Why more data isn’t always better — and how to tell when your tree is lying to you.
1. The Genomic Promise
When the first complete genomes became available in the early 2000s, evolutionary biologists found themselves with a staggering new resource. Instead of choosing a few “marker genes,” they could now use thousands — perhaps every orthologous gene — to reconstruct the history of species.
This new paradigm, called phylogenomics, promised to resolve the great controversies of the tree of life:
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What are the earliest-branching animals?
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How are placental mammals related?
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What’s the true relationship between birds, reptiles, and dinosaurs?
By comparing hundreds or thousands of loci across genomes, the hope was to drown out the noise of stochastic error that plagued single-gene trees. A simple logic emerged: the more data we have, the more accurate our evolutionary inferences will be.
For a while, this seemed true. Genome-scale datasets brought remarkable resolution to previously ambiguous branches of the tree of life. Yet, as data volume grew, so did the contradictions. Different datasets — even different subsets of the same data — sometimes produced entirely different trees.
What was going on?
2. The Reality: Genomes Don’t Always Agree
The assumption that more data always yields better results turns out to be naive in the genomic era. While adding genes does increase statistical power, it also multiplies sources of biological conflict and methodological bias.
Imagine that each gene in a genome is a witness to the evolutionary past. If evolution were simple, every witness would tell the same story. But in reality, these witnesses often disagree — sometimes subtly, sometimes dramatically. The challenge is to decide which witnesses to trust, and which might be misleading.
This discordance arises from a mix of biological and technical reasons.
3. Biological Sources of Discordance
a. Incomplete Lineage Sorting (ILS)
When species diverge rapidly, ancestral genetic variation doesn’t always have time to sort out neatly before the next speciation event. As a result, some genes “coalesce” (find their common ancestor) deeper or shallower in the tree than expected.
The classic example comes from the relationships among humans, chimpanzees, and gorillas. While the consensus species tree places humans and chimps as sister taxa, about 30% of gene trees place gorillas closer to either humans or chimps. This isn’t because the genomes are wrong — it’s because the speciation events happened in quick succession, and ancestral alleles persisted across species boundaries.
ILS is now modeled explicitly under the multispecies coalescent (MSC) framework, which underlies modern tools like ASTRAL and SVDquartets. But it remains a major source of confusion in concatenated analyses that assume a single tree topology for all genes.
b. Horizontal Gene Transfer (HGT)
In prokaryotes, genes often jump across species boundaries — sometimes even between distant taxa. This process, horizontal gene transfer, blurs the concept of a single species tree. A bacterium might inherit one gene from its ancestor and another from a completely unrelated lineage.
In eukaryotes, HGT is rarer but not absent. Examples include endosymbiotic gene transfer (from organelles to the nucleus) and occasional transfers between parasites and hosts. For instance, plant-parasitic nematodes have acquired cellulase genes from bacteria, allowing them to digest plant cell walls.
HGT can create dramatic conflicts between gene trees, giving rise to the idea that microbial evolution is better represented as a network rather than a strictly branching tree.
c. Gene Duplication and Loss
Gene families expand and contract over evolutionary time. When a gene duplicates, the copies may diverge and be lost independently in different lineages. If these paralogs are mistakenly analyzed as orthologs (genes derived from a single ancestral locus), the resulting trees can be misleading.
Distinguishing orthology from paralogy is thus critical in phylogenomics. Tools like OrthoFinder, OMA, and PhylomeDB aim to automate this process, but no method is perfect, especially when dealing with incomplete or fragmented genomes.
d. Hybridization and Introgression
In many lineages — plants, fishes, amphibians, mammals — species boundaries are porous. Hybridization can produce gene flow between diverging lineages, leaving a mosaic genomic signature. In such cases, some loci support one topology, others another, reflecting different ancestries across the genome.
The human lineage is a prime example: modern humans carry DNA introgressed from Neanderthals and Denisovans, while some African populations show evidence of archaic introgression from unknown “ghost lineages.” These complex histories can’t be captured by a simple bifurcating tree.
4. Technical and Analytical Sources of Discordance
Even without messy biology, phylogenetic trees can be misled by analytical choices. Genomic data are rich but not foolproof.
a. Alignment Errors
Aligning homologous sequences across distantly related taxa is notoriously difficult. Misaligned regions — especially in noncoding or highly variable regions — can introduce artificial signal. Programs like MAFFT, PRANK, and MACSE attempt to model indels accurately, but even small misalignments can flip tree topology.
b. Model Misspecification
Every phylogenetic analysis assumes a model of molecular evolution — substitution rates, base composition, rate heterogeneity, codon biases, etc. If the true evolutionary process differs from the assumed model, inference can be biased.
For example, ignoring compositional heterogeneity (different GC content across lineages) can group together unrelated taxa with similar base composition — a classic form of long-branch attraction. Modern software like IQ-TREE 2 allows for mixture models and model partitioning, but the risk of misspecification remains.
c. Missing Data
No genome is perfect. Missing or incomplete genes can skew results if certain taxa contribute fewer loci or shorter sequences. Ironically, adding poorly sampled taxa can sometimes destabilize rather than strengthen the analysis.
