On the effect of fluctuating recombination rates on the decorrelation of gene histories in the human genome

We show how to incorporate fluctuations of the recombination rate along the chromosome into standard gene-genealogical models for the decorrelation of gene histories. This enables us to determine how small-scale fluctuations (Poissonian hot-spot model) and large-scale variations (Kong et al. 2002) of the recombination rate influence this decorrelation. We find that the empirically determined large-scale variations of the recombination rate give rise to a significantly slower decay of correlations compared to the standard, unstructured gene-genealogical model assuming constant recombination rate. A model with long-range recombination-rate variations and with demographic structure (divergent population) is found to be consistent with the empirically observed slow decorrelation of gene histories. Conversely, we show that small-scale recombination-rate fluctuations do not alter the large-scale decorrelation of gene histories.     

The relative power of genome scans to detect local adaptation depends on sampling design and statistical method

Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distance) and nonequilibrium (i.e. range expansion from one or two refugia) demographic histories in spatially heterogeneous environments. We simulated spatially complex landscapes, which allowed us to exploit local maxima and minima in the environment in ‘pair’ and ‘transect’ sampling strategies. We compared F_ST outlier and genetic–environment association (GEA) methods for each of two approaches that control for population structure: with a covariance matrix or with latent factors. We show that while the relative power of two methods in the same category (F_ST or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and F_ST had higher power under isolation by distance. In the refugia models, however, these methods varied in their power to detect local adaptation at weakly selected loci. At weakly selected loci, paired sampling designs had equal or higher power than transect or random designs to detect local adaptation. Our results can inform sampling designs for studies of local adaptation and have important implications for the interpretation of genome scans based on landscape data.     

Genetic differentiation on multiple spatial scales in an ecotype-forming marine snail with limited dispersal: Littorina saxatilis

The population genetic structure of marine species lacking free-swimming larvae is expected to be strongly affected by random genetic drift among populations, resulting in genetic isolation by geographical distance. At the same time, ecological separation over microhabitats followed by direct selection on those parts of the genome that affect adaptation might also be strong. Here, we address the question of how the relative importance of stochastic vs. selective structuring forces varies at different geographical scales. We use microsatellite DNA and allozyme data from samples of the marine rocky shore snail Littorina saxatilis over distance scales ranging from metres to 1000 km, and we show that genetic drift is the most important structuring evolutionary force at distances > 1 km. On smaller geographical scales (< 1 km), divergent selection between contrasting habitats affects population genetic structure by impeding gene flow over microhabitat borders (microsatellite structure), or by directly favouring specific alleles of selected loci (allozyme structure). The results suggest that evolutionary drivers of population genetic structure cannot a priori be assumed to be equally important at different geographical scales. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society , 2008, 94 , 31–40.     

Scale‐dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (i) examining hierarchical partitioning of genetic variation in stream networks; and (ii) testing for genetic isolation by distance along stream corridors vs. overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. amova identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in‐stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.     

Widespread Signals of Convergent Adaptation to High Altitude in Asia and America

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.     

Mitochondrial DNA diversity and phylogeography of endangered green turtle (Chelonia mydas) populations in Africa

We analysed the genetic structure of seven nesting sites of the endangered green turtle (Chelonia mydas) in Africa using mitochondrial DNA control region sequences. Tissue samples were collected from 188 nesting females at six sites in West Africa and one in the Indian Ocean. A 488 bp fragment of the control region revealed 14 different haplotypes, 10 of which are previously undescribed. The most common haplotype (CM8) was observed in 157 individuals. All other haplotypes were closely related, except two divergent lineages: CM38, removed by four substitutions, and the three Indian Ocean haplotypes, distinguished by 31 substitutions. Significant differences in haplotype and nucleotide diversity were observed between Atlantic rookeries and among ocean basins. Analysis of molecular variance revealed high levels of differentiation between the Atlantic and the Indian Ocean populations but a much shallower Atlantic substructuring. Green turtle population genetic structure is thought to have been shaped by a dynamic succession of extinction and recolonisation of rookeries, by natal homing and occasional breakdown in nest-site fidelity. Mismatch distributions of pairwise differences between haplotypes at each rookery were found to be consistent with recent population expansion. We argue that demographic histories can be explained by scenarios at several temporal scales, including geological events, sea level fluctuations and more recent patterns of exploitation. We discuss management and conservation implications of our results for these threatened populations, identifying two ESUs (one in the Atlantic and one in the Indian ocean) and three MUs within the Atlantic.     

Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change

Landscape genetics seeks to determine the effect of landscape features on gene flow and genetic structure. Often, such analyses are intended to inform conservation and management. However, depending on the many factors that influence the time to reach equilibrium, genetic structure may more strongly represent past rather than contemporary landscapes. This well‐known lag between current demographic processes and population genetic structure often makes it challenging to interpret how contemporary landscapes and anthropogenic activity shape gene flow. Here, we review the theoretical framework for factors that influence time lags, summarize approaches to address this temporal disconnect in landscape genetic studies, and evaluate ways to make inferences about landscape change and its effects on species using genetic data alone or in combination with other data. Those approaches include comparing correlation of genetic structure with historical versus contemporary landscapes, using molecular markers with different rates of evolution, contrasting metrics of genetic structure and gene flow that reflect population genetic processes operating at different temporal scales, comparing historical and contemporary samples, combining genetic data with contemporary estimates of species distribution or movement, and controlling for phylogeographic history. We recommend using simulated data sets to explore time lags in genetic structure, and argue that time lags should be explicitly considered both when designing and interpreting landscape genetic studies. We conclude that the time lag problem can be exploited to strengthen inferences about recent landscape changes and to establish conservation baselines, particularly when genetic data are combined with other data.     

Identification and quantification of disease-related gene clusters

MOTIVATION: DNA microarray technology and the completion of human and mouse genome sequencing programs are now offering new avenues for the investigation of complex genetic diseases. In particular, this makes possible the study of the spatial distribution of disease-related genes within the genome. We report on the first systematic search for clustering of genes associated with a polygenic autoimmune disease. RESULTS: Using a set of cDNA microarray chip experiments in two mouse models of rheumatoid arthritis, we have identified approximately 200 genes based on their expression in inflamed joints and mapped them into the genome. We compute the spatial autocorrelation function of the selected genes and find that they tend to cluster over scales of a few megabase pairs. We then identify significant gene clusters using a friends-of-friends algorithm. This approach should aid in discovering functionally related gene clusters in the mammalian genome.     

The influence of life‐history strategy on genetic differentiation and lineage divergence in darters (Percidae: Etheostomatinae)

Recent studies determined that darters with specialized breeding strategies can exhibit deep lineage divergence over fine geographic scales without apparent physical barriers to gene flow. However, the extent to which intrinsic characteristics interact with extrinsic factors to influence population divergence and lineage diversification in darters is not well understood. This study employed comparative phylogeographic and population genetic methods to investigate the influence of life history on gene flow, dispersal ability, and lineage divergence in two sympatric sister darters with differing breeding strategies. Our results revealed highly disparate phylogeographic histories, patterns of genetic structure, and dispersal abilities between the two species suggesting that life history may contribute to lineage diversification in darters, especially by limiting dispersal among large river courses. Both species also showed striking differences in demographic history, indicating that extrinsic factors differentially affected each species during the Pleistocene. Collectively, our results indicate that intrinsic and extrinsic factors have influenced levels of gene flow among populations within both species examined. However, we suggest that life‐history strategy may play a more important role in lineage diversification in darters than previously appreciated, a finding that has potentially important implications for understanding diversification of the rich North American freshwater fish fauna.     

Retrospective coalescent methods and the reconstruction of metapopulation histories in the sea

Phylogeographic analyses are a key interface between ecological and evolutionary ways of knowing because such analyses integrate the cumulative effects of demographic (ecological) processes over geological (evolutionary) time scales. Newly developed coalescent methods allow evolutionary ecologists to overcome some limitations associated with inferring population history from classic methods such as Wright’s F _ST. Here we briefly contrast classic and coalescent methods for looking backward in time through a population genetic lens, focusing on the key advantages of the isolation-with-migration (IM) class of coalescent methods for distinguishing ancient connectedness from actual recurrent contemporary gene flow as causes of genetic similarity or differentiation among populations. Making this critical distinction can lead to the discovery of otherwise obscured histories underlying conventional patterns of spatial variation. We illustrate the importance of these insights using analyses of Pacific fishes, snails, and sea stars in which population sizes and divergence times are more important than rates of contemporary gene flow as determinants of population genetic differentiation. We then extend the IM method to genetic data from two model metapopulation species (California abalone, Australian damselfish). The analyses show the potential use of non-equilibrium IM methods for differentiating among metapopulation models that make different predictions about population parameters and have different implications for the design of marine protected areas and other conservation goals. At face value, the results largely rule out classic metapopulation dynamics (dominated by extinction and colonization rather than connectivity via ongoing recurrent gene flow) but, at the same time, do not strongly support a modern marine metapopulation dynamic (ecologically significant connectivity between demes). However, the results also highlight the need for much more data (i.e., loci) sampled on different spatial scales in order to determine whether metapopulation dynamics might exist on smaller scales than are typically sampled by most phylogeographers and landscape geneticists.     

