712(16)30368-4