Posts Tagged ‘gwas’

Investigating the case of human nose shape and climate adaptation

September 25, 2017

The case of human nose shape & climate adaptation
http://journals.PLoS.org/plosgenetics/article?id=10.1371/journal.pgen.1006616 Comparing its Qst-Fst statistic w/ that for height & skin color

QT:{{”
“To address the question of whether local adaptation to climate is responsible for nose shape divergence across populations, we use Qst–Fst comparisons to show that nares width and alar base width are more differentiated across populations than expected under genetic drift alone. To test whether this differentiation is due to climate adaptation, we compared the spatial distribution of these variables with the global distribution of temperature, absolute humidity, and relative humidity. We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may indeed have been driven by local adaptation to climate. However, we think that this is a simplified explanation of a very complex evolutionary history, which possibly also involved other non-neutral forces such as sexual selection.”
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An Expanded View of Complex Traits: From Polygenic to Omnigenic: Cell

June 19, 2017

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

Allele-specific transcription factor binding in liver and cervix cells unveils many likely drivers of GWAS signals. – PubMed – NCBI

August 7, 2016

Allele-specific TF binding in liver & cervix cells [HepG2 & HeLa] unveils many likely drivers of GWAS [SNP] signals
http://www.sciencedirect.com/science/article/pii/S0888754316300271

Genome-wide association study identifies 74 loci associated with educational attainment : Nature : Nature Publishing Group

May 21, 2016

GWAS identifies 74 loci associated w. educational attainment http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html Described in 3 pgs of main text & 146 pgs of supplement

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html

Understanding multicellular function and disease with human tissue-specific networks : Nature Genetics : Nature Publishing Group

November 28, 2015

Human tissue-specific #networks by @TroyanskayaLab
http://www.nature.com/ng/journal/v47/n6/full/ng.3259.html
Brain-specific ones & NetWAS approach for combining #GWAS genes

access all tissue networks including the brain-specific
networks at giant.princeton.edu

Searching for missing heritability: Designing rare variant association studies

October 5, 2014

Searching for missing heritability… rare variant association studies http://www.pnas.org/content/111/4/E455.abstract Pessimistic on #RVAS in #noncoding regions

Nice overview of study design. Good journal-club material.

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).

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Primate Transcript and Protein Expression Levels Evolve Under Compensatory Selection Pressures

December 6, 2013

Primate Transcript and Protein #Expression Levels Evolve Under Compensatory #Selection Pressures: [Protein]<[mRNA]
https://www.sciencemag.org/content/342/6162/1100

AJHG – General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

June 17, 2013

http://www.cell.com/AJHG/abstract/S0002-9297(13)00223-1

Rare Gene Mutation Is Found to Stave Off Alzheimer’s – NYTimes.com

July 15, 2012

http://www.nytimes.com/2012/07/12/health/research/rare-gene-mutation-is-found-to-stave-off-alzheimers.html?pagewanted=all