Evidence for Strong Fixation Bias at 4-fold Degenerate Sites Across Genes in the Great Tit Genome

Abstract

It is well established that GC content varies across the genome in many species and that GC biased gene conversion, one form of meiotic recombination, is likely to contribute to this heterogeneity. Bird genomes provide an extraordinary system to study the impact of GC biased gene conversion owed to their specific genomic features. They are characterized by a high karyotype conservation with substantial heterogeneity in chromosome sizes, with up to a dozen large macrochromosomes and many smaller microchromosomes common across all bird species. This heterogeneity in chromosome morphology is also reflected by other genomic features, such as smaller chromosomes being gene denser, more compact and more GC rich relative to their macrochromosomal counterparts-illustrating that the intensity of GC biased gene conversion varies across the genome. Here we study whether it is possible to infer heterogeneity in GC biased gene conversion rates across the genome using a recently published method that accounts for GC biased gene conversion when estimating branch lengths in a phylogenetic context. To infer the strength of GC biased gene conversion we contrast branch length estimates across the genome both taking and not taking non-stationary GC composition into account. Using simulations we show that this approach works well when GC fixation bias is strong and note that the number of substitutions along a branch is consistently overestimated when GC biased gene conversion is not accounted for. We use this predictable feature to infer the strength of GC dynamics across the great tit genome by applying our new pipeline to data at 4-fold degenerate sites from three bird species-great tit, zebra finch and chicken-three species that are among the best annotated bird genomes to date. We show that using a simple one-dimensional binning we fail to capture a signal of fixation bias as observed in our simulations. However, using a multidimensional binning strategy, we find evidence for heterogeneity in the strength of fixation bias, including AT fixation bias. This highlights the difficulties when combining sequence data across different regions in the genome.

Noncoding regions underpin avian bill shape diversification at macroevolutionary scales

“…For example, GC-biased gene conversion (described as the preferential conversion of "A" or "T" alleles to "G" or "C" during recombination induced repair) has been shown to significantly affect estimates of substitution rates, in particular at synonymous sites in birds (Galtier et al 2009;Weber et al 2014;Bolívar et al 2016;Botero-Castro et al 2017;Corcoran et al 2017;Bolívar et al 2019). However, although differences in the extent of GC-biased gene conversion across genes are known, much less is known about its variation over time, and incorporating such biases into large-scale phylogenetic frameworks is far from trivial (Gossmann et al 2018).…”

“…Owing to special features of the avian karyotype, such as a stable recombinational and mutational landscape, it seems unlikely that variation in mutation rate can contribute to the patterns observed here. However, although inter-chromosomal rearrangements are rare in birds, intra-chromosomal changes are more common and could lead to sudden changes in local mutation rates (Gossmann et al 2018). Additionally, we restricted our analysis to noncoding regions that are specific to birds or highly divergent relative to vertebrates (Seki et al 2017).…”

Controlling for Variable Transposition Rate with an Age-Adjusted Site Frequency Spectrum

“…Finally, when comparing TEs to putatively neutral sites, the putatively neutral sites may not be evolving neutrally, either because of direct or linked selection. It has been shown that even synonymous sites can be under selective constraint ( Chamary and Hurst 2005 ; Chamary et al 2006 ; Eöry et al 2010 ; Künstner et al 2011 ; Gu et al 2012 ; Lawrie et al 2013 ; Gossmann et al 2018 ) and, therefore, the SFS of putatively neutral sites such as synonymous sites might be altered by natural selection. However, having weak selection affecting the putatively neutral sites might not be as important as the difference in the strength of selection between the compared sites, since the approach proposed here is evaluating frequency differences between sites of the same age.…”

Controlling for variable transposition rate with an age-adjusted site frequency spectrum

“…Finally, when comparing TEs to putatively neutral sites, the putatively neutral sites may not be evolving neutrally, either because of direct or linked selection. It has been shown that even synonymous sites can be under selective constraint (Chamary and Hurst 2005; Chamary et al 2006; Eöry et al 2010; Künstner et al 2011; Gu et al 2012; Lawrie et al 2013; Gossmann et al 2018) and, therefore, the SFS of putatively neutral sites such as synonymous sites might be altered by natural selection. However, having weak selection affecting the putatively neutral sites might not be as important as the difference in the strength of selection between the compared sites, since the approach proposed here is evaluating frequency differences between sites of the same age.…”