Numts help to reconstruct the demographic history of the ocellated lizard (Lacerta lepida) in a secondary contact zone

In northwestern Iberia, two largely allopatric Lacerta lepida mitochondrial lineages occur, L5 occurring to the south of Douro River and L3 to the north, with a zone of putative secondary contact in the region of the Douro River valley. Cytochrome b sequence chromatograms with polymorphisms at nucleotide sites diagnostic for the two lineages were detected in individuals in the region of the Douro River and further north within the range of L3. We show that these polymorphisms are caused by the presence of four different numts (I-IV) co-occurring with the L3 genome, together with low levels of heteroplasmy. Two of the numts (I and II) are similar to the mitochondrial genome of L5 but are quite divergent from the mitochondrial genome of L3 where they occur. We show that these numts are derived from the mitochondrial genome of L5 and were incorporated in L3 through hybridization at the time of secondary contact between the lineages. The additional incidence of these numts to the north of the putative contact zone is consistent with an earlier postglacial northward range expansion of L5, preceding that of L3. We show that genetic exchange between the lineages responsible for the origin of these numts in L3 after secondary contact occurred prior to, or coincident with, the northward expansion of L3. This study shows that, in the context of phylogeographic analysis, numts can provide evidence for past demographic events and can be useful tools for the reconstruction of complex evolutionary histories.     

Effects of landscape and history on diversification of a montane, stream-breeding amphibian

Aim The aim of this study was to understand the roles of landscape features in shaping patterns of contemporary and historical genetic diversification among populations of the Andean tree frog (Hypsiboas andinus) across spatial scales. Location Andes mountains, north‐western Argentina, South America. Methods Mitochondrial DNA control region sequences were utilized to assess genetic differentiation among populations and calculate population pair‐wise genetic distances. Three models of movement, namely traditional straight‐line distance and two effective distances based on habitat classification, were examined to determine which of these explained the most variation in pair‐wise population genetic differentiation. The two habitat classifications were based on digital vegetation and hydrology layers that were generated from a 90‐m resolution digital elevation model (DEM) and known relationships between elevation and habitat. Mantel tests were conducted to test for correlations between geographic and genetic distance matrices and to estimate the percentage variation explained by each type of geographic distance. To investigate the location of possible barriers to gene flow, we used Monmonier’s maximum difference algorithm as implemented in barrier 2.2. Results At both geographic scales, effective distances explained more variation in genetic differentiation than did straight‐line distance. The least‐cost distances based on the simple classification performed better than the more detailed habitat classification. We controlled for the effects of historical range fragmentation determined from previous nested clade analyses, and therefore evaluated the effect of different distances on the genetic variation attributable to more recent factors. Effective distances identified populations that were highly divergent as a result of isolation in unsuitable habitats. The proposed locations of barriers to gene flow identified using Monmonier’s maximum difference algorithm corresponded well with earlier analyses and supported findings from our partial Mantel tests. Main conclusions Our results indicate that landscape features have been important in both historical and contemporary genetic structuring of populations of H. andinus at both large and small spatial scales. A landscape genetic perspective offers novel insights not provided by traditional phylogeographic studies: (1) effective distances can better explain patterns of differentiation in populations, especially in heterogeneous landscapes where barriers to dispersal may be common; and (2) least‐cost path analysis can help to identify corridors of movement between populations that are biologically more realistic.     

Evaluation of model fit of inferred admixture proportions

Model based methods for genetic clustering of individuals, such as those implemented in structure or ADMIXTURE, allow the user to infer individual ancestries and study population structure. The underlying model makes several assumptions about the demographic history that shaped the analysed genetic data. One assumption is that all individuals are a result of K homogeneous ancestral populations that are all well represented in the data, while another assumption is that no drift happened after the admixture event. The histories of many real world populations do not conform to that model, and in that case taking the inferred admixture proportions at face value might be misleading. We propose a method to evaluate the fit of admixture models based on estimating the correlation of the residual difference between the true genotypes and the genotypes predicted by the model. When the model assumptions are not violated, the residuals from a pair of individuals are not correlated. In the case of a bad fitting admixture model, individuals with similar demographic histories have a positive correlation of their residuals. Using simulated and real data, we show how the method is able to detect a bad fit of inferred admixture proportions due to using an insufficient number of clusters K or to demographic histories that deviate significantly from the admixture model assumptions, such as admixture from ghost populations, drift after admixture events and nondiscrete ancestral populations. We have implemented the method as an open source software that can be applied to both unphased genotypes and low depth sequencing data.     

A consensus tree approach for reconstructing human evolutionary history and detecting population substructure

The random accumulation of variations in the human genome over time implicitly encodes a history of how human populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a challenging computational and statistical problem but has important applications both to basic research and to the discovery of genotype-phenotype correlations. We present a novel approach to inferring human evolutionary history from genetic variation data. We use the idea of consensus trees, a technique generally used to reconcile species trees from divergent gene trees, adapting it to the problem of finding robust relationships within a set of intraspecies phylogenies derived from local regions of the genome. Validation on both simulated and real data shows the method to be effective in recapitulating known true structure of the data closely matching our best current understanding of human evolutionary history. Additional comparison with results of leading methods for the problem of population substructure assignment verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to inferring relationships among them. The consensus tree approach thus provides a promising new model for the robust inference of substructure and ancestry from large-scale genetic variation data.     

