Genetic architecture of disease resistance and tolerance in Douglas-fir trees.

Understanding the genetic basis of how plants defend against pathogens is important to monitor and maintain resilient tree populations. Swiss needle cast (SNC) and Rhabdocline needle cast (RNC) epidemics are responsible for major damage of forest ecosystems in North America. Here we investigate the genetic architecture of tolerance and resistance to needle cast diseases in Douglas-fir (Pseudotsuga menziesii) caused by two fungal pathogens: SNC caused by Nothophaeocryptopus gaeumannii, and RNC caused by Rhabdocline pseudotsugae. We performed case-control genome-wide association analyses and found disease resistance and tolerance in Douglas-fir to be polygenic and under strong selection. We show that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection, and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identify a major transcriptional regulator of plant defense, ERF1, as the top candidate for RNC resistance. Our findings shed light on the highly polygenic architectures underlying fungal disease resistance and tolerance and have important implications for forestry and conservation as the climate changes.

How useful are genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

The WZA: A window-based method for characterizing genotype-environment associations.

Genotype-environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the Weighted-Z Analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST . Performing GEA methods in analysis windows has the advantage that it takes advantage of the increased linkage disequilibrium expected surrounding sites subject to local adaptation. We analyse simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single-SNP (single nucleotide polymorphism)-based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes were sampled. Particularly troubling, we found that some GEA methods exhibit very high false positive rates. We applied the WZA to previously published data from lodgepole pine and identified candidate loci that were identified in the original study alongside numerous loci that were not found in the original study.

Repeatability of adaptation in sunflowers reveals that genomic regions harbouring inversions also drive adaptation in species lacking an inversion.

Local adaptation commonly involves alleles of large effect, which experience fitness advantages when in positive linkage disequilibrium (LD). Because segregating inversions suppress recombination and facilitate the maintenance of LD between locally adapted loci, they are also commonly found to be associated with adaptive divergence. However, it is unclear what fraction of an adaptive response can be attributed to inversions and alleles of large effect, and whether the loci within an inversion could still drive adaptation in the absence of its recombination-suppressing effect. Here, we use genome-wide association studies to explore patterns of local adaptation in three species of sunflower: Helianthus annuus, Helianthus argophyllus, and Helianthus petiolaris, which each harbour a large number of species-specific inversions. We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an inversion in one species. This shows that while inversions may facilitate local adaptation, at least some of the loci can still harbour mutations that make substantial contributions without the benefit of recombination suppression in species lacking a segregating inversion. While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species.

In plants, like in humans, DNA is arranged into sections known as genes that are in turn organised into structures called chromosomes. Mutations that modify the activity of these genes can help plant species to adapt to a new environment or to extreme conditions such as drought. However, successful adaptation often requires changes in many different genes. If these sets of genes are located close to each other on the same chromosome, any mutations will likely be passed onto the next generation together. If the genes are located further away, or even on different chromosomes, they may instead be inherited separately so that the next generation does not benefit as much from the adaptation. A chromosome inversion ­ when a segment of chromosome breaks off and reattaches the other way around ­ can increase the likelihood that sets of mutations on the same chromosome will be inherited together. Many previous studies have found that chromosome inversions tend to drive the ability of species to adapt to different environments by keeping together mutations that affect the same characteristics. However, it is not clear how inversions affect the repeatability of the adaptation, that is, if another group of closely related plants faced the same challenge in their environment would they evolve in the same way, or would they evolve a new response? To address this question, Soudi, Jahani et al. used a genetics approach known as a genome wide association study to explore how three closely related species of sunflower have adapted to their respective environments. Two of the species grow in various environments across the centre and west of the USA that are often hot and dry, whereas the third species is restricted to the more humid coastal plain of Texas, USA. The experiments found that a few key genes had changed in all three sunflower species. However, each species also had mutations in a larger set of unique genes that were not changed in the other species. Regions of chromosomes harbouring inversions in one of the species tended to have more of the key genes within them, compared to other genomic regions. This was also true for species that did not have inversions in those regions. This demonstrates that genes in regions affected by chromosome inversions can still help plants adapt to changes in the environment even in the absence of inversions. Sunflowers are widely grown for their edible oily seeds. In the future, some of the key genes identified in this work may be useful candidates for plant breeding to improve the resilience of sunflowers to drought, high temperatures and other environmental challenges.

