linkstream2 microblog

Links
https://linkstream2.gerstein.info/tag/i0bioit17/

Tweet
https://storify.com/markgerstein/favorite-tweets-from-bioit-17

Talk
http://lectures.gersteinlab.org/summary/Multiscale-Elt-Annotation-n-Var-Prioritization–20170524-i0bioit17/

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%

TWEETS

https://storify.com/markgerstein/favorite-tweets-from-bog-17-gp-write-ib.html

TAGGED ITEMS

https://linkstream2.gerstein.info/tag/i0gpwrite/

SLIDES

http://lectures.gersteinlab.org/summary/Scaling-Computation-to-Keep-Pace-w-Data-Gen–20170510-i0gpwrite/

TWEETS

https://storify.com/markgerstein/favorite-tweets-from-bog-17-gp-write-ib.html

TAGGED ITEMS

https://linkstream2.gerstein.info/tag/i0gpwrite/

SLIDES

http://lectures.gersteinlab.org/summary/Scaling-Computation-to-Keep-Pace-w-Data-Gen–20170510-i0gpwrite/

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.

Temporal Collateral Sensitivity in Tumor…Evolution
http://www.cell.com/cell/abstract/S0092-8674(16)30059-9 Drug-fitness landscape illuminates transiently vulnerable state

Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution

Boyang Zhao
Joseph C. Sedlak
Raja Srinivas
Pau Creixell
Justin R. Pritchard
Bruce Tidor
Douglas A. Lauffenburger
Michael T. Hemann

Genome-wide…analysis implicates miRNA dysregulation in #ASD http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html 58 diff. expr. miRNAs incl 17 strongly down in cases

http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html

QT:{{”
The miRNA expression profiles were very similar between the frontal and temporal cortex, but were distinct in the cerebellum
(Supplementary Fig. 2a–f), consistent with previous observations for mRNAs11, 12. We therefore combined 95 covariate-matched samples (47 samples from 28 ASD cases and 48 samples from 28 controls;
Supplementary Fig. 1c and Supplementary Table 1) from the FC and TC for differential gene expression (DGE) analysis, comparing ASD and CTL using a linear mixed-effects regression framework to control for potential confounders (Online Methods). We identified 58 miRNAs showing significant (false discovery rate (FDR) < 0.05) expression changes between ASD and CTL: 17 were downregulated and 41 were upregulated in ASD cortex (Fig. 1b and Supplementary Table 2). The fold changes for the differentially expressed miRNAs were highly concordant between the FC and TC (Pearson correlation coefficient R = 0.96, P < 2.2 × 10−16; Fig. 1c).
“}}

The impact of #SVs on…gene expression
http://biorxiv.org/content/early/2016/06/09/055962 24k in 147 people in GTEx pilot act as causal variants in 3-7% of ~25k eQTLs

The impact of structural variation on human gene expression

Colby Chiang, Alexandra J Scott, Joe R Davis, Emily
K Tsang, Xin Li, Yungil Kim, Farhan N Damani, Liron Ganel, GTEx Consortium, Stephen B Montgomery, Alexis Battle, Donald F Conrad, Ira M Hall
doi: https://doi.org/10.1101/055962