Matthew D. Brennan, Raymond Cheong, and Andre Levchenko
Science. 2012 October 19; 338(6105): 10.1126/science.1227946. doi: 10.1126/science.1227946
PMCID: PMC3820285
NIHMSID: NIHMS512743
How Information Theory Handles #Cell Signaling & Uncertainty… really well since it’s ideal for noisy communication
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3820285/?report=classic
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.
QT:{{”
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).
…
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.
…
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.
…
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).
“}}
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:
QT:{{”
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.
“}}
Quite relevant to #transcriptome changes over #development: Earliest Transcribed… Genes Are Short, Newly Evolved…
http://www.cell.com/cell-reports/fulltext/S2211-1247(13)00788-2
The draft #genome of sweet orange: Nearly 30K genes in only ~370 Mb + #RNAseq to find key Vitamin C genes
http://www.nature.com/ng/journal/v45/n1/full/ng.2472.html
The authors present a draft genome of sweet orange (Citrus sinensis) which covers 87.3% of the relatively compact orange genome
(approximately 367 Mb). Self-alignment of the citrus genome sequences identified one ancient triplication event, which was shared with a number of diverse plants including Arabidopsis thaliana, and no recent whole genome duplication events partially explaining the compact size of its genome. A combination of short sequence repeat (SSR) and SNP markers revealed that sweet orange is an interspecific hybrid between pummelo and mandarin (1:3 in genome composition with female of pummelo origin). Characterization of the unique protein coding genes in the citrus genome and the transcriptome analysis (RNA-Seq and RNA-PET) derived from different tissues in the citrus plant were used to identify the specific genes that are involved in the accumulation of Vitamin C in its fruit (the rate limiting GalUR in the galacturonate pathway is present in 12 copies which are developmentally regulated). Overall, the genome has almost 30,000 genes.
The draft genome of sweet orange (Citrus sinensis).
Xu Q, Chen LL, …., Ruan Y.
Nat Genet. 2013 Jan;45(1):59-66.
PMID: 23179022
QT:{{”
…Recurrent mutations in the potassium channel gene KCNJ5 that result in cell depolarization and Ca(2+) influx cause ∼40% of these tumors. We identified 5 somatic mutations (4 altering Gly403 and 1 altering Ile770) in CACNA1D, encoding a voltage-gated calcium channel, among 43 APAs without mutated KCNJ5…. We also identified de novo germline mutations at identical positions in two children with a previously undescribed syndrome featuring primary aldosteronism and neuromuscular abnormalities….
“}}
http://www.ncbi.nlm.nih.gov/pubmed/23913001
Nat Genet. 2013 Sep;45(9):1050-4. doi: 10.1038/ng.2695. Epub 2013 Aug 4.
Somatic and germline CACNA1D calcium channel mutations in
aldosterone-producing adenomas and primary aldosteronism.
Scholl UI, Goh G, Stölting G, de Oliveira RC, Choi M, Overton JD, Fonseca AL, Korah R, Starker LF, Kunstman JW, Prasad ML, Hartung EA, Mauras N, Benson MR, Brady T, Shapiro JR, Loring E, Nelson-Williams C, Libutti SK, Mane S, Hellman P, Westin G, Åkerström G, Björklund P, Carling T, Fahlke C, Hidalgo P,Lifton RP.
QT:{{”
We discovered abundant (12 to 24%) monoallelic expression of autosomal genes and that expression of the two alleles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal X chromosome.
“}}
linkstream2 microblog 