Jonathan Pritchard
@jkpritch · Stanford University · joined 25 Apr 2012
My lab at Stanford studies human population genetics and complex traits.
15 233Followers
342Following
1 847Posts total
788KViews on collected posts
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New preprint alert: we use sign errors as a test of how well TWAS works.
Very worryingly we find that TWAS gets the sign wrong around 1/3 of the time (compared to 50% for pure guessing). You can read more about our analysis here, and what we think is going on 👇
10.6K views · 55 likes
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How well does TWAS estimate a gene’s direction of effect on a trait? We think of this as an important stress-test for the accuracy of TWAS.
In a new pre-print with @PGerlach98341, Jeff Spence, and @jkpritch, we find that TWAS gets the sign wrong around 20-30% of the time!
1/n
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I'm delighted to announce our new preprint on genome-scale perturb-seq in CD4 T cells. We learned general lessons about the power of perturb-seq, and specific lessons in T cell biology
Led by amazing postdocs Ronghui Zhu and Emma Dann with my wonderful collaborator Alex Marson
14K views · 108 likes
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Our latest preprint revisits the classic model of mutation-selection balance. Do human recessive genes fit Haldane's 100-year old model?
This work is by the wonderful Jon Judd, and co-mentored by Jeff Spence.
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@jkpritch Wow. I'm really looking forward to reading this. Thanks for the effort you put into this. There has been a need for a text book like this one.
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... including especially @molly_przew and @DocEdge85 who both heroically read this twice and gave me many helpful comments; also our many close collaborators and lab members who have been wonderful partners in science, and who have also taught me much over the years.
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and Part 4 will cover the genetic basis of trait variation. This will have some basic chapters for students on GWAS, etc, but I also plan to emphasize the links between human popgen and statgen, and argue that neither can be understood without the other. https://t.co/qaNHPvSWc6
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I have many many people I could thank, but I'll limit this to @Anna_DiRienzo (this is motivated in part by a course that we co-taught from 2001-2013); @molly_przew from whom I have learned so much on all these topics; also many people who commented on earlier drafts...
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Part 3 of the book focuses on human history. I've already written rough drafts of these chapters, so I expect to release them in the coming months. https://t.co/867qcReH7A
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and a summary of what we know about different types of selection in human genomes https://t.co/5lP9WgrzZE
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... as well as the wonderful PRDM9 story, and a bit on haplotype models: https://t.co/usHvde32Ve
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Chapter 2.4 tackles models of structure in population genetics (we defer PCA and Structure/Admixture to Part 3 which I'll release later): https://t.co/xwQMaufk9W
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The last three chapters of Part 2 of the book develop models of selection, with an emphasis on the interplay of selection and drift: https://t.co/dP3jLiXnmh
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We cover recombination and LD in depth, as it's often difficult to get intuition for these topics: https://t.co/UawaUYsMl0
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Part 2 of the book tackles human population genetics. Some themes are familiar from other popgen texts, but I try to focus on building intuition and human examples in many cases. Here's a bit about the difference between forward-in-time and backward-in-time models: https://t.co/T
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Part 1 of the book includes an overview of human genomics; an introduction to human variation; modern DNA sequencing; and an introduction to human mutation. My undergrads are reading the variation chapter for tomorrow's lecture: https://t.co/o5OL20WOW4
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Another goal is to show how quantitative thinking and theory can illuminate interpretation of biological data. Here's a reference page about useful numbers for human genome data: https://t.co/h2qGK9Z5dL
5.3K views · 62 likes
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I also tackle human mutation, but with more of an evolutionary and human genetics focus than most other introductory materials: https://t.co/vriz5SwLoq
2.9K views · 22 likes
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I'm delighted to release the first half of my new open-access online textbook in human population genetics:
https://t.co/GHMPCTv6BL https://t.co/Zlpls3qm3A
683.4K views · 3.6K likes
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The book aims to provide a unified view of human variation, population genetics, human history and trait genetics / GWAS
I wrote this partly because I find a lack of suitable readings for teaching, and partly to connect the popgen and statgen world views in an accessible format
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Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
2K1 Oct
2.3K
2.6K
2.1K
1.9K
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2K
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2.3K2 Oct
14.6K13 Dec
14K5 Jan
17.9K6 Jan
10.6K
2.6K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.88%1 Oct
0.98%
1.00%
1.05%
1.34%
0.86%
1.09%
0.92%
0.60%2 Oct
0.83%13 Dec
0.91%5 Jan
0.38%6 Jan
0.74%
0.70%
Reactions — likes, reposts, replies and quotes — divided by views.
What the audience does
Likes57.4%4 363 in total
Reposts15.5%1 181 in total
Replies1.2%88 in total
Bookmarks25.9%1 970 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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