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+---
+layout: news
+title: New tskit-related papers
+description: |
+ A trio of papers in the prestigious journals Nature, Science, and Nature Genetics
+ illustrate the power of tskit-based simulation and analysis to carry out
+ genome-wide analysis of human genome data. These focus on deep human ancestry,
+ recent human pedigrees, and statistical algorithms for biobank-scale datasets.
+image: ../assets/images/news/20230828-papers/quebec-thin.jpg
+timestamp: 2023-08-28
+author: Yan Wong
+---
+
+# A trio of high-impact papers
+
+In May and August 2023 the following papers, which make extensive use of tskit projects, were published:
+
+#### A weakly structured stem for human origins in Africa – [Nature](https://www.nature.com/articles/s41586-023-06055-y){:class="link--type-1--external"}
+{: style="clear:both"}
+
+{:class="news-float-left" style="width: 12em;"}
+
+A paper by [@apragsdale](https://github.com/apragsdale) and colleagues, who contribute to
+[msprime](https://tskit.dev/software/msprime.html) software development, uses msprime simulations
+(among other techniques) to argue for a complex history of structured subpopulations in deep human
+history.
+
+#### On the genes, genealogies, and geographies of Quebec – [Science](https://www.science.org/doi/10.1126/science.add5300){:class="link--type-1--external"}
+{: style="clear:both"}
+
+{:class="news-float-left" style="width: 12em;"}
+
+This paper uses the recently implemented feature in [msprime](https://tskit.dev/software/msprime.html)
+to [simulate genetic transmission through a known pedigree](https://tskit.dev/msprime/docs/stable/ancestry.html#sec-ancestry-models-fixed-pedigree).
+In this case, a human pedigree based on 4 million Canadian genealogical records was used.
+Results highlight the multiple impacts of geography on human migration, and a freely available
+simulated whole-genome dataset of almost 1.5 million individuals is provided.
+
+
+#### Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies – [Nature Genetics](https://www.nature.com/articles/s41588-023-01487-8){:class="link--type-1--external"}
+{: style="clear:both"}
+
+{:class="news-float-left" style="width: 12em;"}
+
+This paper shows how common statistical genetic algorithms, for example used in GWAS studies, can be both sped up
+and made more accurate by leveraging genetic genealogies inferred from real data by [tsinfer](https://tskit.dev/tsinfer/docs/stable/).
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