Kiran H Kumar

@kiranhk.bsky.social

Population and statistical geneticist, postdoc @ucsf Spence lab | phd @University of Michigan where I studied genotype imputation , aDNA, coalescent theory | climbing, fiction writing, yoga

I will be talking about how we are employing the Ancestral Recombination Graph to estimate the Distribution of Fitness Effects, the mutational model of microsatellites and the impact of stabilizing selection.

Institute for Human Genetics at UCSF@ihgatucsf.bsky.social · 3d ago

We invite you to join our seminar at UCSF Mission Bay campus with @delvecchyo.bsky.social from the National Autonomous University of Mexico humangenetics.ucsf.edu/ihg-seminar-...

How accurately do our devices depict an ancient amphora? Our curator Ben Paites has inspected every version, and the results range from “solid Greek amphora” to “What in the Minecraft?!” Swipe for expert analysis, questionable handles and one surprisingly respectable 9/10. 🏺🧵

Blue graphic showing nine amphora emojis from different platforms arranged in a grid around the title: "Roman ceramic curator rates every amphora emoji". The emojis are labelled Facebook, JoyPixels, Skype, Twitter/X, Serenity OS, Google, Samsung, Microsoft and WhatsApp.

@roshnipatel.bsky.social and I wrote about using biobanks to learn about evolution, and how those findings shape interpretations of association studies. We focused on estimating evolutionary constraint and relating relating selection on variants to selection on traits and include open questions.

arXiv q-bio.PE Populations and Evolution@qbiope-bot.bsky.social · 4w ago

Jeffrey P. Spence, Roshni A. Patel: Insights into human evolution from large genetic biobanks https://arxiv.org/abs/2609.12297 https://arxiv.org/pdf/2609.12297 https://arxiv.org/html/2609.12297

The first manuscript from my postdoc is out (doi.org/10.64898/202...)! We introduce 𝐭𝐢𝐦𝐞-𝐬𝐭𝐫𝐚𝐭𝐢𝐟𝐢𝐞𝐝 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 for studying population structure change over time, with the temporal resolution of Ancestral Recombination Graphs (ARGs). Joint with @jkpritch.bsky.social and @jeffspence.github.io. 1/n

Coalescent-Based Time-Stratified Statistics Reveal Population Structure Dynamics using the Ancestral Recombination Graph

Many questions in population genetics are concerned with reconstructing evolutionary history through time, such as inferring how population structure has changed throughout the past. Yet, many existing approaches have only an implicit temporal component, using quantities such as allele frequency or haplotype length as rough proxies for age. Recent advances in the inference of Ancestral Recombination Graphs (ARGs) have made it possible to estimate the entire sequence of local genealogies along the genome. These genealogies explicitly encode how samples are related to each other at different time points in the past, enabling the inference of how population structure has changed over time. To this end, recent work has used ARGs to define time-stratified versions of widely-used population genetics summary statistics in an attempt to capture the population structure present within a particular time window. Here, we show that naive approaches result in statistics that cannot be interpreted solely in terms of the population structure present within the time window they are targeting. To address this problem, we introduce a framework of coalescent-based time-stratified statistics, which use coalescence probabilities to partition classical summary statistics into interval-specific contributions. Using coalescent simulations, we demonstrate that these statistics accurately isolate population structure at different temporal depths and avoid spurious signals. Our results highlight the necessity of integrating coalescent theory into ARG-based temporal analyses and provide a principled and practical foundation for studying the dynamics of population structure through time. ### Competing Interest Statement The authors have declared no competing interest. National Human Genome Research Institute, https://ror.org/00baak391, R01HG014005

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