Structural genomics: correlation blocks, population structure, and genome architecture

An integration of the pattern of genome-wide inter-site associations with evolutionary forces is important for gaining insights into the genomic evolution in natural or artificial populations. Here, we assess the inter-site correlation blocks and their distributions along chromosomes. A correlation block is broadly termed as the DNA segment within which strong correlations exist between genetic diversities at any two sites. We bring together the population genetic structure and the genomic diversity structure that have been independently built on different scales and synthesize the existing theories and methods for characterizing genomic structure at the population level. We discuss how population structure could shape correlation blocks and their patterns within and between populations. Effects of evolutionary forces (selection, migration, genetic drift, and mutation) on the pattern of genome-wide correlation blocks are discussed. In eukaryote organisms, we briefly discuss the associations between the pattern of correlation blocks and genome assembly features in eukaryote organisms, including the impacts of multigene family, the perturbation of transposable elements, and the repetitive nongenic sequences and GC-rich isochores. Our reviews suggest that the observable pattern of correlation blocks can refine our understanding of the ecological and evolutionary processes underlying the genomic evolution at the population level.     

Purifying Selection Causes Widespread Distortions of Genealogical Structure on the Human X Chromosome

The extent to which selective forces shape patterns of genetic and genealogical variation is unknown in many species. Recent theoretical models have suggested that even relatively weak purifying selection may produce significant distortions in gene genealogies, but few studies have sought to quantify this effect in humans. Here, we employ a reconstruction method based on the ancestral recombination graph to infer genealogies across the length of the human X chromosome and to examine time to most recent common ancestor (TMRCA) and measures of tree imbalance at both broad and very fine scales. In agreement with theory, TMRCA is significantly reduced and genealogies are significantly more imbalanced in coding regions and introns when compared to intergenic regions, and these effects are increased in areas of greater evolutionary constraint. These distortions are present at multiple scales, and chromosomal regions as broad as 5 Mb show a significant negative correlation in TMRCA with exon density. We also show that areas of recent TMRCA are significantly associated with the disease-causing potential of site as measured by the MutationTaster prediction algorithm. Together, these findings suggest that purifying selection has significantly distorted human genealogical structure on both broad and fine scales and that few chromosomal regions escape selection-induced distortions.     

Sequential Markov coalescent algorithms for population models with demographic structure

We analyse sequential Markov coalescent algorithms for populations with demographic structure: for a bottleneck model, a population-divergence model, and for a two-island model with migration. The sequential Markov coalescent method is an approximation to the coalescent suggested by McVean and Cardin, and by Marjoram and Wall. Within this algorithm we compute, for two individuals randomly sampled from the population, the correlation between times to the most recent common ancestor and the linkage probability corresponding to two different loci with recombination rate R between them. These quantities characterise the linkage between the two loci in question. We find that the sequential Markov coalescent method approximates the coalescent well in general in models with demographic structure. An exception is the case where individuals are sampled from populations separated by reduced gene flow. In this situation, the correlations may be significantly underestimated. We explain why this is the case.     

Spatially explicit models of divergence and genome hitchhiking

Strong barriers to genetic exchange can exist at divergently selected loci, whereas alleles at neutral loci flow more readily between populations, thus impeding divergence and speciation in the face of gene flow. However, ‘divergence hitchhiking’ theory posits that divergent selection can generate large regions of differentiation around selected loci. ‘Genome hitchhiking’ theory suggests that selection can also cause reductions in average genome‐wide rates of gene flow, resulting in widespread genomic divergence (rather than divergence only around specific selected loci). Spatial heterogeneity is ubiquitous in nature, yet previous models of genetic barriers to gene flow have explored limited combinations of spatial and selective scenarios. Using simulations of secondary contact of populations, we explore barriers to gene flow in various selective and spatial contexts in continuous, two‐dimensional, spatially explicit environments. In general, the effects of hitchhiking are strongest in environments with regular spatial patterning of starkly divergent habitat types. When divergent selection is very strong, the absence of intermediate habitat types increases the effects of hitchhiking. However, when selection is moderate or weak, regular (vs. random) spatial arrangement of habitat types becomes more important than the presence of intermediate habitats per se. We also document counterintuitive processes arising from the stochastic interplay between selection, gene flow and drift. Our results indicate that generalization of results from two‐deme models requires caution and increase understanding of the genomic and geographic basis of population divergence.