Prospects for Building Large Timetrees Using Molecular Data with Incomplete Gene Coverage among Species

Abstract

Scientists are assembling sequence data sets from increasing numbers of species and genes to build comprehensive timetrees. However, data are often unavailable for some species and gene combinations, and the proportion of missing data is often large for data sets containing many genes and species. Surprisingly, there has not been a systematic analysis of the effect of the degree of sparseness of the species-gene matrix on the accuracy of divergence time estimates. Here, we present results from computer simulations and empirical data analyses to quantify the impact of missing gene data on divergence time estimation in large phylogenies. We found that estimates of divergence times were robust even when sequences from a majority of genes for most of the species were absent. From the analysis of such extremely sparse data sets, we found that the most egregious errors occurred for nodes in the tree that had no common genes for any pair of species in the immediate descendant clades of the node in question. These problematic nodes can be easily detected prior to computational analyses based only on the input sequence alignment and the tree topology. We conclude that it is best to use larger alignments, because adding both genes and species to the alignment augments the number of genes available for estimating divergence events deep in the tree and improves their time estimates.

Combining phylogenomic and supermatrix approaches, and a time-calibrated phylogeny for squamate reptiles (lizards and snakes) based on 52 genes and 4162 species

“…Zheng and Wiens, 2015) reinforces the idea that large amounts of missing data need not be problematic for divergencetime estimation (e.g. Filipski et al, 2014;Zheng and Wiens, 2015).…”

Do missing data influence the accuracy of divergence-time estimation with BEAST?

“…However, when each gene was 75% incomplete, the error was more substantial, with mean error close to 17 Myr and with a strong bias towards underestimated clade ages (relative to the complete dataset of 20 genes). These results emphasize the benefits of including genes that are sampled for all taxa (see also Filipski et al, 2014). However, in some cases, the errors were relatively small without these ''backbone'' genes, even when data were missing in 50% of the taxa in all genes.…”

“…This was intended to mimic the situation explored by Filipski et al (2014) in which there are no complete ''backbone'' genes, a situation in which the divergence-dating method that they examined sometimes performed poorly. Here, three different levels of missing data were again explored: (a) missing data in 8 taxa, with 25% missing data in the matrix overall; (b) missing data in 16 taxa, 50% overall; and (c) missing data in 24 taxa, 75% overall.…”

“…Instead, they investigated whether missing data caused incorrect acceptance or rejection of a molecular clock model in simulations of the four-taxon case. Second, Filipski et al (2014) more directly analyzed the impact of missing data on divergence-time estimation, focusing on the performance of a relatively new dating method (RelTime; Tamura et al, 2012) using both simulated and empirical datasets. For example, they compared the accuracy of estimated divergence times using datasets with no missing data, limited missing data (20%), and extensive missing data (60%), using a simulated dataset of 426 taxa.…”

“…Overall, the analysis of Filipski et al (2014) provided an invaluable contribution to this important question. However, the RelTime method is only one of many methods for analyzing divergence times.…”