Posts Tagged ‘ncvarg’

2016 News Feature: NIH supports new approaches to discovering DNA differences in the genome’s regulatory regions that affect disease – National Human Genome Research Institute (NHGRI)

October 7, 2016

https://www.genome.gov/27566612/2016-news-feature-nih-supports-new-approaches-to-discovering—dna-differences-in-the-genomes-regulatory-regions-that-affect-disease-/

Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay: Cell

August 18, 2016

Identification of…expr.-Modulating Variants using #MPRA, by @sabeti_lab http://www.cell.com/cell/fulltext/S0092-8674(16)30421-4 Some w. allelic skew related to PWM change

Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. – PubMed – NCBI

February 15, 2016

Massively parallel quantification of…regulatory effects of #noncoding…variation in a…cohort
http://genome.cshlp.org/content/25/8/1206.long new popstarr assay

http://www.ncbi.nlm.nih.gov/pubmed/26084464
Genome Res. 2015 Aug;25(8):1206-14. doi: 10.1101/gr.190090.115. Epub 2015 Jun 17.
Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.
Vockley CM1, Guo C2, Majoros WH3, Nodzenski M4, Scholtens DM4, Hayes MG5, Lowe WL Jr5, Reddy TE6.

“The Race” to Clone BRCA1

April 25, 2015

The Race to Clone #BRCA1 http://www.sciencemag.org/content/343/6178/1462.abstract
Lessons on #LOF mutations, synthetic lethality, silly gene names & the 2-hit hypothesis

synthetic lethality (PARP inhibitors), gene names (RING fingers)

From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus : Nature : Nature Publishing Group

March 23, 2015

From noncoding variant to phenotype…at…#cholesterol locus http://www.nature.com/nature/journal/v466/n7307/full/nature09266.html
Gold standard ex of #SNP functional effect: LDL changes

Kiran Musunuru,
Alanna Strong,
Maria Frank-Kamenetsky,
et al.

Nature 466, 714–719 (05 August 2010) doi:10.1038/nature09266

Changes LDL level

PLOS Genetics: A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations

February 7, 2015

Massively Parallel Pipeline to Clone DNA Variants & Examine…Disease
Mutations http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004819 CloneSeq leverages NextGen sequencing

With the advance of sequencing technologies, tens of millions of genomic variants have been discovered in the human population. However, there is no available method to date that is capable of determining the functional impact of these variants on a large scale, which has increasingly become a huge bottleneck for the development of population genetics and personal genomics. Clone-seq and comparative interactome-profiling pipeline is a first to address this issue.

Can be coupled to many readouts.

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR. Pooled association tests for rare variants in exon-resequencing studies. American Journal of Human Genetics (2010) 86: 832-838.

February 1, 2015

Pooled association tests for rare variants in exon-resequencing http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073 Simulation shows advantage of mult. rarity thresholds

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ,
Sunyaev SR. Pooled association tests for rare variants in
exon-resequencing studies. American Journal of Human Genetics (2010)
86: 832-838.

SUMMARY

Multiple studies indicate strong association between rare variants and
resulting phenotype. This paper describes a population-genetics
simulation framework to study the influence of variant allele
frequency on the corresponding phenotype. In a prior study, causal
relationship between variants and phenotype was resolved by performing
association test on set of variants having allele frequency below a
fixed threshold. However, here it is observed that simulation
frameworks based on a variable allele frequency threshold provide
higher accuracy in association test compared to the fixed allele
frequency model. In addition, inclusion of predicted functional
effects of variants (Polyphen-2 scores) increases the accuracy of the
variable frequency threshold model. Overall, this paper describes a novel methodology, which can be
used to explore the association between rare variants and various
diseases.

NOT-OD-15-032: Update: New Biographical Sketch Format Required for NIH and AHRQ Grant Applications Submitted for Due Dates on or After May 25, 2015

January 11, 2015

http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-032.html

CRE-Seq paper…

November 24, 2014

High-throughput functional testing of ENCODE segmentation
predictionsGenome Res. October 2014 24: 1595-1602; Published in Advance July 17, 2014,

that can be used for training predicted enhancers

http://genome.cshlp.org/content/24/10/1595.full.pdf

Mapping rare and common causal alleles for complex human diseases

February 1, 2014

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).

“}}