Evolutionary Shortcuts via Multinucleotide Substitutions and Their Impact on Natural Selection Analyses

Abstract

Inference and interpretation of evolutionary processes, in particular of the types and targets of natural selection affecting coding sequences, are critically influenced by the assumptions built into statistical models and tests. If certain aspects of the substitution process (even when they are not of direct interest) are presumed absent or are modeled with too crude of a simplification, estimates of key model parameters can become biased, often systematically, and lead to poor statistical performance. Previous work established that failing to accommodate multi-nucleotide (or multi-hit, MH) substitutions strongly biases dN/dS-based inference towards false positive inferences of diversifying episodic selection, as does failing to model variation in the rate of synonymous substitution (SRV) among sites. Here we develop an integrated analytical framework and software tools to simultaneously incorporate these sources of evolutionary complexity into selection analyses. We found that both MH and SRV are ubiquitous in empirical alignments, and incorporating them has a strong effect on whether or not positive selection is detected, (1:4-fold reduction) and on the distributions of inferred evolutionary rates. With simulation studies, we show that this effect is not attributable to reduced statistical power caused by using a more complex model. After a detailed examination of 21 benchmark alignments and a new high-resolution analysis showing which parts of the alignment provide support for positive selection, we show that MH substitutions occurring along shorter branches in the tree explain a significant fraction of discrepant results in selection detection. Our results add to the growing body of literature which examines decades-old modeling assumptions (including MH) and finds them to be problematic for comparative genomic data analysis. Because multi-nucleotide substitutions have a significant impact on natural selection detection even at the level of an entire gene, we recommend that selection analyses of this type consider their inclusion as a matter of routine. To facilitate this procedure, we developed, implemented, and benchmarked a simple and well-performing model testing selection detection framework able to screen an alignment for positive selection with two biologically important confounding processes: site-to-site synonymous rate variation, and multi-nucleotide instantaneous substitutions.

Rapid evolution of genes with anti-cancer functions during the origins of large bodies and cancer resistance in elephants

“…The ABSREL and BUSTED models are variants of the branch-sites random effect likelihood (BSREL) model of coding sequence evolution (Pond et al, 2011a; Smith et al, 2015), which allows for variation in d N / d S (ω) rates across lineages and sites; however, while ABSREL selects the optimal number of rate categories for each gene, BUSTED imposes three rate categories such that ω 1 ≤ω 2 ≤ω 3 ( Figure 1B ). Both models also accommodate synonymous rate variation across sites [S] (Pond and Muse, 2005; Wisotsky et al, 2020), double and triple multi-nucleotide mutations per codon [MH] (Lucaci et al, 2021), and both synonymous rate variation and multi-nucleotide mutations [SMH]; failure to account for synonymous rate variation (Dunn et al, 2019; Rahman et al, 2021; Wisotsky et al, 2020) and multi-nucleotide mutations can lead to widespread false positive inferences of positive selection (Dunn et al, 2019; Lucaci et al, 2023a; Nozawa et al, 2009; Suzuki, 2008; Venkat et al, 2018; Yang and Reis, 2011). We used small sample corrected Akaike Information Criterion values (ΔAICc≥10) to select for the best [S], [MH], and [SMH] model and inferred positive selection for a gene when the best fitting model included a class of sites with ω>1 and a likelihood ratio test P ≤0.05.…”

“…Goodman et al (2009), for example, used the free ratio model in PAML (Yang, 2007) to identify positively selected genes in the elephant lineage using a dataset of 11 species and 5,714 protein-coding genes; they identified 67 genes with d N / d S ≥1.01, which were enriched in mitochondrial functions but none of the GO terms we identified ( Supplementary dataset 1 ). There are several important differences between Goodman et al (2009) and our study: 1) The free ratio model does not test if the d N / d S ratio in a particular lineage is significantly different than one, rather it tests if d N / d S rate variation is different across lineages (Yang, 1998); 2) Unlike ABSREL and BUSTED, the free ratio model does not allow for rate variation across sites, d N / d S >1 is only inferred if the average d N / d S across all sites is greater than 1; and 3) The method implemented in PAML does not account for synonymous rate variation across sites or multiple simultaneous codon substitutions, which can bias inferences of both d S (Dunn et al, 2019; Rahman et al, 2021; Wisotsky et al, 2020) and d N (Dunn et al, 2019; Lucaci et al, 2023a; Nozawa et al, 2009; Suzuki, 2008; Venkat et al, 2018; Yang and Reis, 2011). While we tested 81.6% (4,663) of the genes tested by Goodman et al (2009), only one ( CD3G ) was inferred to have been positively selected in our analyses; therefore, it is unsurprising that there was little overlap in our GO enrichment results.…”

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“…This indicates that this change possibly had an adaptive character in the ancestral background. It is important to note, however, that models used for the analysis do not account for variation of synonymous substitution rate and multinucleotide substitution events, which in some cases might lead to false positive results (Lucaci et al, 2023). Because of that, the alternative hypothesis that substitutions Q66H and G109D occurred in the common ancestor of Erwiniaceae due to genetic drift, enabling the subsequent loss of ibpB gene, cannot be fully discounted.…”

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“…Genomic instability is a general characteristic of viruses, particularly in single-strand RNA viruses [8,9]. Having short replication cycles, recombination, error-prone replication, and strong selection drive genome instability in RNA viruses [10].…”