Modular Skeletal Evolution in Sticklebacks Is Controlled by Additive and Clustered Quantitative Trait Loci

Abstract

Understanding the genetic architecture of evolutionary change remains a long-standing goal in biology. In vertebrates, skeletal evolution has contributed greatly to adaptation in body form and function in response to changing ecological variables like diet and predation. Here we use genome-wide linkage mapping in threespine stickleback fish to investigate the genetic architecture of evolved changes in many armor and trophic traits. We identify .100 quantitative trait loci (QTL) controlling the pattern of serially repeating skeletal elements, including gill rakers, teeth, branchial bones, jaws, median fin spines, and vertebrae. We use this large collection of QTL to address long-standing questions about the anatomical specificity, genetic dominance, and genomic clustering of loci controlling skeletal differences in evolving populations. We find that most QTL (76%) that influence serially repeating skeletal elements have anatomically regional effects. In addition, most QTL (71%) have at least partially additive effects, regardless of whether the QTL controls evolved loss or gain of skeletal elements. Finally, many QTL with high LOD scores cluster on chromosomes 4, 20, and 21. These results identify a modular system that can control highly specific aspects of skeletal form. Because of the general additivity and genomic clustering of major QTL, concerted changes in both protective armor and trophic traits may occur when sticklebacks inherit either marine or freshwater alleles at linked or possible "supergene" regions of the stickleback genome. Further study of these regions will help identify the molecular basis of both modular and coordinated changes in the vertebrate skeleton. U NDERSTANDING the quantitative genetic architecture underlying evolutionary change in nature remains a major goal in genetics. The past two decades have seen a rapid increase in experimental data from various model systems, generating vigorous debate over the relative importance of coding vs. regulatory alleles, the prevalence of pleiotropy, and the role of large-effect mutations during adaptation to new environments (Stern and Orgogozo 2008;Streisfeld and Rausher 2011;Rockman 2012).One particularly interesting genetic architecture found in several natural systems is close linkage of loci controlling multiple, often coadaptive, phenotypes. Such trait clusters, sometimes called "supergenes," have been observed in primroses (Darwin 1877;Mather 1950;Li et al. 2011), butterflies (Clarke et al. 1968Mallet 1989;Joron et al. 2006), snails (Murray and Clarke 1976), and fish (Winge 1927;Protas et al. 2008;Roberts et al. 2009;Tripathi et al. 2009). Trait clusters could result from recombination suppression (Noor et al. 2001), for example through chromosomal inversions (Lowry and Willis 2010;Joron et al. 2011;Fishman et al. 2013). Alternatively, trait clusters could result from tightly linked loci or pleiotropic effects of individual genes (Mallet 1989;Studer and Doebley 2011 Cis-regulatory changes may predominate during morphological evoluti...

Evolved tooth gain in sticklebacks is associated with acis-regulatory allele ofBmp6

“…Previous quantitative genetic studies of stickleback pharyngeal tooth number revealed five QTL controlling ventral pharyngeal tooth number in a F2 genetic cross between an ancestral lowtoothed Japanese marine fish and a derived high-toothed Paxton benthic freshwater fish (15). Our more detailed studies suggest that differences in total adult tooth number arise from a combination of several factors, including changes in the development programs controlling tooth number, the size of the tooth field, and the spacing of teeth within that field.…”

“…The largest-effect QTL controlling pharyngeal tooth patterning in this study maps to chromosome 21 and explains ∼30% of the variance in pharyngeal tooth number (Fig. 3A) (15). To test whether this large-effect tooth number QTL replicates in other wild-derived chromosomes from the Paxton lake benthic population and to ask when during development this QTL acts, we analyzed an additional F2 genetic cross at three different developmental stages: before the tooth number divergence in the time course, around the time of divergence, and an adult stage after tooth number diverged in the time course.…”

“…Previous work identified quantitative trait loci (QTL) controlling tooth number in a large genetic cross of 370 F2 fish derived from a Paxton benthic and a Japanese marine grandparent (15). Compared with Paxton benthic freshwater sticklebacks, Japanese marine sticklebacks, like Alaskan marine sticklebacks from the Rabbit Slough population, are lowtoothed both in the wild and in the laboratory (SI Appendix, Table S1).…”

“…We first genotyped 1,004 additional F2 fish from the Paxton benthic × Japanese marine cross (16) to identify individuals with recombination events within the original chromosome 21 tooth QTL (15). This genotyping identified 91 recombinant F2 fish, which were then selectively phenotyped for tooth number.…”

Regulatory architecture of gene expression variation in the threespine stickleback, Gasterosteus aculeatus

“…Local adaptation of threespine sticklebacks to freshwater has been demonstrated to arise, at least partly, from selection on standing genetic variation in the marine environment. Further, QTL underlying morphological divergence between marine and freshwater populations have been demonstrated to have pleiotropic effects (Rogers et al 2012;Miller et al 2014), and frequently colocalize with regions of the genome found to be under parallel selection among independent freshwater colonizations. One way in which these regions could exert such pleiotropic effects is by harboring loci that influence the expression of many genes, i.e., eQTL hotspots.…”