Posts Tagged ‘gwas’

New GWAS SCZ loci (nature genetics 2018)

Tuesday, March 6th, 2018

Common #schizophrenia alleles are enriched in mutation-intolerant genes & in regions under strong background selection 50 novel SCZ loci & 145 loci in total, from #GWAS – associated w/ 33 candidate causal genes

We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
Nature Genetics (2018)

Investigating the case of human nose shape and climate adaptation

Tuesday, September 26th, 2017

The case of human nose shape & climate adaptation Comparing its Qst-Fst statistic w/ that for height & skin color

“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.”

An Expanded View of Complex Traits: From Polygenic to Omnigenic: Cell

Tuesday, June 20th, 2017

Thought-provoking calculations, perhaps suggesting that ever bigger association studies won’t yield useful results

An Expanded View of Complex Traits: From Polygenic to Omnigenic

Evan A. Boyle
Yang I. Li
Jonathan K. Pritchard

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

Sunday, August 7th, 2016

Allele-specific TF binding in liver & cervix cells [HepG2 & HeLa] unveils many likely drivers of GWAS [SNP] signals

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

Saturday, May 21st, 2016

GWAS identifies 74 loci associated w. educational attainment Described in 3 pgs of main text & 146 pgs of supplement

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

Saturday, November 28th, 2015

Human tissue-specific #networks by @TroyanskayaLab
Brain-specific ones & NetWAS approach for combining #GWAS genes

access all tissue networks including the brain-specific
networks at

Searching for missing heritability: Designing rare variant association studies

Sunday, October 5th, 2014

Searching for missing heritability… rare variant association studies 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

Saturday, February 1st, 2014

Mapping rare & common causal alleles for complex human diseases: great primer, describing yin & yang of #RVAS v #GWAS

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

Soumya Raychaudhuri
Cell. 2011 September 30; 147(1): 57-69.

Some particularly useful quoted snippets below.


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).


Primate Transcript and Protein Expression Levels Evolve Under Compensatory Selection Pressures

Friday, December 6th, 2013

Primate Transcript and Protein #Expression Levels Evolve Under Compensatory #Selection Pressures: [Protein]<[mRNA]

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

Monday, June 17th, 2013