Genome-wide Allele Frequency Research Articles

Sequential introgression of a carotenoid processing gene underlies sexual ornament diversity in a genus of manakins.

In a hybrid zone between two tropical lekking birds, yellow male plumage of one species has introgressed asymmetrically replacing white plumage of another via sexual selection. Here, we present a detailed analysis of the plumage trait to uncover its physical and genetic bases and trace its evolutionary history. We determine that the carotenoid lutein underlies the yellow phenotype and describe microstructural feather features likely to enhance color appearance. These same features reduce predicted water shedding capacity of feathers, a potential liability in the tropics. Through genome-scale DNA sequencing of hybrids and each species in the genus, we identify BCO2 as the major gene responsible for the color polymorphism. The BCO2 gene tree and genome-wide allele frequency patterns suggest that carotenoid-pigmented collars initially arose in a third species and reached the hybrid zone through historical gene flow. Complex interplay between sexual selection and hybridization has thus shaped phenotypes of these species, where conspicuous sexual traits are key to male reproductive success.

Abstract B012: Prioritization of ancestry-specific variations in disease data helps to alleviate bias in genomic data

Abstract 95% of sequencing data is from individuals of European descent. Underrepresentation of non- European individuals in genomic resources has limited our understanding of the effects of population-specific variants on complex traits and disease. Human populations have undergone selection for variants beneficial to survival in their environment, resulting in individuals with genetic variants unique to their ancestral populations. While these variants can impact disease occurrence and treatment outcome, genetic ancestry is often unaccounted for in disease modeling. Lack of ancestral population representation in data has led to disparate disease outcomes and treatment. While the primary proposed solution to this problem is more diverse sequencing of patients, these efforts will take years of effort and continual work to maintain the diversity balance. Additionally, they will not solve the issue; clinical trials cannot expect to capture human global diversity at every stage. Here we show we can mitigate data bias by utilizing readily available genomic population databases to target genes with ancestrally relevant variations. Our AI model, PhyloFrame, creates equitable gene signatures from transcriptomic input data. PhyloFrame corrects for ancestral bias in data by utilizing genomic population databases and functional interaction networks to identify genes both relevant to disease and associated with a variant unique to a single ancestry. PhyloFrame uses gnomAD data to quantify genome-wide ancestry-specific allele frequencies from a large cohort of healthy individuals, which PhyloFrame uses to create ancestry-agnostic signatures when modeling disease data. Our model demonstrates the utility of including readily available population-level data from healthy individuals in AI methods to overcome existing genomic data bias. PhyloFrame serves also as a demonstration of how equitable AI methods create gene signatures applicable across ancestries without requiring sample-specific ancestry information. Our model demonstrates how AI can be used to help mitigate ancestral bias in genomic data using preexisting data and contribute to equitable representation in medical research. Citation Format: Leslie A. Smith, Kiley Graim, James A. Cahill. Prioritization of ancestry-specific variations in disease data helps to alleviate bias in genomic data [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B012.

An explanation for the sister repulsion phenomenon in Patterson's f-statistics.

Patterson's f-statistics are among the most heavily utilized tools for analyzing genome-wide allele frequency data for demographic inference. Beyond studying admixture, f3- and f4-statistics are also used for clustering populations to identify groups with similar histories. However, previous studies have noted an unexpected behavior of f-statistics: multiple populations from a certain region systematically show higher genetic affinity to a more distant population than to their neighbors, a pattern that is mismatched with alternative measures of genetic similarity. We call this counter-intuitive pattern "sister repulsion". We first present a novel instance of sister repulsion, where genomes from Bronze Age East Anatolian sites show higher affinity toward Bronze Age Greece rather than each other. This is observed both using f3- and f4-statistics, contrasts with archaeological/historical expectation, and also contradicts genetic affinity patterns captured using principal components analysis or multidimensional scaling on genetic distances. We then propose a simple demographic model to explain this pattern, where sister populations receive gene flow from a genetically distant source. We calculate f3- and f4-statistics using simulated genetic data with varying population genetic parameters, confirming that low-level gene flow from an external source into populations from 1 region can create sister repulsion in f-statistics. Unidirectional gene flow between the studied regions (without an external source) can likewise create repulsion. Meanwhile, similar to our empirical observations, multidimensional scaling analyses of genetic distances still cluster sister populations together. Overall, our results highlight the impact of low-level admixture events when inferring demographic history using f-statistics.

