linkstream2 microblog

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%

#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

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

Uncovering Earth’s virome
http://www.nature.com/nature/journal/v536/n7617/full/nature19094.html Meta analysis of 5Tb of extant #metagenomic sequences finds >125k partial viral genomes

David Paez-Espino, Emiley A. Eloe-Fadrosh, Georgios A. Pavlopoulos, Alex D. Thomas, Marcel Huntemann, Natalia Mikhailova, Edward Rubin, Natalia N. Ivanova & Nikos C. Kyrpides

Zhang, Bin, Chris Gaiteri, Liviu-Gabriel Bodea, Zhi Wang, Joshua McElwee, Alexei A. Podtelezhnikov, Chunsheng Zhang et al. "Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease." Cell 153, no. 3 (2013): 707-720.

Integrated systems approach identifies genetic…#networks in…Alzheimer’s http://www.Cell.com/cell/abstract/S0092-8674(13)00387-5 Determining causality from co-expression

Basset: #DeepLearning the regulatory code w/…NNs by @noncodarnia lab http://genome.cshlp.org/content/early/2016/05/03/gr.200535.115 Has score for all possible SNVs in the genome

“Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks”

Reg. variation in cplx traits by @LeonidKruglyak
http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html nice teaching figure for #eQTLs, showing how mostly cis + hotspots http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html

Identification of [872] sig. mutated regions across #cancer types http://www.nature.com/ng/journal/v48/n2/full/ng.3471.html ranges from noncoding annotations to 3D structure