RT @muntakim_rafi: 0/ Essential reading for anyone training or using sequence-function models trained on genomic sequences! 🚨 In our new pr….

10/ It’s time to shift how we think about variants – instead of associating each variant with a discrete impact on disease risk, they instead have context-dependent distributions of effects. For more details – and more examples! – check out our preprint!

9/ Similarly, the dependence on polygenic background means that selection coefficients for the same variant vary across populations, time, individuals, and environments – instead of a single value, they may be better represented as a distribution.

8/ Because the same variant has different phenotypic consequences in different genetic backgrounds, it could persist in low-risk backgrounds indefinitely, while being strongly selected against in individuals with high polygenic risk!.

7/ Polygenic background also changes how selection acts! More alleles contributing to a trait widens the genetic risk distribution, increasing selective pressure as individuals are pushed to extremes. Highly polygenic traits lead to have smaller, more uniform effect sizes.

6/ What about in the clinic? Genetic testing for rare variants could be complimented by PGS info to better stratify patient risk. This would help prioritize those at highest risk of disease for early intervention while providing peace of mind to those at lower risk.

5/ For researchers: selecting cell lines with an appropriate genetic background may be as crucial as choosing the right cell type when characterizing disease-associated variants. Low-risk backgrounds could mask variant effects that would be revealed in other genetic contexts!.

4/ Disease prevalence scales non-linearly with disease liability. A 1-unit increase in PGS leads to very different changes in disease prevalence depending on whether it occurs in a low-risk (green) or high-risk (red) genetic background. Same variant, totally different outcomes!

3/ That’s a pretty straightforward observation, but it has widespread consequences for experimental design, clinical genetics, and evolution. The key idea is that you cannot fully understand a variant’s effect without considering the genetic background it occurs in. Here’s why:.

2/ Rare and common genetic variants both shape human disease risk, but researchers often focus on one or the other. A n increasing number of studies show that their effects are additive: rare variant + common variants = log odds ratio for disease.

1 / Interested in how genetic variants influence complex traits? In our new preprint, we show that variant effects depend on the polygenic background in which they occur, and why it is critical to consider polygenic background when studying complex traits.

RT @baym: <chefs kiss>.

RT @ElowitzLab: Hard to believe it’s almost 2 decades since I was served, and later consumed, this molecularly precise model of a gram nega….

RT @griffiemma: Is curating, validating and transforming microbial contextual data bringing you down? Give the DataHarmonizer a try. You ca….

The building I work in has been leaving puzzles out for researchers to work on during downtime. I just stayed late to put the last few pieces of one together and I feel so mischievous. People will come to work tomorrow and be like "Wow who finished the puzzle" . It was meeee 🕵️‍♀️.

I made it 🎉

RT @ParafilmM: P-value now refers to the amount of Parafilm used in an experiment. Pass it on.

RT @Nature: Sign up for Nature Briefing for a chance to win a Nature-branded toy to celebrate the Year of the Tiger: .

As always, I'm massively grateful to @UoPeople for enabling me to complete my second bachelor's degree while working full-time. This made my grad school applications so much more competitive, and it wouldn't have happened if my only options were traditional universities. ❤️.

Big exciting news!! I've been accepted into the MSc in Bioinformatics program at @UBC and I CANNOT WAIT to get started! 🎉.