8/ Our method does not seek an absolute genetic truth. Instead, it recognizes that genetic relationships are contextual and vary depending on the research questions and markers analyzed.🧬💡

7/ For example, using the network-based approach, we found between 5 and 34 genetic communities in one analysis, and the number of communities depends on the metrics and resolutions applied. This challenges the idea of ​​universal categorizations.🚫📦

6/ We applied this approach to a dataset combining information from the 1000 Genomes and Human Genome Diversity Projects. The results? A much richer and more detailed view of human genetic diversity.🌍

5/ Unlike traditional methods, our tool does not group individuals into a fixed number of predefined categories. Instead, it identifies genetic communities at different levels of resolution using clustering algorithms, such as Louvain and Leiden.🧑‍💻👥

4/ We have developed a network-based pipeline and visualization tool that diverges from conventional genetic analysis models. This approach, grounded in relational thinking, builds individual networks utilizing genetic similarity metrics.💡🧬
sohail-lab.shinyapps.io/GG-NC/

1/ Human genetics has traditionally been framed and studied using approaches that assume discrete, unchanging clusters such as continental groups🗺️. But what if this oversimplifies complex genetic relationships and co-produces racial frameworks?🤔

8/ Our method does not seek an absolute genetic truth. Instead, it recognizes that genetic relationships are contextual and vary depending on the research questions and markers analyzed.🧬💡

7/ For example, using the network-based approach, we found between 5 and 34 genetic communities in one analysis, and the number of communities depends on the metrics and resolutions applied. This challenges the idea of ​​universal categorizations.🚫📦

6/ We applied this approach to a dataset combining information from the 1000 Genomes and Human Genome Diversity Projects. The results? A much richer and more detailed view of human genetic diversity.🌍

5/ Unlike traditional methods, our tool does not group individuals into a fixed number of predefined categories. Instead, it identifies genetic communities at different levels of resolution using clustering algorithms, such as Louvain and Leiden.🧑‍💻👥

4/ We have developed a network-based pipeline and visualization tool that diverges from conventional genetic analysis models. This approach, grounded in relational thinking, builds individual networks utilizing genetic similarity metrics.💡🧬
sohail-lab.shinyapps.io/GG-NC/

1/ Human genetics has traditionally been framed and studied using approaches that assume discrete, unchanging clusters such as continental groups🗺️. But what if this oversimplifies complex genetic relationships and co-produces racial frameworks?🤔