Jennifer Asimit

Assistant Professor @MRC_BSU @Cambridge_Uni

[Jennifer Asimit @JennAsimit]

Excited to share our latent factor approach for high-dimensional trait #GWAS + #finemapping out in @CellGenomics
https://t.co/DKfSmAVDME

Uses summary-level data.
Software: https://t.co/L4D0Hs7r0Q

@FZ_Cambridge @WilliamAstle @aidanbutty
@MRC_BSU @CAMBRIDGE_CEU

[Cell Genomics @CellGenomics]

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits https://t.co/MXxrZKj7VU

[Veera Rajagopal  @doctorveera]

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
https://t.co/X9Z2joGLgv

[Jennifer Asimit @JennAsimit]

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: https://t.co/6mK1iBiUPt

[Communications Biology @CommsBio]

Adjusting for and quantifying environmental heterogeneity in the meta-analysis of genome-wide association studies of diverse populations identifies additional heterogeneity beyond ancestral effects. https://t.co/HvQ0dWhk1p

[Jennifer Asimit @JennAsimit]

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

[Jennifer Asimit @JennAsimit]

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

https://t.co/GxJDrghRXL

[Jennifer Asimit @JennAsimit]

Try our env.MRmega R package at https://t.co/UApO3elKUP.

For input, only need GWAS summary statistics and study-level covariates, e.g. mean BMI, urban proportion.

[Jennifer Asimit @JennAsimit]

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

https://t.co/GXg52UNq2R

[stephen o'rahilly (its pronounced O-RA-hill-EEE) @StephenORahilly]

Nice paper from @GenesHealth, an increasingly valuable resource

[Christopher Kintu @Chriskintu2021]

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

[Jennifer Asimit @JennAsimit]

R package for flashfmZero, bundled with updated flashfm functions (correlated traits) available at:

https://t.co/srxAWSS4DC

[Jennifer Asimit @JennAsimit]

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

[bioRxiv Genetics @biorxiv_genetic]

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits https://t.co/aDvR8BfRTV #biorxiv_genetic

[Jennifer Asimit @JennAsimit]

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

[MRC Biostatistics Unit @MRC_BSU]

"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 👇
https://t.co/qEF7ZfNKKH

#GetIntoSTEM @Cambridge_Uni

[Marios Georgakis @MariosGeorgakis]

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❗️

https://t.co/Yy5lLBXojU

[Jennifer Asimit @JennAsimit]

@InesBarroso4 @RJStrawbridge Congrats @JanaSoenksen!

[Jennifer Asimit @JennAsimit]

Try our env.MRmega R package at https://t.co/UApO3elKUP.

For input only need GWAS summary statistics and study-level covariates, e.g. mean BMI, urban proportion.

[Jennifer Asimit @JennAsimit]

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

[bioRxiv Genetics @biorxiv_genetic]

Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies https://t.co/YmvWbbN9lh #biorxiv_genetic

[Jennifer Asimit @JennAsimit]

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