Nikhil Milind

For many traits there is a correlation between the number of duplications or loss-of-function (LoF) mutations someone carries, and their phenotype. Curiously, for most traits, these effects are aligned in the SAME direction. Why?

Past eQTL studies have mostly focused on one gene at a time. But in the human genome, genes often occur in clusters and tend to be co-regulated by shared mechanisms. So why not consider neighboring genes together when mapping eQTLs? A new preprint tests this idea and introduces…

Common variants influencing gene expression, often influence multiple genes, yet GWAS secondary analyses prioritize single causal gene identification. New method introduces a multi-gene eQTL mapping framework biorxiv.org/content/10.110… @biorxivpreprint

I'm thrilled that my lab at NYU is now supported by an NIH MIRA grant! I'm looking to hire 1-2 senior lab members (outstanding postdoc candidates or experienced staff scientists) with expertise in computational and statistical methods in human genetics or genomics. Please share!

📣New today! 📄Haplotype analysis reveals pleiotropic disease associations in the HLA region 🧑‍🤝‍🧑 @jkpritch @FinnGen_FI & colleagues cell.com/ajhg/abstract/…

Staff scientist position (computational): I am looking for a computational scientist to join my genomics lab at Stanford. They should have an outstanding skillset in ML/statistical methods for genomic applications, postdoc experience and a strong publication record. #sciencejobs

It was fun writing this short piece on the omnigenic model. Sharing it here, though it's a painful time as my family, along with millions of others, is at risk in Iran and the region.

Excited to share our latest preprint on the impact of background selection on complex trait evolution. It’s amazing to finally see one of the major projects from my PhD journey come to life. Huge thanks to @JeremyJBerg and @jnovembre for their guidance and support.

In #GENETICS, @RoshniAPatel, @spence_jeffrey_, @jkpritch, and colleagues explored the use of conditional frequency spectra to study selection on complex traits that circumvent some of the limitations of #GWAS. From our new Biobanks Series, read more: bit.ly/4gXsc1x

I’m excited to share the first manuscript out of my PhD work with @NAltemose, in which we describe FiberFold: a deep learning tool that predicts cell-type-specific and haplotype-specific 3D genome organization from a single experiment! biorxiv.org/content/10.110… 1/

Announcing a new preprint from my lab from my talented grad student Ziqi Xu! We created scGeneHE (pronounced sc-genie) to fully leverage single cell RNAseq data in the estimation of genetic heritability of gene expression. biorxiv.org/content/10.110…

Thrilled to see Evo 2 out. We trained a model across DNA from all domains of life, enabling mutation scoring, sequence embedding, and genome generation. All code, models, and data are available. Great summary thread from @pdhsu x.com/pdhsu/status/1…

Thank you Jonathan for the elegant explanation of our recent work! We dove into how we can model the gene regulatory architecture of complex traits with 1) gene effects from LoF burden tests and 2) Perturb-seq. It’s been an incredibly exciting and thought-provoking journey!

GWAS provides a unique tool in human biology as it can establish causal links from variants to trait. But interpretation is difficult as most effects flow through unobserved gene regulatory networks. Can we gain insight using modern Perturb-seq data? biorxiv.org/content/10.110…

Modern GWAS can identify 1000s of hits but it can be hard to turn this into biological insight. What key cellular functions link genetic variation to disease? I'm excited to present our new work combining associations + Perturb-seq to build interpretable causal graphs! A🧵

Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq biorxiv.org/cgi/content/sh… #biorxiv_genetic

Super excited to share my first grad school paper! It was so much fun working on this collaborative project from day one as a rotation student in the Curtis lab! Such a joy to work on with Katie, @LiseMangiante, @CrisISotomayor, @cncurtis and @caswell_jin & thanks to the lab!

Excited to share our latest work! We find that common-variant GWAS and rare-variant burden tests prioritize genes in fundamentally different ways, with important implications. Check out Jeff’s excellent thread below👇

If you do GWAS or rare variant association studies, check out this short thread on the concept of SPECIFICITY, and why it is a major determinant of what we find:

Super excited to release this new preprint: Jeff and Hakhamanesh drill into key questions about GWAS and rare variant studies: What SNPs and genes do these discover and why? We introduce a concept called SPECIFICITY, which we show is a fundamental determinant of GWAS/RV studies

Excited to share the latest from the @MarsonLab! An investigation of context-specific gene regulation in T cells, which led to dynamic mechanisms required to control T cell rest and activation. Published today in @Nature thanks to the work of many! nature.com/articles/s4158…