Saturday, January 12, 2013

A Critical Assessment of Storytelling: Gene Ontology Categories and the Importance of Validating Genomic Scans. " Where there is life there is wishful thinking "  Gerald F. Lieberman

Finding genes which are under positive selection is an important part of any molecular evolution biologists' work as these genes can be responsible for adaptations in a studied specie. To find such genes, genomic scans are conducted and regions of the genome that show specific patterns, such as selective sweeps, are further studied and sensible biological interpretations are made. In this paper, Pavlidis & al. show that one has to be careful with such biological interpretations as the patterns for positive selection can appear under an a priori known neutrally evolving genome and that it might not be that difficult to come up with a satisfying story about such false-positives.

Figure 1 | Flowchart representing the the steps in the simulation. These steps were repeated for all of the 100 simulations.

To show the existence of  false-positives in the detection of positive selection patterns, Pavlidis & al simulated 100 data sets of 40 D.melanogaster X chromosomes evolving under a neutral Wright-Fisher model. The D.melanogaster X chromosome, which was sampled in the Netherlands, is believed to have gone through a recent and deep bottleneck. A demographic scenario for that population was inferred using the Markovian Coalescent Simulator (MaCS) software.  The group then used the SeepFinder program to find the regions characteristic of selective sweeps in these artificially neutrally evolving genomes and mapped them to the actual X chromosome using Flybase, this allowed the naming of identified genes. Interesting genes were detected and  biological meaning was assigned using the Gene Ontology Statistics (g:GOSt) module of g:Profiler. A "convincing" narrative was then given (Figure 1.).

The results showed  that on average, 43 regions per simulation (min. 27 & max. 60) were found where the site frequency spectrum (SFS) shifted towards low- and high-frequency-derived alleles. These patterns presenting a lack of intermediate allelic frequencies are characteristic of recent selective sweep and are indistinguishable from selective sweeps occurring in nature under selective pressures (Figure 2.). These detected regions were then mapped to the real X chromosome using FlyBase as was mentioned earlier.

Figure 2 | SFS pattern for the highest SweepFinder peak in the first simulated data set. These patterns showing a lack of intermediate allelic frequencies are characteristic of recent selective sweeps. 
For each of the 100 simulated data sets, the g:GOSt enrichment analysis of every detected region showed that on average, 5.19 statistically significant categories were detected per data set with 77 sets yielding at least one significant category and 16 giving rise to more than 10 significant categories. To be able to quantitatively compare these results to real data results, an enrichment analysis was done on 37 inbred lines of D.melanogaster sampled in North Carolina which are accepted to have gone through very recent and deep bottlenecks as well. This real data enrichment analysis showed that 9 statistically significant terms were related to transcription factor binding site. This important result shows that the number of biological terms obtained with a g:GOSt enrichment analysis are not higher in the real data than in the simulated data sets. A few issues in the model were also addressed.

1. It is known that bottlenecks increase the proportion of false-positives in neutrality tests so the group made another simulation with a milder bottleneck model. The g:GOSt analysis still yielded significant categories in 85% of the simulated data sets.

2. It is known that large recombination rates result in different coalescent genealogies every few base pair thus hiding any genetic sweep and that small recombination rates tend to diminish the independence of genes to the hole genome thus not allowing selective sweeps to happen. To address this issue, the group did more simulations with 5 different combinations of recombination rates and bottleneck models. The g:GOSt analysis didn't show substantial differences between these simulations.

3. SweepFinder detects SFS outsider as signatures for recent selective sweeps but there exists other statistics such as the omega-statistic which will detect other signature for recent selective sweeps such as linkage disequilibrium (LD). Two more simulations were done using firstly a LD detection method (OmegaPlus software) and secondly a joint method combining SFS and LD detection. The g:GOSt enrichment for both simulations yielded similar amounts of significant categories even though the distributions of the detected regions along the genome are different (the distribution is more uniform with omega-statistics than with the SFS detection).

The group then tried to make up convincing narratives about the three highest SweepFinder scoring genes (CG15211, CG8188 & CG6788) in the first simulation. In my opinion, these narratives were not the most convincing from a biological point of view but that is not the point of the article.

