Excited to share VIPerturb-seq!
New tech from my lab which aims to improve the cost, data quality, and efficiency of single-cell CRISPR screens so that they are accessible to any lab - even at genome-wide scale
Preprint and 🧵 (1/): https://t.co/m8nleniSUD
The latest issue of COB describes how AI infrastructure can create value for biotechnology beyond finding new drug candidates. Highly recommend reading if you work at the intersection of tech and life sciences
Naive population genetics question: in multi-ancestry GWAS, are ancestry-specific associations common? For example, the recent BD analysis from @PGCgenetics found EAS specific associations. Rule or exception?
@SashaGusevPosts@dgmacarthur@tuuliel
https://t.co/grKwq1WMdY
@doctorveera@sanogenetics Gotcha, thanks @doctorveera for the explanation that what is being observed is more statistical, rather than biological, in nature.
@SashaGusevPosts@PGCgenetics@dgmacarthur@tuuliel “…apparent cross-population differences in genetic effects may be largely explained by differences in allele frequencies or linkage disequilibrium with “tagging” variants rather than differences in the underlying effect sizes.”
Thanks for the great write up @SashaGusevPosts
A new paper has identified an answer to this question, in which a structural variant causes the de novo creation of a cardiomyocyte-specific enhancer, leading to ectopic expression of a potassium channel and heart disease.
@TonyZador Others have discussed grant funding as a weighted lottery (i.e. better study section score gives you more ping pong balls in the ticket tumbler)....could something similar happen for publications?
@TonyZador Their solution was to inject noise into the ordering of draft picks; a lottery that is weighted by the teams number of wins. It overall allocates resources (draft picks) where they belong but due to individual randomness keeps teams from gaming the system and losing on purpose
@OstuniLab@MolecularCell@pioneerfactors Thanks for highlighting this discussion which has a core topic (advantages / disadvantages of in vitro / in vivo studies) that is relevant beyond pioneer factors and permeates much of biology. Well written on both sides.
@GeneticsMike7@doctorveera@Nature@mnelsonxy@NatureGenet Conceptually the important genes = low effect sizes seems measurable. Practically I'm not sure how it would be done; perhaps some kind of meta study across multiple traits comparing effect size with PhyloP scores or dN/dS...something like that. Anyone tried this?
New GWAS study examines when a baby takes their first steps 🧬 ➡️ 🚼 ➡️ 🚶♀️
SNP based heritability, a lower bound of heritability calculated using only common variants, shows up at ~25%, much higher than I would have guessed.
Work led by @annagui86
This recent GWAS preprint is the first to explore genetic influences on the age at onset of walking 🚶♀️🚶, a milestone that may indicate broader (neuro)developmental delays.
Find out more⤵️
https://t.co/MkzgEtKodl
@doctorveera@Nature@mnelsonxy@NatureGenet I’m sure folks smarter than me have pondered over this before, if anyone has any thoughts please share; would love to learn more
@doctorveera@Nature@mnelsonxy@NatureGenet But on the other hand I could image a set of coding mutations that very slightly decrease their function but is not enough to be completely eliminated from the population…..in this case perhaps these low effect GWAS genes would be enriched for rare variants in disease cases