linkstream2 microblog

Design for data sharing
http://www.Nature.com/nbt/journal/v34/n4/full/nbt.3516.html Issues in distributing mPower mobile dataset – no DAC, allowing donors to change preferences

QT:{{”
“This March, Sage Bionetworks (Seattle) began sharing curated data collected from >9,000 participants of mPower, a smartphone-enabled health research study for Parkinson’s disease. The mPower study is notable as one of the first observational assessments of human health to rapidly achieve scale as a result of its design and execution purely through a smartphone interface. To support this unique study design, we developed a novel electronic informed consent process that includes participant-determined data-sharing preferences. It is through these preferences that the new data—including self-reported outcomes and quantitative sensor data—are shared broadly for secondary analysis. Our hope is that by sharing these data immediately, prior even to our own complete analysis, we will shorten the time to harnessing any utility that this study’s data may hold to improve the condition of patients who suffer from this disease.

Turbulent times for data sharing

Our release of mPower comes at a turbulent time in data sharing. The power of data for secondary research is top of mind for many these days. Vice President Joe Biden, in heading President Barack Obama’s ambitious cancer ‘moonshot’, describes data sharing as second only to funding to the success of the effort. However, this powerful support for data sharing stands in opposition to the opinions of many within the research establishment. To wit, the august New England Journal of Medicine (NEJM)’s recent editorial suggesting that those who wish to reuse clinical trial data without the direct participation and approval of the original study team are “research parasites”. In the wake of colliding perspectives on data sharing, we must not lose sight of the scientific and societal ends served by such efforts.” “}}

A…transcriptional map of primate (macaque) #brain development http://www.Nature.com/nature/journal/vaop/ncurrent/full/nature18637.html Gene expression changes more rapidly before birth
Nature (2016) doi:10.1038/nature18637

Thought-provoking calculations, perhaps suggesting that ever bigger association studies won’t yield useful results
https://twitter.com/joe_pickrell/status/875406448716632064

http://www.cell.com/cell/abstract/S0092-8674(17)30629-3

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Evan A. Boyle
Yang I. Li
Jonathan K. Pritchard
DOI: http://dx.doi.org/10.1016/j.cell.2017.05.038

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

A Valid Alternative to #Correlation for Rel. Data
http://journals.PLoS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Illustrates how r fails on simple expression expts HT @mason_lab

https://twitter.com/mason_lab/status/870643989246074881

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
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Seq. variants influencing…TF occupancy
http://www.Nature.com/ng/journal/v47/n12/full/ng.3432.html Uses allelic analysis to develop the CATO score, how variants alter binding

QT:{{”
This approach resulted in a simple scoring scheme, termed contextual analysis of transcription factor occupancy (CATO), that provides a recalibrated probability of affecting the binding of any transcription factor, as well as a quantitatively ranked list of transcription factor families whose binding might be altered.
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https://www.ncbi.nlm.nih.gov/pubmed/7556106

https://www.ncbi.nlm.nih.gov/pubmed/9367782