Continuously fluctuating selection reveals fine granularity of adaptation.

Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown1,2. Here we generated genome-wide allele frequency data every 1-2 generations from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December (a period of approximately 12 total generations). Adaptation throughout the fundamental ecological phases of population expansion, peak density and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of change in allele frequency to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveala fitness relevance of standing variation that is likely to be masked by inference approaches based on static population sampling or insufficiently resolved time-series data. We propose that such fine-scaled, temporally fluctuating selection may be an important force contributing to the maintenance of functional genetic variation in natural populations and an important stochastic force impacting genome-wide patterns of diversity at linked neutral sites, akin to genetic draft.

Grenedalf: population genetic statistics for the next generation of pool sequencing.

Pool sequencing is an efficient method for capturing genome-wide allele frequencies from multiple individuals, with broad applications such as studying adaptation in Evolve-and-Resequence experiments, monitoring of genetic diversity in wild populations, and genotype-to-phenotype mapping. Here, we present grenedalf, a command line tool written in C++ that implements common population genetic statistics such as θ, Tajima's D, and FST for Pool sequencing. It is orders of magnitude faster than current tools, and is focused on providing usability and scalability, while also offering a plethora of input file formats and convenience options. grenedalf is published under the GPL-3, and freely available at github.com/lczech/grenedalf.

Inference of Locus-Specific Population Mixtures from Linked Genome-Wide Allele Frequencies.

Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and f-statistics in terms of accuracy and sensitivity to detect introgressed loci.

Genome-wide allele frequency changes reveal that dynamic metapopulations evolve differently.

Two important characteristics of metapopulations are extinction-(re)colonization dynamics and gene flow between subpopulations. These processes can cause strong shifts in genome-wide allele frequencies that are generally not observed in "classical" (large, stable, panmictic) populations. Subpopulations founded by one or a few individuals, the so-called propagule model, are initially expected to show intermediate allele frequencies at polymorphic sites until natural selection and genetic drift drive allele frequencies toward a mutation-selection-drift equilibrium characterized by a negative exponential-like distribution of the site frequency spectrum (SFS). We followed changes in SFS distribution in a natural metapopulation of the cyclically parthenogenetic pond-dwelling microcrustacean Daphnia magna using biannual pool-seq samples collected over a five-year period from 118 ponds occupied by subpopulations of known age. As expected under the propagule model, SFSs in newly founded subpopulations trended toward intermediate allele frequencies and shifted toward right skewed distributions as the populations aged. Immigration and subsequent hybrid vigor altered this dynamic. We show that the analysis of SFS dynamics is a powerful approach to understand evolution in metapopulations. It allowed us to disentangle evolutionary processes occurring in a natural metapopulation, where many subpopulations evolve in parallel. Thereby, stochastic processes like founder and immigration events lead to a pattern of subpopulation divergence, while genetic drift, leads to converging SFS distributions in the persisting subpopulations. The observed processes are well explained by the propagule model and highlight that metapopulations evolve differently from classical populations.

Allele frequency dynamics under sex-biased demography and sex-specific inheritance in a pedigreed jay population.

Sex-biased demography, including sex-biased survival or migration, can alter allele frequency changes across the genome. In particular, we can expect different patterns of genetic variation on autosomes and sex chromosomes due to sex-specific differences in life histories, as well as differences in effective population size, transmission modes, and the strength and mode of selection. Here, we demonstrate the role that sex differences in life history played in shaping short-term evolutionary dynamics across the genome. We used a 25-year pedigree and genomic dataset from a long-studied population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of sex-biased demography and inheritance in shaping genome-wide allele frequency trajectories. We used gene dropping simulations to estimate individual genetic contributions to future generations and to model drift and immigration on the known pedigree. We quantified differential expected genetic contributions of males and females over time, showing the impact of sex-biased dispersal in a monogamous system. Due to female-biased dispersal, more autosomal variation is introduced by female immigrants. However, due to male-biased transmission, more Z variation is introduced by male immigrants. Finally, we partitioned the proportion of variance in allele frequency change through time due to male and female contributions. Overall, most allele frequency change is due to variance in survival and births. Males and females make similar contributions to autosomal allele frequency change, but males make higher contributions to allele frequency change on the Z chromosome. Our work shows the importance of understanding sex-specific demographic processes in characterizing genome-wide allele frequency change in wild populations.

The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change

Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 y, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.

