Postdoc in the Translational Genomics of Diabetes Lab working on GWAS, function and polygenic risk. @StanfordMed formerly @ExeterMed @turinginst

Palo Alto, CA
Joined June 2017
Seth Sharp retweeted
Out today from @StanfordPeds Endocrine Fellow #HectorOrtega & @SethASharp a state of the art review on polygenic risk scores (#PRS) in diabetes. This is your 101 on what we currently know about them and their application to understanding disease heterogeneity and clinical translation. @StanfordPeds @StanfordPedEndo @StanfordEndo @NIDDKgov Read here for free - rdcu.be/eprel 1/4
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Pleased to share our new preprint medrxiv.org/content/10.1101/… - Genetically confirmed MODY have high T2D risk ~ 24% of phenotypic variance due to common genetic changes - polygenic background alters diabetes risk 18 to 81%
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5-drug precision prescribing for people with type 2 diabetes is here - @TheLancet our @ExeterMedmodel for optimising glucose lowering therapy using low-cost routine clinical features Paper: thelancet.com/journals/lance… Web calc: diabetesgenes.org/t2-treatme…
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Thrilled to see GRS2 adopted in real world T1D screening and research studies. But clear we needed a standardized non-expert way to replicate our PRS. Great to work with @AmberMLuckett @Exeter_Diabetes @broadinstitute and @StanfordPeds colleagues to make a tool available!
Want to generate #T1DGRS in your data or data from any biobank? @SethASharp has updated his #GRS2 originally developed with @RAOram & @mnweedon - code available on @github Great collaboration with @AmberMLuckett medrxiv.org/content/10.1101/… github.com/sethsh7/prsedm @StanfordPeds @StanfordPedEndo 👇👇👇
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Proteomic predictors of individualized nutrient-specific insulin secre... sciencedirect.com/science/ar… is the latest work from star fellow (and current faculty job hunter) Dr. Jelena Kolic @JelenaK86.
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Electrophysiological characterisation of iPSC-derived human β-like cells and an SLC30A8 disease model. | Diabetes | American Diabetes Association diabetesjournals.org/diabete…
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New in #PLOSBiology, @npburtt @kjgaulton &co present the type 1 #diabetes (#T1D) Knowledge Portal, an open-access resource that aims to help address the challenge of translating #genetic discoveries into mechanistic insight @AMP_CMDKP plos.io/3YrKuQt
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Seth Sharp retweeted
Congratulations to all but especially to our very own #VarshaRajesh who won the staff technician award. We are so lucky you are in our team!!! @StanfordPedEndo
The Annual #Stanfordpeds Awards Day honored faculty & staff whose work & dedication help make the Department & @Stanford the great institution that it is today! Without their contributions, the advances of #stanfordpeds would not be possible! Congratulations!
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Our T2D genetic clustering paper is free to read in #editorspicks of @DiabetologiaJnl March issue 🧬
Up front & free to read in our March issue #editorspicks : High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease bit.ly/3QlsQtj
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NEW in @ScienceMagazine PUBLIC VIEWS ON POLYGENIC SCREENING OF EMBRYOS: We find substantial *superficial* interest in using emerging tech to increase the odds of having a child admitted to a top-100 college & conclude its time to consider regulation science.org/doi/10.1126/scie…🧵1/22
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Seth Sharp retweeted
🚨An early Christmas Present👇 Congratulations to @antje_rottner on this tour de force - despite 700M pesky human cells, a transatlantic lab move & a global pandemic she delivered 580 genes which influence how much insulin human beta-cells contain. rdcu.be/c12EO
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Seth Sharp retweeted
So excited to announce that my work with Colin Nichols during my fellowship @WUSTLPeds has finally been published! tandfonline.com/doi/figure/1…
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Correction for participation bias in the UK Biobank reveals non-negligible impact on genetic associations and downstream analyses biorxiv.org/cgi/content/shor… #biorxiv_genetic
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Seth Sharp retweeted
🚨We are delighted to share our perspective on the importance of collaborative human islet research in elucidating the pathophysiology of all forms of #diabetes which is published today in @NatMetabolism. (1/7) ➡️ nature.com/articles/s42255-0…
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One of the highlights of my PhD - getting to contribute to the discussion of polygenic prediction in mixed ancestry / ethnicity populations. The summary: for Type 1 diabetes general prediction holds up but risk distributions are different.
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And a bonus finding of this study : clustering by genetic risk in 2 dimensions (Type 1 vs Type 2 diabetes) those with autoantibodies cluster the same (high T1D risk) regardless of measures of insulin resistance (e.g. waist hip ratio).
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Highlights a particular problem - bias towards assumption of T2 from clinical measures alone, especially in non-White populations with high prevalence of T2. But genetic risk tells us T1 (or even double diabetes) is no less probable!
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