Approaches like supermatrix filtering, coalescent summary methods, and data occupancy thresholds are used to balance inclusion and completeness — but each comes with trade-offs.
d. Computational Constraints
Even the best models are only as good as the hardware they run on. Genome-scale trees can involve tens of thousands of genes and hundreds of taxa, leading to billions of possible topologies. Software must use heuristics, approximations, and pruning to make the problem tractable. This means the “best” tree found is often just the best among many local optima.
5. When Trees Disagree: Diagnosing Conflict
How do researchers know whether their tree is trustworthy?
Modern phylogenomics has developed a suite of support measures and conflict diagnostics that go beyond simple bootstrap values.
a. Bootstrap and Posterior Support
Traditional bootstrap values and Bayesian posterior probabilities estimate the stability of branches given resampling or model uncertainty. However, in genome-wide datasets, even wrong relationships can achieve high bootstrap support simply because of massive data volume.
b. Gene and Site Concordance Factors (gCF / sCF)
Developed in IQ-TREE, these metrics quantify what proportion of individual gene trees (gCF) or alignment sites (sCF) support a given branch. They are invaluable for spotting hidden conflict — cases where a branch has high bootstrap but low concordance, signaling disagreement among loci.
c. Quartet Concordance and Phylogenetic Networks
Methods like PhyParts, BUCKy, and SplitsTree visualize conflict explicitly by summarizing alternative relationships as networks or consensus plots. These approaches help disentangle whether conflict reflects true biological processes (like ILS or hybridization) or analytical artifacts.
6. Case Studies: Lessons from the Field
a. The Deep Animal Tree
One of the most contentious questions in modern phylogenomics is the root of the animal tree: are sponges (Porifera) or comb jellies (Ctenophora) the earliest-branching animals?
Genome-scale analyses have supported both scenarios depending on dataset composition, alignment method, and evolutionary model. Ctenophora-first trees often arise when site-heterogeneous models aren’t used, while Porifera-first trees emerge when complex models and compositional biases are accounted for. The debate illustrates how sensitive results can be to model choice, despite vast datasets.
b. The Avian Radiation
The rapid diversification of birds after the Cretaceous–Paleogene extinction (~66 Mya) produced one of the hardest problems in phylogenomics. The Avian Phylogenomics Project (2014) analyzed 48 genomes and found widespread gene tree discordance — a hallmark of incomplete lineage sorting during a rapid radiation. Coalescent-based methods improved resolution, but even today, certain deep splits among birds remain uncertain.
c. Mammalian Relationships
Similarly, the early divergence of placental mammals (Afrotheria, Xenarthra, Boreoeutheria) remains debated. Concatenated analyses and coalescent analyses sometimes yield different roots, depending on how genes are filtered and how substitution models handle rate variation. The “mammalian tree wars” epitomize how even well-sampled groups can harbor deep uncertainties.
7. The Bright Side: Opportunities in Phylogenomics
Despite its pitfalls, the genomic revolution offers immense opportunities — arguably greater than any previous era in evolutionary biology.
a. Resolution of Deep Time
Whole-genome data allow researchers to tackle ancient divergences with unprecedented statistical power. Lineages that diverged hundreds of millions of years ago — like early animals or fungi — can now be investigated using thousands of slowly evolving loci.
b. Integration Across Data Types
Phylogenomics isn’t just about DNA. Increasingly, researchers integrate transcriptomic, proteomic, and even morphological data into combined analyses, using frameworks like total-evidence dating. This synthesis helps link genotype to phenotype evolution.
c. Linking Genomes to Ecology
Genome-wide phylogenies are becoming tools to study adaptation and macroevolution. Phylogenetic comparative methods can now leverage high-resolution trees to correlate genetic changes with ecological shifts — for example, in diet, habitat, or metabolic strategy.
d. Real-Time Phylogenomics
With portable sequencing and real-time computation, phylogenetics is no longer confined to deep-time studies. Viral and bacterial evolution can be tracked in real time, as seen with Nextstrain for SARS-CoV-2. Genome-wide phylogenies here serve public health as much as they serve systematics.
8. Finding Balance: Toward a More Honest Phylogenetics
The main lesson from the past decade is not that genome-wide phylogenies fail, but that data quantity and data quality must be matched by analytical nuance.
Best practices are evolving to include:
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Explicit modeling of gene tree discordance (coalescent methods).
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Testing multiple models and filtering data systematically.
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Reporting concordance factors alongside bootstrap values.
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Using reproducible, automated workflows (e.g., Nextflow, Snakemake).
The field has matured from asking “What is the true tree?” to asking “What processes shaped the conflicting histories we observe?” This shift reflects a deeper understanding of evolution itself — as a complex, reticulate, and dynamic process.
9. Conclusion: Embracing Complexity
Genome-wide phylogenies have transformed our understanding of life’s history. They’ve resolved old debates, created new ones, and forced the field to confront the messy reality of evolution.
Conflict is no longer a problem to be ignored but a feature to be explained.
As one researcher aptly put it:
“The true tree of life may be less like an oak and more like a banyan — interconnected, tangled, and endlessly fascinating.”
By acknowledging the power and pitfalls of genome-scale data, we move toward a more realistic, more nuanced view of evolution — one where uncertainty is not a weakness, but a measure of how complex and beautiful life’s history truly is.
Next Up:
In the next post — “Tools of the Trade: How Scientists Build Genomic Trees” — we’ll dive into the practical side: the pipelines, software, and workflows that power modern phylogenomics, and what each step contributes to building and evaluating a reliable tree.
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