Jonathan Marchini

Introducing a new method called REMETA from the Regeneron Genetics Center (RGC) @RegeneronDNA for meta-analysis of gene-based tests using summary statistics rgcgithub.github.io/remeta/ This is great work from Tyler Joseph. Pre-print is here​ medrxiv.org/content/10.110…

Our paper in @NatureComms reveals how myostatin gene variants may protect muscle mass during weight loss. By studying 1M+ individuals, we found that rare MSTN variants are linked to increased skeletal muscle mass and reduced body fat. Learn more: go.nature.com/3OyD0uo

We’re announcing a new collaboration with TriNetX to support Regeneron’s drug discovery and development and advance future digital health solutions. The work expands RGC’s EHR-linked database with access to ~300M de-identified patient records. Read more: bit.ly/41bdUEE

Neurological disease risk is more common, and neurodegeneration may be detected earlier than we thought. RGC analyzed 1M+ genomes, finding repeat expansions are more widespread, and that brain changes may begin years before symptoms appear. Via @Nature: bit.ly/4tikaXH

Humans with function-disrupting variants in the myostatin gene (MSTN) have increased skeletal muscle mass and strength, and less adiposity nature.com/articles/s4146…

@KarlLandheer @hthieblot Ok, I’ll hire you ;)

@hthieblot I was ranked #1 in wow during its most competitive arena era, held #1 in wc3 ladder and am currently top 5k in dota

if you ever meet someone who at one point was globally ranked in a video game hire them immediately

It's such a painful process to upload summary statistics to GWAS catalog. Hey @GWASCatalog if you really care for open data sharing, then make the submission process simple. Get rid of globus personal connect.

The REMETA paper is now published in Nature Genetics. Great work from Tyler Joseph and team. See my previous post for a summary of the key properties of this approach x.com/marchini/statu…

7. Senior App Developer careers.regeneron.com/en/jobs/r40802 8. Physician Scientist, Translational Genetics – Immunology careers.regeneron.com/en/jobs/r40483 9. Physician Scientist, Obesity and Liver Disease Human Genetics careers.regeneron.com/en/jobs/r39319

4. Translational Human Genetics (All Phenotypes) careers.regeneron.com/en/jobs/R40970 5. Translational Genetics R-Code Developer careers.regeneron.com/en/jobs/r41614 6. Clinical Informatics careers.regeneron.com/en/jobs/r40950

A thread listing 9 open positions at @RegeneronDNA. If you are at ASHG visit our booth to find out more 1. Pipeline Development careers.regeneron.com/en/jobs/r41275 2. Proteomics Analysis/Methods careers.regeneron.com/en/jobs/r40672 3. Applied Statistical Genetics careers.regeneron.com/en/jobs/r41513

Some links to open positions in the Analytical Genetics group at @RegeneronDNA Pipeline development careers.regeneron.com/en/jobs/r41275 Proteomics analysis and methods careers.regeneron.com/en/jobs/r40672 Applied statistical genetics careers.regeneron.com/en/jobs/r41513 StatML and imaging careers.regeneron.com/en/jobs/r41506

JOB ADVERT If you work in statistical genetics/machine learning/imaging and want to join the amazing RGC team @RegeneronDN working on some of the largest and most ancestrally diverse genetic datasets in the world, then check out this role careers.regeneron.com/en/jobs/r41506/

📣Today we announced that we intend to acquire @23andMe with plans to maintain their consumer genetics business and advance our shared goal of improving human health and wellness. bit.ly/43gI75D

REGENIE v4.1 now live! github.com/rgcgithub/rege…

We’re collaborating with @truveta, @illumina, and U.S. health systems to create the largest, most-diverse genetic database of electronic health records and linked genetic data to advance scientific innovation and healthcare delivery.

Finally, for ease of use we have developed this approach in an open-source software package called REMETA github.com/rgcgithub/reme…, that is designed to integrate seamlessly with the summary statistic output files from the REGENIE software rgcgithub.github.io/regenie/

Fourth, we have extended the approach to handle binary trait meta-analysis of gene-based tests with high case-control imbalance and show that this is well calibrated.

Third, p-values alone are not sufficient for follow up interpretation of gene-based tests. So we developed a very accurate approximate method for calculating allele frequencies, genotype counts and effect size of burden tests from summary statistics.

Second, we have developed a compact per-chromosome binary file format for the LD files. The format handles both marginal and conditional testing scenarios and is indexed to allow fast access to the LD information of any gene.

The approach is accurate when compared to existing approaches to meta-analysis that do not use summary stats. For some tests like variance component and ACATV tests it can lead to more power as it avoids having to meta-analyze p-values.

REMETA solves these challenges and has several nice properties. Firstly, REMETA uses a single sparse covariance reference file per study that is rescaled for each phenotype using single variant summary statistics. This can be pre-calculated once, so it massively cuts down storage

It leads to a simple 3 stage approach to meta-analysis of ExWAS.