raungar.bsky.social
Postdoc at @StanfordBioethx
Genetics PhD @StanfordMed, BS @UArkansas
elsi / rna-seq / rare disease / multi-omics / chronic illness / x-chromosome
42 posts
191 followers
225 following
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This was the first project I started when I joined the Montgomery lab over five years ago, and I really grew up scientifically with this project. I am extra lucky that Stephen developed this project specifically with my personal scientific interests in mind.
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Many thanks to my second author Taibo Li, who was wonderful to work with and even helped finish this project even after returning to the MD side of his MD-PhD. I am also grateful for the mentorship of @sbmontgom.bsky.social throughout this project.
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We saw that many of these sex-biased variants were enriched for being in motifs that recruited transcription factors with known sex biases.
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We show these sex-biased variants are enriched for being in pharmacogenes with adverse-drug reactions, including those with known sex-biased adverse drug reactions. To my knowledge no study had previously looked at pharmacogenetic implications of sex-biased rare variants.
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Sex differences are often overstated, so I want to be clear. Males and females are more similar than they are different. We identified just 0.04% of total rare variants to have a difference by sex.
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We (Taibo Li) then trained a Bayesian model to predict variants with different functional effects by sex. We identified 753 variants with a predicted sex bias across 464 genes.
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We then wanted to understand the functional impact of rare variants, both with and without sex-stratification. In males, but not females, we found that rare variants had a stronger functional impact when on the X-chromosome as compared to the autosomes.
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This project actually started as one trying to understand the impact of sex-stratification on expression outlier discovery. We saw a small change (0.1-0.2% genes changed outlier status), but one that indicated a reduction in false outliers.
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For legal documents and physical spaces the new administration is changing things to be “designated by sex and not identity”. This of course has loads of ethical, legal, and social implications.
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~1% of people do not have all four of these categories match in the traditional binary way, they are intersex. This means you know someone who is intersex, and you yourself might be intersex and not know it. Many folks do not find out until they are having fertility issues, or might never find out.
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(2) How do we define sex in biology? It is by definition multifactorial with four categories: sex chromosomes, internal genitalia, external genitalia, and sex hormones.
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Declaring there are two sexes does not change the complexity of human biology. People simply do not fit into two discrete categories, and here's why.
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(1c) Another example of why this definition doesn’t make sense is that ~1 in 80,000 people have Swyer syndrome, aka have XY chromosomes but have traditionally female external genitalia and some internal genitalia like a uterus. Folks with this syndrome produce no reproductive cells.
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(1b) By this definition, to be defined as male or female one must produce a reproductive cell. That means people who are postmenopausal, who are not producing reproductive cells, are neither male nor female. This of course makes no sense.
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(1a) This phrasing “at conception”, when there is just a single cell, is especially odd. There can be no reproductive cells, because there is just one cell type.
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To “biologically” define males and females, the administration refers to “at conception, the sex that produces the [large/small] reproductive cell”. Let’s break down (1) this definition, and (2) how we define sex in biology.
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Please let us know if you have any questions, feedback, or comments! (8/8)
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This work was done extremely closely with Pagé Goddard, with huge help from Tanner Jensen and Fabien Degalez and Stephen Montgomery, along with the a wonderful team including Devon Bonner, Kevin Smith, Jon Bernstein, & @mwheelermd.bsky.social (7/8)
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We also provide a resource (Supplementary Table 1) in which you can find your favorite gene, and see if it is impacted by genome build. (6/8)
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Finally, we look into the impact on clinically-relevant genes. We identified 341 rare disease & 68 cancer genes with changes between builds. In the top transcriptome-guided candidate lists we found annotation-specific genes and solved genes with rank differences between builds. (5/8)
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We then investigate how build impacts detection of outliers. Most outlier status is consistent between builds, but we do see that the amount gene’s quantification changes between builds is correlated with the average z-score change. (4/8)
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For this framework, we identify differences between the hg19 and hg38 build (hg19:hg38) and hg38 and chm13 build (hg38:chm13). Across numerous metrics, we see that the hg38:chm13 comparisons display more differences than the hg19:hg38 comparisons. (3/8)
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As a framework, we identify three ways in which build can impact results. This can be due to (1) a gene is only annotated in a given build, or a gene is annotated in both builds but (2) quantified at different levels between the builds, or (3) quantified in only one of builds. (2/8)
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yes! sorry still figuring this platform out, meant to tag you on the earlier thread! 😅
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If you’re a Stanford trainee, this course is being offered again this spring – look out for GENE 220! (9/9)
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Finally, this was a HUGE effort with help from: @daphmarts @Pyke_26 Alvina Adimoelja, @MeenaChakrabor7 @danjcotter @malikafreund @page_goddard Justin Gomez-Stafford @EmilyMGreenwald Emily Higgs @nhen_hunter Tim MacKenzie & Anjali Narain (& more - see acknowledgements!) (8/9)
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We want to recognize this course made possible due to the support of our advisors, as well as funding from @StanfordEthics and the Stanford Genetics Department, which allowed us to pay ourselves and the TAs. (7/9)
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We’re so proud of this course: it’s a community-driven solution to an institutional problem, and we hope that it can serve as a model for other institutions and/or scientific disciplines. (6/9)
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We share data-driven strategies and suggestions, and our full curriculum is available on our website: stanford-genethics.github.io! Whether you’re looking to build a similar course or just interested in some reading material, we hope this resource will be helpful. (5/9)
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We focus on topics most relevant for human geneticists including eugenics, community engaged research, race & ancestry, sex & gender, forensic genetics, and reproductive genetics - but this course can be adapted for other disciplines/formats (journal clubs, workshops, etc). (4/9)
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In response to this gap we observed, we created a course that prioritizes student-led discussions and self-reflection, ultimately giving students the tools to reason through some of these big questions. (3/9)