Category Archives: GWAS

Citation: Martin, Alicia R., et al. bioRxiv(2019): 441261.One of the curious things about genomics is the field has exploded in the 21st century so fast, with such explosive growth and increase in power, that it is hard to keep up if you blink. The fir…

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Very interesting preprint, A global view of pleiotropy and genetic architecture in complex traits. Nothing too surprising, but worth a read. After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate […]

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Like an Old Testament prophet of yore Graham Coop has been prophesying that cryptic population stratification may be a major confounder in analyses for as long as I’ve known him with any degree of familiarity. So it’s no surprise he’s an author on one of two preprints which have rocked the genomics world: Reduced signal […]

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Last fall I talked about a preprint, Human demographic history impacts genetic risk prediction across diverse populations. It’s now published in AJHG, with the same informative title, Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Even though talked about this before, I thought it would be useful to highlight again. To recap, GWAS is […]

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Last week Luke Jostins (soon to be Dr. Luke Jostins) published an interesting paper in Nature. To be fair, this paper has an extensive author list, but from what I am to understand this is the fruit of the first author’s Ph.D. project. In any cas…

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In science, like most things, one prefers simple over complex whenever possible. You keep adding variables until the explanatory juice starts hitting diminishing marginal returns. So cystic fibrosis is due to a mutation at one gene, and the disease exp…

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PLoS Biology has four items of great interest out today:
– Synthetic Associations Created by Rare Variants Do Not Explain Most GWAS Results
– Synthetic Associations Are Unlikely to Account for Many Common Disease Genome-Wide Association Signals
– Th…

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I recall projections in the early 2000s that 25% of the American population would be employed as systems administrators circa 2020 if rates of employment growth at that time were extrapolated. Obviously the projections weren’t taken too seriously, and the pieces were generally making fun of the idea that IT would reduce labor inputs and […]

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It looks like Genomes Unzipped has their own Mortimer Adler, with an excellent posting, How to read a genome-wide association study. For those outside the biz I suspect that #4, replication, is going to be the easiest. In the early 2000s a biologist who’d been in the business for a while cautioned about reading too […]

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9/9
Razib Khan