Elucidation of the mescaline biosynthetic pathway in peyote (Lophophora williamsii).

Peyote (Lophophora williamsii) is an entheogenic and medicinal cactus native to the Chihuahuan desert. The psychoactive and hallucinogenic properties of peyote are principally attributed to the phenethylamine derivative mescaline. Despite the isolation of mescaline from peyote over 120 years ago, the biosynthetic pathway in the plant has remained undiscovered. Here, we use a transcriptomics and homology-guided gene discovery strategy to elucidate a near-complete biosynthetic pathway from l-tyrosine to mescaline. We identified a cytochrome P450 that catalyzes the 3-hydroxylation of l-tyrosine to l-DOPA, a tyrosine/DOPA decarboxylase yielding dopamine, and four substrate-specific and regiospecific substituted phenethylamine O-methyltransferases. Biochemical assays with recombinant enzymes or functional analyses performed by feeding putative precursors to engineered yeast (Saccharomyces cerevisiae) strains expressing candidate peyote biosynthetic genes were used to determine substrate specificity, which served as the basis for pathway elucidation. Additionally, an N-methyltransferase displaying broad substrate specificity and leading to the production of N-methylated phenethylamine derivatives was identified, which could also function as an early step in the biosynthesis of tetrahydroisoquinoline alkaloids in peyote.

Using genome scans to identify genes used repeatedly for adaptation.

Adaptation occurring in similar genes or genomic regions in distinct lineages provides evolutionary biologists with a glimpse at the fundamental opportunities for and constraints to diversification. With the widespread availability of high-throughput sequencing technologies and the development of population genetic methods to identify the genetic basis of adaptation, studies have begun to compare the evidence for adaptation at the molecular level among distinct lineages. However, methods to study repeated adaptation are often oriented toward genome-wide testing to identify a set of genes with signatures of repeated use, rather than evaluating the significance at the level of an individual gene. In this study, we propose PicMin, a novel statistical method derived from the theory of order statistics that can test for repeated molecular evolution to estimate significance at the level of an individual gene, using the results of genome scans. This method is generalizable to any number of lineages and, indeed, statistical power to detect repeated adaptation increases with the number of lineages that have signals of repeated adaptation of a given gene in multiple lineages. An implementation of the method written for R can be downloaded from https://github.com/TBooker/PicMin.

Evaluating the accuracy of variant calling methods using the frequency of parent-offspring genotype mismatch.

The use of next-generation sequencing (NGS) data sets has increased dramatically over the last decade, but there have been few systematic analyses quantifying the accuracy of the commonly used variant caller programs. Here we used a familial design consisting of diploid tissue from a single lodgepole pine (Pinus contorta) parent and the maternally derived haploid tissue from 106 full-sibling offspring, where mismatches could only arise due to mutation or bioinformatic error. Given the rarity of mutation, we used the rate of mismatches between parent and offspring genotype calls to infer the single nucleotide polymorphism (SNP) genotyping error rates of FreeBayes, HaplotypeCaller, SAMtools, UnifiedGenotyper, and VarScan. With baseline filtering HaplotypeCaller and UnifiedGenotyper yielded more SNPs and higher error rates by one to two orders of magnitude, whereas FreeBayes, SAMtools and VarScan yielded lower numbers of SNPs and more modest error rates. To facilitate comparison between variant callers we standardized each SNP set to the same number of SNPs using additional filtering, where UnifiedGenotyper consistently produced the smallest proportion of genotype errors, followed by HaplotypeCaller, VarScan, SAMtools, and FreeBayes. Additionally, we found that error rates were minimized for SNPs called by more than one variant caller. Finally, we evaluated the performance of various commonly used filtering metrics on SNP calling. Our analysis provides a quantitative assessment of the accuracy of five widely used variant calling programs and offers valuable insights into both the choice of variant caller program and the choice of filtering metrics, especially for researchers using non-model study systems.

