Polygenic adaptation from standing genetic variation allows rapid ecotype formation
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eLife assessment
This valuable study combines phenotypic analysis, quantitative genetics and population genomics to propose that multiple genes underlie adaptive divergence in a marine midge system linked to tidal rhythm. Genes with a plausible role in perceiving and responding to lunar information are among the loci that most highly differentiate populations with distinct behaviors, but how much of this might be due to demography remains unclear. The evidence from quantitative trait locus is also deemed incomplete at this point.
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Abstract
Adaptive ecotype formation can be the first step to speciation, but the genetic underpinnings of this process are poorly understood. Marine midges of the genus Clunio (Diptera) have recolonized Northern European shore areas after the last glaciation. In response to local tide conditions they have formed different ecotypes with respect to timing of adult emergence, oviposition behavior and larval habitat. Genomic analysis confirms the recent establishment of these ecotypes, reflected in massive haplotype sharing between ecotypes, irrespective of whether there is ongoing gene flow or geographic isolation. QTL mapping and genome screens reveal patterns of polygenic adaptation from standing genetic variation. Ecotype-associated loci prominently include circadian clock genes, as well as genes affecting sensory perception and nervous system development, hinting to a central role of these processes in ecotype formation. Our data show that adaptive ecotype formation can occur rapidly, with ongoing gene flow and largely based on a re-assortment of existing alleles.
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eLife assessment
This valuable study combines phenotypic analysis, quantitative genetics and population genomics to propose that multiple genes underlie adaptive divergence in a marine midge system linked to tidal rhythm. Genes with a plausible role in perceiving and responding to lunar information are among the loci that most highly differentiate populations with distinct behaviors, but how much of this might be due to demography remains unclear. The evidence from quantitative trait locus is also deemed incomplete at this point.
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Reviewer #1 (Public Review):
The core conclusions in this manuscript are well supported. First, the genomic data clear support a recent origin of the Baltic and Arctic ecotypes from an Atlantic-like ancestor, without major bottlenecks and with gene exchange at least on the one sampled sympatric location but probably also more widely. The genome scans strongly suggest the involvement of multiple loci in divergence. This is supported by the quantitative trait locus analysis but this support is relatively weak because the number of markers used was small and markers were rather unevenly distributed on the genetic map, leaving little power to detect QTL in some areas. An analysis of the power of the QTL analysis is lacking and there might also be an issue about whether the measured trait truly captures rhythmicity.
The presence of …
Reviewer #1 (Public Review):
The core conclusions in this manuscript are well supported. First, the genomic data clear support a recent origin of the Baltic and Arctic ecotypes from an Atlantic-like ancestor, without major bottlenecks and with gene exchange at least on the one sampled sympatric location but probably also more widely. The genome scans strongly suggest the involvement of multiple loci in divergence. This is supported by the quantitative trait locus analysis but this support is relatively weak because the number of markers used was small and markers were rather unevenly distributed on the genetic map, leaving little power to detect QTL in some areas. An analysis of the power of the QTL analysis is lacking and there might also be an issue about whether the measured trait truly captures rhythmicity.
The presence of segregating variants across all sampled populations for the loci that appear to underlie local adaptation, plus the absence of strong sweep signatures, is good evidence that adaptation to the Baltic environment was based on standing genetic variation. This is supported by the known presence of local adaptation to tidal regimes within the Atlantic ecotype, providing a mechanism for the maintenance of standing variation. Clustering of putatively adaptive loci in one region of chromosome 1 seems clear, although it is not formally compared to a random distribution.
The authors make an interesting point that it is only when many genes are involved that Gene Ontology enrichment is expected to be informative. Here, they also had clear a priori expectations which are neatly fulfilled, further suggesting that differentiated loci are good candidates for a role in adaptation.
The broader context provided for the analysis of the fascinating marine midge system is brief and could usefully be expanded. It should make clear that, despite some focus on major gene effects that can be assigned to individual loci, there is widespread evidence for polygenic adaptation. Even where structural variants are known to have major effects, many other regions of the genome typically contribute to local adaptation. It would also be helpful to refer to theoretical expectations regarding distributions of effect sizes and clustering of locally-adaptive loci, with or without gene flow.
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Reviewer #2 (Public Review):
Fuhrman et al. explore a fascinating system to study the evolution and genetic architecture of ecological adaptation in marine midges. They use a number of approaches including analyses of whole genome sequences and QTL mapping to explore population structure and the loci associated with the timing and mode of reproduction. I have some concerns about the analyses and interpretations which I outline below.
My primary concern is in the design and interpretation of the QTL analysis. The QTL approach used here has low power, both due to the sample size and the number of markers used (it looks like ~8 per chromosome). The authors use an analysis of the sex determining locus as a "control" but because of the complete heritability of this trait in most systems it is more of a straw man to me. The authors conclude …
Reviewer #2 (Public Review):
Fuhrman et al. explore a fascinating system to study the evolution and genetic architecture of ecological adaptation in marine midges. They use a number of approaches including analyses of whole genome sequences and QTL mapping to explore population structure and the loci associated with the timing and mode of reproduction. I have some concerns about the analyses and interpretations which I outline below.
My primary concern is in the design and interpretation of the QTL analysis. The QTL approach used here has low power, both due to the sample size and the number of markers used (it looks like ~8 per chromosome). The authors use an analysis of the sex determining locus as a "control" but because of the complete heritability of this trait in most systems it is more of a straw man to me. The authors conclude that the architecture of the trait is polygenic based on this, but we are missing key information to evaluate this.
There are some issues with the presentation and interpretation of the population genetic analyses. Many assumptions are made about whether introgression or ILS occurred and there are statements that are not accurate about it being "impossible" to distinguish between these scenarios.
Some of the analyses associated with ecological adaptation that follow on the QTL results struck me as ad hoc and with the potential to lead to spurious results. I am not familiar with the BayPass approach but since it is the approach that explicitly accounts for population structure it seems the one that would be most appropriate for the authors to focus on in a revised manuscript. The use of phylogenetic windows that associate with ecotype is concerning to me as given the level of ILS and gene flow that appears to be present in this system is would be very challenging to distinguish signal from noise.
There were issues with the GO analysis that should be addressed. Because the gene universe used for GO enrichment is a subset of the full gene set, GO enrichment results will be biased. This will mostly lead to false positives (i.e. overrepresentation of a GO category due to evaluating a subset of genes that fall in that category).
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