Selective pressures experienced by organisms are complex, varied and changing with time. Even if we knew all the selective pressures imposed on a population at one point, the ways in which its' organisms could respond are vast! Every gene, as obscure as it might be, is linked one way or another to an important biological process so "meaningful" narratives, even about false-positive, can relatively easily be constructed. The extensive use of Gene Ontology and the ever increasing precision of data bases put at greater risk researchers of seeing patterns of positive selection were there are none.
What the authors of this article have shown isn't that computational nor that statistical approaches for detecting positive selection are wrong but that one should be cautious of not over-interpreting genomic scans and blindly trusting statistics because: No null hypothesis of what "makes sense" exists.

Pavlidis P, Jensen JD, Stephan W, & Stamatakis A (2012). A critical assessment of storytelling: gene ontology categories and the importance of validating genomic scans. Molecular biology and evolution, 29 (10), 3237-48 PMID: 22617950

Tuesday, January 8, 2013

The genomic basis of adaptive evolution in threespine sticklebacks

Sticklebacks are originally marine fish that colonized freshwater habitats after the last glaciation. Adaptation to freshwater environment happened independently in various rivers and lakes around the globe, giving rise to similar phenotypes following natural selection. In a recent study, researchers aimed to identify potential loci repeatedly associated with the divergence between marine and freshwater sticklebacks. An underlying question was to uncover if this adaptation is due to regulatory or protein-coding changes.
To ensure that the changes reflected parallel evolution, the authors sequenced a reference freshwater stickleback and 20 other freshwater and marine sticklebacks from both Pacific and Atlantic populations. They selected populations showing characteristic marine and freshwater morphologies (Figure1 a, b).
To find loci involved in repeated adaptation to freshwater habitats, the authors used two methods, aiming to identify regions where sequences from freshwater sticklebacks were similar to each other but different from marine sticklebacks. The first method is a self-organizing map-based iterative Hidden Markov Model (SOM/HMM) (Figure1 c). With this method, they identified the 20 most common patterns of genetic relationships (trees) among the 21 individuals. The authors found that for most of the genome, the fish clustered by geography, with fish from Pacific regions being closer to each other than they were to fish from Atlantic regions. For 215 regions however (0.46% of the genome), the fish clustered by marine / freshwater ecology. 
The second method the authors used was genetic distance based. The idea was to use distance matrices based on 21*21 pairwise nucleotide divergence. They then calculated a marine-freshwater cluster separation score (CSS) for each distance matrix, used to quantify the average distance between marine and freshwater clusters (Figure 1 c). 174 marine-freshwater divergent regions were found, covering 0.26% of the genome. The two methods are complementary, as they found 242 regions identified by either method (0.5% of the genome) and 147 regions identified by both (0.2% of the genome). Both methods confirmed that the previously known chromosome IV EDA locus plays an important role in the difference between marine and freshwater populations.

Figure1: Genome scans for parallel marine-freshwater divergence a. Marine (red) and freshwater (blue) stickleback populations were surveyed from diverse locations. b. Morphometric analysis was used to select individuals for re-sequencing. The 20 chosen individuals are from multiple geographically-proximate pairs of populations with typical marine and freshwater morphology (solid symbols). Points: population mean morphologies; ellipses: 95% confidence intervals for ecotypes. c. Genomes were analysed using SOM/HMM (upper) and CSS (lower) methods to identify parallel marine-freshwater divergent regions. Across most of the genome, the dominant patterns reflect neutral divergence or geographic structure. In contrast, <0.5% of the genome show haplotype-ecotype association, a pattern characteristic of divergent marine and freshwater adaptation via parallel reuse of standing genetic variation.

The authors then aimed to determine to what extent the globally shared regions found with the previous methods are widespread in a particular marine-freshwater species pair, compared to locally evolved genomic regions. To do this, they sequenced whole genomes of a single marine-freshwater pair found across a marine-freshwater hybrid zone in a river in Scotland. By analyzing the 0.1% most divergent regions, they found that they contained 35.3% of globally shared marine-freshwater divergence. This result means that only a part of the divergence is due to globally shared variants and that the major part may be due to locally evolved mutations (Figure4).

Figure 4: How much of local marine-freshwater adaptation occurs by reuse of global variants? a. Classic marine and freshwater ecotypes are maintained in downstream and upstream locations of the River Tyne, despite extensive hybridization at intermediate sites16. b. Pairwise sequence comparisons identify many genomic regions that show high divergence between upstream and downstream fish (X-axis). Many, but not all, of these regions also show high global marine-freshwater divergence (Y-axis; red points indicate significant CSS FDR<0.05), indicating that both global and local variants contribute to formation and reproductive isolation of a marine-freshwater species pair.

