Evidence for strong fixation bias at 4-fold degenerate sites across genes in the great tit genome

Abstract

3It is well established that GC content varies across the genome in many species and that 4 GC biased gene conversion, one form of meiotic recombination, is likely to contribute to this 5 heterogeneity. Bird genomes provide an extraordinary system to study the impact of GC biased 6 gene conversion owed to their specific genomic features. They are characterised by a high 7 karyotype conservation with substantial heterogeneity in chromosome sizes, with up to a dozen 8 large macrochromosomes and many smaller microchromosomes common across all bird species. 9 This heterogeneity in chromosome morphology is also reflected by other genomic features, such 10 as smaller chromosomes being gene denser, more compact and more GC rich relative to their 11 macrochromosomal counterparts -illustrating that the intensity of GC biased gene conversion 12 varies across the genome. Here we study whether it is possible to infer heterogeneity in GC biased 13 gene conversion rates across the genome using a recently published method that accounts for 14 GC biased gene conversion when estimating branch lengths in a phylogenetic context. To infer the 15 strength of GC biased gene conversion we contrast branch length estimates across the genome 16 both taking and not taking non-stationary GC composition into account. Using simulations we 17 show that this approach works well when GC fixation bias is strong and note that the number of 18 substitutions along a branch is consistently overestimated when GC biased gene conversion is 19 1 Gossmann et al. GC fixation bias in bird genomesnot accounted for. We use this predictable feature to infer the strength of GC dynamics across the 20 great tit genome by applying our new test statistic to data at 4-fold degenerate sites from three 21 bird species -great tit, zebra finch and chicken -three species that are among the best annotated 22 bird genomes to date. We show that using a simple one-dimensional binning we fail to capture a 23 signal of fixation bias as observed in our simulations. However, using a multidimensional binning 24 strategy, we find evidence for heterogeneity in the strength of fixation bias, including AT fixation 25 bias. This highlights the difficulties when combining sequence data across different regions in the 26 genome. 27

Noncoding regions underpin avian bill shape diversification at macroevolutionary scales

“…For example, GCbiased 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;Boĺivar et al 2016;Botero-Castro et al 2017;Corcoran et al 2017;Bolívar et al 2019). However, while 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 2018a).…”

“…Due 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, while interchromosomal re-arrangements are rare in birds, intra-chromosomal changes are more common and could lead to sudden changes in local mutation rates (Gossmann et al 2018b). Additionally, we restricted our analysis to non-coding regions that are specific to birds, or highly divergent relative to vertebrates (Seki et al 2017).…”

The effective population size modulates the strength of GC biased gene conversion in two passerines

“…fixed by positive selection (α) (Bolívar et al, 2018;Corcoran et al, 2017;Rousselle et al, 2019). Equally, studying gBGC in coding regions is inconvenienced by the action of natural selection also acting on those regions, forcing studies to use putatively neutral sites like third codon positions (Rousselle et al, 2019;Weber et al, 2014) and 4-fold degenerate sites (Bolívar et al, 2016;Corcoran et al, 2017;Gossmann et al, 2018) reducing the amount of data available as well as potentially being confounded by codon usage bias (Chamary and Hurst, 2005;Galtier et al, 2018;Hayes et al, 2020;Jackson et al, 2017;Kunstner et al, 2011).…”

“…The avian system has been the model of choice for many studies addressing GC evolution and biased gene conversion (Bolívar et al, 2016(Bolívar et al, , 2018(Bolívar et al, , 2019Corcoran et al, 2017;Gossmann et al, 2018;Rousselle et al, 2019;Weber et al, 2014). The suitability of avian genomes for addressing these topics stems from their variable intra genomic recombination landscapes (Backström et al, 2010;Stapley et al, 2008;van Oers et al, 2014) and conserved recombination hotspots (Singhal et al, 2015) providing a natural experiment for addressing the role of recombination and Ne in gBGC and GC content evolution.…”

“…Of the work on gBGC to date, much has focused on exploring its impact and interaction within genes and coding regions, largely addressing how it confounds signatures of selection (Bolívar et al, 2019;Corcoran et al, 2017;Gossmann et al, 2018;Ratnakumar et al, 2010;Rousselle et al, 2019). Of those studies that have considered the action of gene conversion in the non-coding genome (Duret and Arndt, 2008;Glémin et al, 2015;Haddrill and Charlesworth, 2008;Jackson et al, 2017;Muyle et al, 2011;Wallberg et al, 2015), little work has investigated fine scale variation within the genome and how this compares between species.…”