linkstream2 microblog

http://www.gigasciencejournal.com/content/1/1/4

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The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify
cell-type-specific and ubiquitous enhancers.
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A promoter-level mammalian expression atlas
http://www.nature.com/nature/journal/v507/n7493/full/nature13182.html

An atlas of active enhancers across human cell types and tissues http://www.nature.com/nature/journal/v507/n7493/full/nature12787.html

het/hom ratio = ~1.5
in the sequencing of many Asian individuals (Table 1)

http://www.nature.com/ng/journal/v43/n8/fig_tab/ng.872_T1.html

From
Extensive genomic and transcriptional diversity identified through massively parallel DNA and RNA sequencing of eighteen Korean individuals
Nature Genetics 43, 745-752 (2011) doi:10.1038/ng.872

Big success for an NHGRI program

http://www.nature.com/news/technology-the-1-000-genome-1.14901

Nature’s Second Act: Nice overview of the resurgence in
#pharmaceutical discovery from meta & microbial #genomics
http://cen.acs.org/articles/91/i35/Natures-Second-Act.html

Contains information on analysis pipelines and datasets produced from Broad’s Firehose. Access to the protected pages requires an NCI login.

https://confluence.broadinstitute.org/display/GDAC/Home

PARADIGM-SHIFT predicts… function of mutations in… #cancers using pathway[s]. #Network-based gene prioritization
http://bioinformatics.oxfordjournals.org/content/28/18/i640

Integrating genotype from many callers & indication of where they differ. Might be useful for the personal diploid genome.
http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2835.html

Mapping rare & common causal alleles for complex human diseases: great primer, describing yin & yang of #RVAS v #GWAS
http://www.cell.com/retrieve/pii/S0092867411010695

Found this a very illuminating primer, particularly relevant to understanding rare variants.

Soumya Raychaudhuri
Cell. 2011 September 30; 147(1): 57-69.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198013/

Some particularly useful quoted snippets below.

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De novo mutations occurring spontaneously in individuals are constantly and rapidly introduced into any population. …Most of these mutations are quickly filtered out or lost by genetic drift and will never achieve appreciable allele frequencies. I illustrate this concept by a simulation in which de novo neutral mutations (conferring no effect on fitness) are introduced into a population of 2,000 diploid individuals. In 31 generations 95% of these mutations disappear from the general population, and not one of these mutations achieves an allele frequency of >1% in 200 generations (see Figure S1).
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Common variant associations to phenotype are often facile to find. Their high frequencies allow case-control studies to be adequately powered to detect even modest effects. Their high r2 to other proximate common variants allows for association signals to be discovered by genotyping the marker directly, or other nearby correlated markers. But mapping those associated variants to the specific variant that functionally influence disease risk can be challenging since the statistical signals invoked by inter-correlated variants are difficult to disentangle.

On the other hand, individual rare variant associations are
challenging to find. Their low frequency renders current cohorts underpowered to detect all but the strongest effects, and lack of correlation to other markers often prevents them from being picked up by a standard genotyping marker panels. But, once a rare associated variant is identified, mapping the causal rare variants is relatively facile since recent ancestry is likely to limit the number of inter-correlated markers.
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For rare variant associations, the field has not yet defined accepted standards for statistical significance that account for the burden of multiple hypothesis testing. Since there are many more rare variants than common ones, and they are not typically inter-correlated with each other, a more stringent threshold may be necessary than applied for common variants. One conservative approach is to correct for the total number of bases genome-wide, ie p=0.05/3000000000 ~ 10-11 as a significance threshold.
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If a genomic region is critical to disease pathogenesis rare mutations may modulate disease susceptibility. Then many affected individuals may have rare mutations more frequently in that region, though the mutations may be different from and unrelated to one another. This concept has sparked interest in the genetics community, and workers in statistical genetics have devised strategies to examine rare variants in aggregate across a target region (Bansal et al., 2010). These “burden” tests assess if rare variants within a specific region are distributed in a non-random way, suggesting that they might be playing a roll in disease pathogenesis (see Figure 3B).

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Nice piece on #SingleCell Seq w/ implications for #cancer, neurosci, &c. Singled out for #sequencing
http://www.nature.com/nmeth/journal/v11/n1/full/nmeth.2768.html HT @naivelocus

Lots on brain, cancer & prenatal sequencing, viz:

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For example, as part of the Single Cell Analysis Program supported by the US National Institutes of Health Common Fund, Kun Zhang’s team will generate full transcriptomes from 10,000 cells in three areas of the human cortex. They will group the transcripts into cell
types—perhaps redefining those cell types in the process—and map the transcripts back to cortical slices of the brain. Single-cell RNA-seq itself is no longer a barrier. “If you have a good cell, and you want to get a measure of the transcriptome, there is more than one option that can lead you to that goal,” Zhang says. In general, however, extracting the neurons posthumously, minimizing RNA degradation and preserving some of the neuronal spatial information is challenging, and the group is evaluating several approaches, Zhang says.
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