The team also observed extended regions of marine-freshwater divergence on chromosomes I, XI and XXI corresponding to chromosome inversions, which are a known genetic mechanism that can maintain diverging ecotypes in hybridizing populations, by preventing recombination between independent adaptive loci (Figure 3).

Figure3: Genome-wide distribution of marine-freshwater divergence regions Whole-genome profiles of SOM/HMM and CSS analyses reveal many loci distributed on multiple chromosomes (plus unlinked scaffolds, here grouped as "ChrUn"). Extended regions of marine-freshwater divergence on chrI, XI, and XXI correspond to inversions (red arrows). Marine-freshwater divergent regions detected by CSS are shown as grey peaks with grey points above chromosomes indicating regions of significant marine-freshwater divergence (FDR 0.05). Genomic regions with marine-freshwater-like tree topologies detected by SOM/HMM are shown as green points below chromosomes.

The authors were then interested in the proportion of regulatory and coding change involved in stickleback’s adaptation to freshwater environment. To estimate this, they analyzed 64 divergent regions showing the strongest evidence of parallel evolution that were identified with the previous SOM/HMM and CSS methods. They found that even though both coding and regulatory changes are involved in stickleback adaptation to freshwater habitats, regulatory changes seem to play a much stronger role. Seventeen percent of these 64 regions consisted of coding regions with consistent non-synonymous substitutions between marine and freshwater fish. On the other hand, 41 % consisted of non coding regions of the genome that were most likely regulatory, while 42% were evaluated as probably regulatory, as they contained both coding and non-coding sequences, but lacked ecotype-specific amino acid substitutions. Finally, the authors investigated whole genome expression levels of freshwater and marine fish. 2817 of the 12594 informative genes across the whole genome showed significant differences in expression levels between freshwater and marine ecotypes. They also found that genes that had a difference in expression between ecotypes were more likely situated in or near adaptive regions previously discovered with the SOM/HMM or CSS methods (Figure 6).

Figure 6: Contributions of coding and regulatory changes to parallel marine-freshwater stickleback adaptation a. A genome-wide set of marine-freshwater loci recovered by both SOM/HMM and CSS analyses includes regions with consistent amino acid substitutions between marine and freshwater ecotypes (yellow sector); regions with no predicted coding sequence (green sector); and regions with both coding and non-coding sequences, but no consistent marine-freshwater amino acid substitutions (grey). b. Genome-wide expression analysis shows that marine-freshwater regions identified by SOM/HMM or CSS analyses are enriched for genes showing significant expression differences in 6 out of 7 tissues between marine LITC and freshwater FTC fish (observed: grey bars; expected: white bars; *P<0.01, **P<0.001, ***P<0.0001, ****P[double less-than sign]0.00001), consistent with a role for regulatory changes in marine-freshwater evolution.

In conclusion, the fact that sticklebacks repeatedly evolved from marine to freshwater habitats, coupled with the power of whole genome sequencing, has allowed to uncover a great number of loci globally involved in marine-freshwater adaptation. The differentiation seems to be spread across the genome, on several different chromosomes. Globally shared mutations, however, only account for a fraction of the differences, as a lot of locally evolved mutations also seem to play a significant role. Moreover, regulatory adaptations are particularly important in this case of repeated evolution, although protein-coding changes have also been found in the set of loci implicated in differences between ecotypes.
The authors finally suggest that although they focused on freshwater-marine differences, other ecological traits could be studied, like lake-stream or open-water and bottom dwelling habitats or gigantism in particular lakes, as sticklebacks have also repeatedly evolved these characteristics.

Jones FC, Grabherr MG, Chan YF, Russell P, Mauceli E, Johnson J, Swofford R, Pirun M, Zody MC, White S, Birney E, Searle S, Schmutz J, Grimwood J, Dickson MC, Myers RM, Miller CT, Summers BR, Knecht AK, Brady SD, Zhang H, Pollen AA, Howes T, Amemiya C, Broad Institute Genome Sequencing Platform & Whole Genome Assembly Team, Baldwin J, Bloom T, Jaffe DB, Nicol R, Wilkinson J, Lander ES, Di Palma F, Lindblad-Toh K, & Kingsley DM (2012). The genomic basis of adaptive evolution in threespine sticklebacks. Nature, 484 (7392), 55-61 PMID: 22481358