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Updated: Jul 4, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
The Effect of Copy Number Hemiplasy on Gene Family Evolution
Qiuyi Li1,2, Yao-Ban Chan1, Nicolas Galtier3
1School of Mathematics and Statistics/Melbourne Integrative Genomics, The University of Melbourne, Melbourne 3010, Australia.
None:
The evolution of gene families is complex, involving gene-level evolutionary events such as gene duplication, horizontal gene transfer, and gene loss, and other processes such as incomplete lineage sorting (ILS). Because of this, topological differences often exist between gene trees and species trees. A number of models have been recently developed to explain these discrepancies, the most realistic of which attempts to consider both gene-level events and ILS. When unified in a single model, the interaction between ILS and gene-level events can cause polymorphism in gene copy number, which we refer to as copy number hemiplasy (CNH). In this paper, we extend the Wright-Fisher process to include duplications and losses over several species, and show that the probability of CNH for this process can be significant. We study how well two unified models-multilocus multispecies coalescent (MLMSC), which models CNH, and duplication, loss, and coalescence (DLCoal), which does not-approximate the Wright-Fisher process with duplication and loss. We then study the effect of CNH on gene family evolution by comparing MLMSC and DLCoal. We generate comparable gene trees under both models, showing significant differences in various summary statistics; most importantly, CNH reduces the number of gene copies greatly. If this is not taken into account, the traditional method of estimating duplication rates (by counting the number of gene copies) becomes inaccurate. The simulated gene trees are also used for species tree inference with the summary methods ASTRAL and ASTRAL-Pro, demonstrating that their accuracy, based on CNH-unaware simulations calibrated on real data, may have been overestimated.
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