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Gene tree quality affects empirical coalescent branch length estimation
Zoologica Scripta ( IF 2.5 ) Pub Date : 2021-09-09 , DOI: 10.1111/zsc.12512
Michael Forthman 1, 2 , Edward L. Braun 3 , Rebecca T. Kimball 3
Affiliation  

Assessing effects of gene tree error in coalescent analyses have widely ignored coalescent branch lengths (CBLs) despite their potential utility in estimating ancestral population demographics and detecting species tree anomaly zones. However, the ability of coalescent methods to obtain accurate estimates remains largely unexplored. Errors in gene trees should lead to underestimates of the true CBL, and for a given set of comparisons, longer CBLs should be more accurate. Here, we furthered our empirical understanding of how error in gene tree quality (i.e., locus informativeness and gene tree resolution) affect CBLs using four datasets comprised of ultraconserved elements (UCE) or exons for clades that exhibit wide ranges of branch lengths. For each dataset, we compared the impact of locus informativeness (assessed using number of parsimony-informative sites) and gene tree resolution on CBL estimates. Our results, in general, showed that CBLs were drastically shorter when estimates included low informative loci. Gene tree resolution also had an impact on UCE datasets, with polytomous gene trees producing longer branches than randomly resolved gene trees. However, resolution did not appear to affect CBL estimates from the more informative exon datasets. Thus, as expected, gene tree quality affects CBL estimates, though this can generally be minimized by using moderate filtering to select more informative loci and/or by allowing polytomies in gene trees. These approaches, as well as additional contributions to improve CBL estimation, should lead to CBLs that are useful for addressing evolutionary and biological questions.

中文翻译:

基因树质量影响经验聚合分支长度估计

评估基因树错误在聚结分析中的影响已经广泛忽略了聚结分支长度 (CBL),尽管它们在估计祖先种群人口统计数据和检测物种树异常区域方面具有潜在用途。然而,聚结方法获得准确估计的能力在很大程度上仍未得到探索。基因树中的错误应该会导致对真实 CBL 的低估,对于给定的一组比较,更长的 CBL 应该更准确。在这里,我们使用由超保守元素 (UCE) 或外显子组成的四个数据集,进一步深入了解基因树质量(即基因座信息量和基因树分辨率)中的错误如何影响 CBL,这些数据集包括分支长度范围广泛的进化枝。对于每个数据集,我们比较了基因座信息量(使用简约信息位点的数量进行评估)和基因树分辨率对 CBL 估计的影响。总的来说,我们的结果表明,当估计值包括低信息位点时,CBL 会大大缩短。基因树解析也对 UCE 数据集产生影响,多分基因树比随机解析的基因树产生更长的分支。然而,分辨率似乎并不影响来自更多信息外显子数据集的 CBL 估计。因此,正如预期的那样,基因树质量会影响 CBL 估计,尽管这通常可以通过使用适度过滤来选择更多信息位点和/或通过允许基因树中的多分体来最小化。这些方法,以及对改进 CBL 估计的额外贡献,
更新日期:2021-09-09
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