Jennifer Asimit

Excited to share our latent factor approach for high-dimensional trait #GWAS + #finemapping out in @CellGenomics doi.org/10.1016/j.xgen… Uses summary-level data. Software: github.com/jennasimit/fla… @FZ_Cambridge @WilliamAstle @aidanbutty @MRC_BSU @CAMBRIDGE_CEU

This went live today, on Thanksgiving, which feels quietly special. This article is about one of my favorite topics: how human genetics is being used to shape drug development. It’s a synthesis of ideas and lessons that have emerged over many years, brought together into a single narrative. I’m grateful for the opportunity to give structure to these thoughts — and excited about where human genetics–driven drug development is headed next. The future here feels genuinely promising. Many thanks to the editors at @WorksInProgMag — @bswud and @salonium — for the opportunity and for helping edit this article into its best possible version. Hope you enjoy this light read on Thanksgiving Day :) 🧬 Nature’s laboratory worksinprogress.co/issue/natures-…

Looking forward to the BioInference conference in Bardonecchia, Italy on May 28-30, 2025. It will be three exciting days of talks and networking! Abstract submission deadline for oral/poster presentations is January 31, 2025. See details here: bioinference.github.io/2025/

Adjusting for and quantifying environmental heterogeneity in the meta-analysis of genome-wide association studies of diverse populations identifies additional heterogeneity beyond ancestral effects. doi.org/10.1038/s42003…

The number of variants prioritised for having high marginal posterior probability (MPP) of causality significantly increased by including annotations, with further gains by combining annotations with multi-trait fine-mapping Resolution followed this same improvement pattern

New preprint - for #GWAS loci associated with >1 glycaemic traits, multi-trait #finemapping (flashfm) + functional annotations improved resolution @JanaSoenksen, Ji Chen, Arushi Varshney, Susan Martin, MAGIC @InesBarroso4, Andrew Morris, Stephen Parker biorxiv.org/cgi/content/sh…

Try our env.MRmega R package at SiruRooney.github.io/env.MRmega/. For input, only need GWAS summary statistics and study-level covariates, e.g. mean BMI, urban proportion.

Delighted to share that our environment-adjusted meta-analysis of #GWAS from diverse populations (env-MR-MEGA), is now in @CommsBio Amazing teamwork with @SiruRooney @SolaOjewunmi @ab_kamiza, Michele Ramsay, Andrew Morris, @tchikowore1 @SFatumo @MRC_BSU rdcu.be/d0hIh

Nice paper from @GenesHealth, an increasingly valuable resource

Close these dates off your schedule. 03-07th, Feb 2025. It's AfSHG in Uganda. See you there!

R package for flashfmZero, bundled with updated flashfm functions (correlated traits) available at: jennasimit.github.io/flashfmZero/

In SMIM1, flashfmZero produces single-variant 99% credible sets (CS99) - rs1175550 - for each of three latent factors related to red blood cell traits CS99 for latent factors were 5-27 variants CS99 for red blood cell traits were 30-58 variants

New preprint - genetic analyses of latent factors from high-dimensional traits give enhanced power for #GWAS discovery and #finemapping Highest gains are through multi-trait fine-mapping of latent factors - we introduce flashfmZero @FZ_Cambridge, @WilliamAstle, @aidanbutty

"My time at the BSU was incredible. I learnt so much and have a newfound appreciation for the integral role of statistics in medical research." Such a privilege to host work experience students this summer 🤩 Read more 👇 mrc-bsu.cam.ac.uk/news/inspiring… #GetIntoSTEM @Cambridge_Uni

I'm often asked about the latest GWAS datasets for different cardiovascular traits🧬 🔗This is my list with links to the most recent and largest publicly available GWAS summary statistics for cardiometabolic traits❗️ docs.google.com/spreadsheets/d…

@InesBarroso4 @RJStrawbridge Congrats @JanaSoenksen!

Well done Dr Soenksen!! Privileged to be part of your journey and looking forward to the next chapter!! Thanks to examiners ⁦@RJStrawbridge⁩ James Wakefield and Gaynor Smith!!

Try our env.MRmega R package at SiruRooney.github.io/env.MRmega/. For input only need GWAS summary statistics and study-level covariates, e.g. mean BMI, urban proportion.

In our analysis of LDL in 12 sex-stratified African GWAS of ~19k individuals, adjusting for sex, BMI, and urban status, we identified additional heterogeneity beyond ancestral effects for nine variants - examples for sex (b) and urban status (c) below

Happy to share env-MR-MEGA - first in our suite of environment-adjusted #GWAS methods Use summary-level data to quantify environmental and ancestral heterogeneity Amazing teamwork with @SiruRooney, @SolaOjewunmi, @ab_kamiza, Michele Ramsay, Andrew Morris, @tchikowore1, @SFatumo