Local Adaptation and the Evolution of Genome Architecture in Threespine Stickleback.

Theory predicts that local adaptation should favor the evolution of a concentrated genetic architecture, where the alleles driving adaptive divergence are tightly clustered on chromosomes. Adaptation to marine versus freshwater environments in threespine stickleback has resulted in an architecture that seems consistent with this prediction: divergence among populations is mainly driven by a few genomic regions harboring multiple quantitative trait loci for environmentally adapted traits, as well as candidate genes with well-established phenotypic effects. One theory for the evolution of these "genomic islands" is that rearrangements remodel the genome to bring causal loci into tight proximity, but this has not been studied explicitly. We tested this theory using synteny analysis to identify micro- and macro-rearrangements in the stickleback genome and assess their potential involvement in the evolution of genomic islands. To identify rearrangements, we conducted a de novo assembly of the closely related tubesnout (Aulorhyncus flavidus) genome and compared this to the genomes of threespine stickleback and two other closely related species. We found that small rearrangements, within-chromosome duplications, and lineage-specific genes (LSGs) were enriched around genomic islands, and that all three chromosomes harboring large genomic islands have experienced macro-rearrangements. We also found that duplicates and micro-rearrangements are 9.9× and 2.9× more likely to involve genes differentially expressed between marine and freshwater genotypes. While not conclusive, these results are consistent with the explanation that strong divergent selection on candidate genes drove the recruitment of rearrangements to yield clusters of locally adaptive loci.

Evolution of polygenic traits under global vs local adaptation.

Observations about the number, frequency, effect size, and genomic distribution of alleles associated with complex traits must be interpreted in light of evolutionary process. These characteristics, which constitute a trait's genetic architecture, can dramatically affect evolutionary outcomes in applications from agriculture to medicine, and can provide a window into how evolution works. Here, I review theoretical predictions about the evolution of genetic architecture under spatially homogeneous, global adaptation as compared with spatially heterogeneous, local adaptation. Due to the tension between divergent selection and migration, local adaptation can favor "concentrated" genetic architectures that are enriched for alleles of larger effect, clustered in a smaller number of genomic regions, relative to expectations under global adaptation. However, the evolution of such architectures may be limited by many factors, including the genotypic redundancy of the trait, mutation rate, and temporal variability of environment. I review the circumstances in which predictions differ for global vs local adaptation and discuss where progress can be made in testing hypotheses using data from natural populations and lab experiments. As the field of comparative population genomics expands in scope, differences in architecture among traits and species will provide insights into how evolution works, and such differences must be interpreted in light of which kind of selection has been operating.

Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species.

Despite their suitability for studying evolution, many conifer species have large and repetitive giga-genomes (16-31 Gbp) that create hurdles to producing high coverage SNP data sets that capture diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies creates further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost-saving benefits of pool-seq and exome-capture to discover SNPs in two conifer species, Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong, positive correlation with those estimated by sequencing the same population as individuals (r > .948), on par with such comparisons made in model organisms. Further, we highlight the utility of haploid megagametophyte tissue for identifying sites that are probably due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > .973). Our work addresses bioinformatic challenges in non-model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralogous artefacts, and suggests the combination of pool-seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

Genome-wide shifts in climate-related variation underpin responses to selective breeding in a widespread conifer.

