Extremely happy that our work is now published in @JEvBio (society journal @eseb_org ) 🧬🐒🐀🐋. Detecting diversifying selection for a trait from within and between-species genotypes and phenotypes. https://t.co/1X3VaePeur See 🧵below:

11/11 Work with @melodie_bastian (at @LbbeLyon), @GaboriauT and @SalaminLab (@dbc_unil), reviewed by two anonymous reviewers (thanks a lot for your comments), edited by @MasahitoTsuboi

10/11 Slides are available at https://t.co/4izpKRZgdn (presented at Evolution 2024, Montreal)

9/11 Snakemake pipeline, analysis scripts and documentation are available at https://t.co/xrOs4oDQQY to replicate the study

8/11 From the field of phylogenetics, our study can be seen as an alternative to the EVE model ( https://t.co/OWKmunh8xA) for a single trait, where we set a threshold for neutral evolution by leveraging species nucleotide polymorphism and divergence.

7/11 Our study bears similarities with others' tests of selection. For example, our study can be seen as the macro-evolutionary analog of QST–FST methods to account for phylogenetic relationships between species.

6/11 For geneticists used to detect selection for a protein-coding gene, our test can be seen as analogous to the dN/dS ratio, but for a quantitative trait.

5/11 From an empirical point of view, our method required integrating genomic and trait variation. Such datasets will become more and more accessible and we showed the applicability of our method by applying it to the illustrative example of mammals brain and body mass.

3/11 We tested our statistical procedure against simulated data and showed that our test was able to correctly detect simulations under diversifying selection (ρ > 1) or under stabilizing selection (ρ < 1).

2/11 At the phylogenetic scale, trait variation between species was normalized by sequence divergence obtained from a neutral set of markers. Similarly, trait variation within species was normalized by sequence polymorphism obtained from a neutral set of markers.

1/11 In this study, we proposed a neutrality index for a quantitative trait that can be used within a statistical framework to test for selection. Our neutrality index (ρ) is calculated as the ratio of the normalized between- to within-species variation.

1/ A thread about mutation and recombination in vertebrates and what we learned by studying three-generation pedigrees of zebra finches. Take home: the remarkable similarities between birds and mammals in many properties of these fundamental processes.

This paper from Thomas Hansen argues that distinct modes of evolution at different time scales act to decouple micro- and macroevolution: https://t.co/oosW7Z02CY

Distribution theories for genetic line of least resistance and evolvability measures: Junya Watanabe https://t.co/xsXXTq6VfY

The evolution of GC-biased gene conversion by means of natural selection https://t.co/40mZSQqc8L #biorxiv_evobio

Glad to announce that the last update of OrthoMaM (v12) is now available and published in NAR! It now includes 15,868 high quality alignments of orthologous coding sequences (CDS) for up to 190 mammals! Check out the article for more information: https://t.co/BK7ZWNdfM9

PhylteR, our new tool for filtering phylogenomics datasets, is now out! https://t.co/wth1DD5rf3 PhylteR identifies with precision, from a collection of gene trees, the "outlier" sequences responsible for a lack of concordance among gene trees. How it works? A small thread 👇

Pervasive relaxed selection on spermatogenesis genes coincident with the evolution of polygyny in gorillas https://t.co/2hzI3uVQGa #biorxiv_evobio

Proud and inspiring moment to see indigenous Wixárika art (an ancient culture still thriving that has been an important personal inspiration for me) on the cover of @Nature featuring our Mexican Biobank study https://t.co/G79nMNDmpL Congrats @mauguz33 for a beautiful design!

It can be hard to fit mechanistic models to single cell data while accounting for the various types of noise (e.g., intrinsic, technical). Are some noise sources necessary to model, and others not? How can we fit models efficiently? Our new paper in Cell Systems addresses this!