CR26-GA-1 - You will join the Genetics, Physiology and Livestock Systems (GenPhyse, about 150 permanent staff) joint research unit, where researchers aim to contribute to the agroecological transition of livestock systems through better understanding of livestock biology, genetic bases of traits, and selection schemes to achieve resilient populations. The unit brings together skills in biology, physiology, genomics, genetics, statistics, and bioinformatics. Within the unit, you will join the Chamade team (Characterization and management of genetic diversity) of the Diversity and Selection group comprising methodologists in quantitative genetics (genomic prediction, selection and evolution) and population genomics, as well as statisticians. Within your research team, you will be in charge of developing a new research programme in applied statistics to integrate high-throughput, heterogenous, and multiscale data into genetic and genomic evaluation methods. You will conduct your research to improve genomic prediction models by integrating new information relative to genome function (e.g. functional annotation), molecular phenotypes (e.g. transcriptomics, methylation), and high-throughput or intermediate phenotypes (e.g. longitudinal data, high throughput sensors).To integrate different types of data into current genomic prediction models, you will draw on a variety of statistical modelling, for example modelling SNP effects according to their functional annotation category, or including random effects capturing the inter-individual covariance for intermediate phenotypes. The modelling could draw, for example, on hierarchical models, meta-analysis methods, mediation analysis or machine learning, possibly simulation-based. In addition, as new high-throughput data may not necessarily be available on the same individuals as traditional data, their integration will require the implementation of suitable statistical techniques. The predictive performance of the developed models will be evaluated using numerical simulations, for instance based on real breeding programmes. The models will also be tested on real data from experimental and commercial programmes of livestock species. These data will be available through existing projects and partnerships within the unit and the division to initiate your research project. Computational efficiency must be taken into consideration in your developments to ultimately ensure their practical use in genetic and genomic evaluation. To develop your research, you will benefit from the proximity of experts in statistics, computer science, quantitative, molecular and population genetics within GenPhySE. In accordance with INRAE's policy for open science, in addition to scientific publications, you will promote your work by distributing free software implementing the new methods developed to ensure their wide dissemination to the international community.

CR26-GA-1 - You will join the Genetics, Physiology and Livestock Systems (GenPhyse, about 150 permanent staff) joint research unit, where researchers aim to contribute to the agroecological transition of livestock systems through better understanding of livestock biology, genetic bases of traits, and selection schemes to achieve resilient populations. The unit brings together skills in biology, physiology, genomics, genetics, statistics, and bioinformatics. Within the unit, you will join the Chamade team (Characterization and management of genetic diversity) of the Diversity and Selection group comprising methodologists in quantitative genetics (genomic prediction, selection and evolution) and population genomics, as well as statisticians. Within your research team, you will be in charge of developing a new research programme in applied statistics to integrate high-throughput, heterogenous, and multiscale data into genetic and genomic evaluation methods. You will conduct your research to improve genomic prediction models by integrating new information relative to genome function (e.g. functional annotation), molecular phenotypes (e.g. transcriptomics, methylation), and high-throughput or intermediate phenotypes (e.g. longitudinal data, high throughput sensors).To integrate different types of data into current genomic prediction models, you will draw on a variety of statistical modelling, for example modelling SNP effects according to their functional annotation category, or including random effects capturing the inter-individual covariance for intermediate phenotypes. The modelling could draw, for example, on hierarchical models, meta-analysis methods, mediation analysis or machine learning, possibly simulation-based. In addition, as new high-throughput data may not necessarily be available on the same individuals as traditional data, their integration will require the implementation of suitable statistical techniques. The predictive performance of the developed models will be evaluated using numerical simulations, for instance based on real breeding programmes. The models will also be tested on real data from experimental and commercial programmes of livestock species. These data will be available through existing projects and partnerships within the unit and the division to initiate your research project. Computational efficiency must be taken into consideration in your developments to ultimately ensure their practical use in genetic and genomic evaluation. To develop your research, you will benefit from the proximity of experts in statistics, computer science, quantitative, molecular and population genetics within GenPhySE. In accordance with INRAE's policy for open science, in addition to scientific publications, you will promote your work by distributing free software implementing the new methods developed to ensure their wide dissemination to the international community.

Identifying population structure from multilocus genotype data is key to downstream population genetic analyses in a variety of fields, including conservation, evolutionary genetics, Genome-Wide Assoc...

Demographic inference is a central tool for reconstructing the genetic history of a population. Recently, several new approaches have brought this tool into a new era, that of the exhaustive use of whole-genome sequence. These approaches can be divided into two categories: demographic inference based on ancestral recombination graphs (ARGs1) and deep learning based inference2,3. However, without any other source of information, it is difficult to assess the actual degree of accuracy of these methods. In the case of livestock species, we usually have access to an additional information, rather unique and valuable, the pedigree over several generations. This set of relationships makes it possible to compare inferred demographic histories with the exact, albeit incomplete, history of the population.In this thesis project, we aim to use the pedigree information on the one hand, to assess the results obtained with the new approaches of demographic inference, and, on the other hand, to refine methods for ARG estimation. Therefore, we propose a research program divided into three main tasks: (i) to appropriate new approaches (ARG, deep learning) in demographic inference on goat and sheep datasets, (ii) to compare these approaches together, in particular to evaluate their inference of the effective size of a population in the light of genealogical information, and (iii) to integrate this information into the inference of ARGs.Kelleher, J. et al. Inferring whole-genome histories in large population datasets. Nature Genetics 51, 1330–1338 (2019).Schrider, D. R. & Kern, A. D. Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics 34, 301–312 (2018).Korfmann, K., Gaggiotti, O. E. & Fumagalli, M. Deep learning in population genetics. Genome Biology and Evolution 15, evad008 (2023).You will be welcomed in the CHAMADE team (“CHAracterization and MAnagement of Diversity”) of the GenPhySE research unit (https://genphyse.inrae.fr/), located in the Occitanie-Toulouse research centre (31320, Castanet-Tolosan). The CHAMADE team is part of the “Diversity and Selection” scientific division of the research unit. The team is interested in methodological issues in the field of population genomics, genetic evaluation of livestock species and quantitative and evolutionary genetics. On deep learning approaches for demographic inference, collaboration is also planned with the BioInfo team from the Laboratoire Interdisciplinaire des Sciences du Numérique (LISN, Paris-Saclay University).

A bone discovered in Taiwan turns out to have belonged to a Denisovan, a lineage previously identified only thousands of miles away.

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The one hundred and third Take:

Beginning in early 2021, unique and highly divergent lineages of SARS-CoV-2 were sporadically found in wastewater sewersheds using a sequencing strategy focused on the most mutagenic region of SARS-Co...

Simulating a metapopulation of human evolution without Neanderthal introgression into Homo sapiens still identifies Neanderthal fragments in simulated genomes, and an admixture event that never took p...

Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These models aim to learn gen...

Optimal contribution selection (OCS) is a selective breeding method that manages the conversion of genetic variation into genetic gain to facilitate short-term competitiveness and long-term sustainabi...

A list of awesome tools for complex trait genetics. - MichelNivard/awesome-complex-trait-genetics

Amino acid substitution rate matrices are fundamental to statistical phylogenetics and evolutionary biology. Estimating them typically requires reconstructed trees for massive amounts of aligned...

Some geneticists say key figure falsely suggests genetic data support notion of distinct races

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