Locally adapted temperate tree populations exhibit genetic trade-offs among climate-related traits that can be exacerbated by selective breeding and are challenging to manage under climate change. To inform climatically adaptive forest management, we investigated the genetic architecture and impacts of selective breeding on four climate-related traits in 105 natural and 20 selectively bred lodgepole pine populations from western Canada. Growth, cold injury, growth initiation, and growth cessation phenotypes were tested for associations with 18,600 single-nucleotide polymorphisms (SNPs) in natural populations to identify "positive effect alleles" (PEAs). The effects of artificial selection for faster growth on the frequency of PEAs associated with each trait were quantified in breeding populations from different climates. Substantial shifts in PEA proportions and frequencies were observed across many loci after two generations of selective breeding for height, and responses of phenology-associated PEAs differed strongly among climatic regions. Extensive genetic overlap was evident among traits. Alleles most strongly associated with greater height were often associated with greater cold injury and delayed phenology, although it is unclear whether potential trade-offs arose directly from pleiotropy or indirectly via genetic linkage. Modest variation in multilocus PEA frequencies among populations was associated with large phenotypic differences and strong climatic gradients, providing support for assisted gene flow polices. Relationships among genotypes, phenotypes, and climate in natural populations were maintained or strengthened by selective breeding. However, future adaptive phenotypes and assisted gene flow may be compromised if selective breeding further increases the PEA frequencies of SNPs involved in adaptive trade-offs among climate-related traits.

Global adaptation complicates the interpretation of genome scans for local adaptation.

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome-wide distribution of summary statistics measuring genetic differentiation, such as FST , are often interpreted as evidence for alleles that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome-wide distribution of FST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate FST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to FST . However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of FST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Comparative Gene Expression Analysis Reveals Mechanism of Pinus contorta Response to the Fungal Pathogen Dothistroma septosporum.

Many conifers have distributions that span wide ranges in both biotic and abiotic conditions, but the basis of response to biotic stress has received much less attention than response to abiotic stress. In this study, we investigated the gene expression response of lodgepole pine (Pinus contorta) to attack by the fungal pathogen Dothistroma septosporum, which causes Dothistroma needle blight, a disease that has caused severe climate-related outbreaks in northwestern British Columbia. We inoculated tolerant and susceptible pines with two D. septosporum isolates and analyzed the differentially expressed genes (DEGs), differential exon usage, and coexpressed gene modules using RNA-sequencing data. We found a rapid and strong transcriptomic response in tolerant lodgepole pine samples inoculated with one D. septosporum isolate, and a late and weak response in susceptible samples inoculated with another isolate. We mapped 43 of the DEG- or gene module-identified genes to the reference plant-pathogen interaction pathway deposited in the Kyoto Encyclopedia of Genes and Genomes database. These genes are present in PAMP-triggered and effector-triggered immunity pathways. Genes comprising pathways and gene modules had signatures of strong selective constraint, while the highly expressed genes in tolerant samples appear to have been favored by selection to counterattack the pathogen. We identified candidate resistance genes that may respond to D. septosporum effectors. Taken together, our results show that gene expression response to D. septosporum infection in lodgepole pine varies both among tree genotypes and pathogen strains and involves both known candidate genes and a number of genes with previously unknown functions.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Massive haplotypes underlie ecotypic differentiation in sunflowers.

Species often include multiple ecotypes that are adapted to different environments1. However, it is unclear how ecotypes arise and how their distinctive combinations of adaptive alleles are maintained despite hybridization with non-adapted populations2-4. Here, by resequencing 1,506 wild sunflowers from 3 species (Helianthus annuus, Helianthus petiolaris and Helianthus argophyllus), we identify 37 large (1-100 Mbp in size), non-recombining haplotype blocks that are associated with numerous ecologically relevant traits, as well as soil and climate characteristics. Limited recombination in these haplotype blocks keeps adaptive alleles together, and these regions differentiate sunflower ecotypes. For example, haplotype blocks control a 77-day difference in flowering between ecotypes of the silverleaf sunflower H. argophyllus (probably through deletion of a homologue of FLOWERING LOCUS T (FT)), and are associated with seed size, flowering time and soil fertility in dune-adapted sunflowers. These haplotypes are highly divergent, frequently associated with structural variants and often appear to represent introgressions from other-possibly now-extinct-congeners. These results highlight a pervasive role of structural variation in ecotypic adaptation.

Author Correction: Gene clustering and copy number variation in alkaloid metabolic pathways of opium poppy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Variation in recombination rate affects detection of outliers in genome scans under neutrality.

Genome scans can potentially identify genetic loci involved in evolutionary processes such as local adaptation and gene flow. Here, we show that recombination rate variation across a neutrally evolving genome gives rise to mixed sampling distributions of mean FST ( FST^ ), a common population genetic summary statistic. In particular, we show that in regions of low recombination the distribution of FST^ estimates has more variance and a longer tail than in more highly recombining regions. Determining outliers from the genome-wide distribution without taking local recombination rate into consideration may therefore increase the frequency of false positives in low recombination regions and be overly conservative in more highly recombining ones. We perform genome scans on simulated and empirical Drosophila melanogaster data sets and, in both cases, find patterns consistent with this neutral model. Similar patterns are observed for other summary statistics used to capture variation in the coalescent process. Linked selection, particularly background selection, is often invoked to explain heterogeneity in FST^ across the genome, but here we point out that even under neutrality, statistical artefacts can arise due to variation in recombination rate. Our results highlight a flaw in the design of genome-scan studies and suggest that without estimates of local recombination rate, interpreting the genomic landscape of any summary statistic that captures variation in the coalescent process will be very difficult.

The Importance of Genetic Redundancy in Evolution.

Genetic redundancy has been defined in many different ways at different levels of biological organization. Here, we briefly review the general concept of redundancy and focus on the evolutionary importance of redundancy in terms of the number of genotypes that give rise to the same phenotype. We discuss the challenges in determining redundancy empirically, with published experimental examples, and demonstrate the use of the C-score metric to quantify redundancy in evolution studies. We contrast the implicit assumptions of redundancy in quantitative versus population genetic models, show how this contributes to signatures of allele frequency shifts, and highlight how the rapid accumulation of genome-wide association data provides an avenue for further understanding the prevalence and role of redundancy in evolution.

Gene clustering and copy number variation in alkaloid metabolic pathways of opium poppy.

Genes in plant secondary metabolic pathways enable biosynthesis of a range of medically and industrially important compounds, and are often clustered on chromosomes. Here, we study genomic clustering in the benzylisoquinoline alkaloid (BIA) pathway in opium poppy (Papaver somniferum), exploring relationships between gene expression, copy number variation, and metabolite production. We use Hi-C to improve the existing draft genome assembly, yielding chromosome-scale scaffolds that include 35 previously unanchored BIA genes. We find that co-expression of BIA genes increases within clusters and identify candidates with unknown function based on clustering and covariation in expression and alkaloid production. Copy number variation in critical BIA genes correlates with stark differences in alkaloid production, linking noscapine production with an 11-gene deletion, and increased thebaine/decreased morphine production with deletion of a T6ODM cluster. Our results show that the opium poppy genome is still dynamically evolving in ways that contribute to medically and industrially important phenotypes.

Mating system impacts the genetic architecture of adaptation to heterogeneous environments.

Self-fertilisation has consequences for variation across the genome as it reduces effective population size, effect recombination rates and pollen flow, with implications for local adaptation. We conducted simulations of divergent stabilising selection on a quantitative trait with drift, pollen flow, mutation, recombination and different outcrossing rates. We quantified trait divergence and the genetic architecture of adaptation. We conducted an FST outlier analysis to identify candidate loci and quantified the impact of mating system on detectability. Selfing promoted trait divergence mainly through reductions in pollen flow. Moreover, trait architecture became more diffuse with selfing. Average effect size of trait loci was lower, while the number of loci, and their clustering distance increased. The genetic architecture of selfers was also more diffuse than outcrossers for equivalent migration rates. However, when deleterious alleles were included, architectures became more concentrated in selfers, likely to be because of reductions in population size caused by mutational meltdown and impacts of background selection on Ne . Our simulations demonstrate that mating system has important impacts on adaptive divergence of traits and the genetic landscape underlying that divergence. Selfing has a significant effect on detectability of regions of the genome important for adaptation because of neutral divergence and diffuse trait architecture.