Genome-wide allele frequency studies in Pacific oyster families identify candidate genes for tolerance to ostreid herpesvirus 1 (OsHV-1)

BackgroundHost genetics influences the development of infectious diseases in many agricultural animal species. Identifying genes associated with disease development has the potential to make selective breeding for disease tolerance more likely to succeed through the selection of different genes in diverse signaling pathways. In this study, four families of Pacific oysters (Crassostrea gigas) were identified to be segregating for a quantitative trait locus (QTL) on chromosome 8. This QTL was previously found to be associated with basal antiviral gene expression and survival to ostreid herpesvirus 1 (OsHV-1) mortality events in Tomales Bay, California. Individuals from these four families were phenotyped and genotyped in an attempt to find candidate genes associated with the QTL on chromosome 8.ResultsGenome-wide allele frequencies of oysters from each family prior to being planting in Tomales Bay were compared with the allele frequencies of oysters from respective families that survived an OsHV-1 mortality event. Six significant unique QTL were identified in two families in these genome-wide allele frequency studies, all of which were located on chromosome 8. Three QTL were assigned to candidate genes (ABCA1, PIK3R1, and WBP2) that have been previously associated with antiviral innate immunity in vertebrates.ConclusionThe identification of vertebrate antiviral innate immunity genes as candidate genes involved in molluscan antiviral innate immunity reinforces the similarities between the innate immune systems of these two groups. Causal variant identification in these candidate genes will enable future functional studies of these genes in an effort to better understand their antiviral modes of action.

The effect of ctDNA tumor fraction (TF) on overall survival and concordance between tissue genomics and ctDNA in Lung-MAP.

9035 Background: Discordance in genomic profiling between tumor tissue and ctDNA can occur due to inter-patient variability in shed tumor DNA, and intra-patient variability in sources of cell-free DNA (tumor heterogeneity, metastatic variants, CHIP). TF is an aneuploidy-based estimation of tumor-specific DNA in circulation. We evaluated the impact of TF levels on overall survival (OS), mutation concordance and tumor mutation burden in tissue (tTMB) and ctDNA (bTMB) in the LUNGMAP screening study. Methods: Eligible advanced NSCLC pts had blood drawn ≤30 days of fresh tissue biopsy with no intervening therapies. Genomic profiling was done by FoundationOne CDx and FoundationOne Liquid CDx. ctDNA TF was quantified via aneuploidy using deviations in genome-wide SNP coverage or somatic allele frequencies if under aneuploidy LoD. High TF was defined as ≥10%. Concordance among driver genes, cancer genes of interest (CGoI: T P53, PIK3CA, STK11, KEAP1, RB1, PTEN, ATM, BRCA1/2), and variant subtype were assessed by the percentage positive agreement (PPA) and positive predictive value (PPV) with 95% confidence intervals (CI). OS by TF used a Cox model and log-rank test. PPA and TF among CGoIs was compared with a Wilcoxon test. Correlation between tTMB and bTMB used Lin’s coefficient (LC). Results: 167 pts met inclusion criteria; 161 had TF data with 51 (32%) ≥10% (high TF). High TF was associated with worse OS (HR: 2.06 [1.43-2.95], p<0.001). The PPA and PPV ranged from 50%-100% and 60%-100%, respectively for driver genes. The PPA was numerically larger for high versus low TF for most drivers (p=0.18) and significantly larger for all mutations and SNVs combined (Table). For CGoI, the distribution of PPA differed between high and low TF (p=0.03). The median PPA for CGoI (25th%ile,75th%ile) was 56% (50,87) for low TF and 100% (100,100) for high TF. PPV for CGoI did not differ by TF (p=0.20). High TF was associated with higher levels of TMB and bTMB (p<0.01 for both). TMB LC (CI) for low and high TF were 0.46 (0.27,0.61) and 0.69 (0.53,0.81). Conclusions: High TF was associated with worse OS, better PPA among CGoI, and better LC for t/bTMB. TF may improve the clinical utility of mutations detected by liquid biopsies and should be reported and considered in interpreting these results. [Table: see text]

Selective effects of a short transient environmental fluctuation on a natural population.

Natural populations experience continuous and often transient changes of environmental conditions. These in turn may result in fluctuating selection pressures leading to variable demographic and evolutionary population responses. Rapid adaptation as short-term response to a sudden environmental change has in several cases been attributed to polygenic traits, but the underlying genomic dynamics and architecture are poorly understood. In this study, we took advantage of a natural experiment in an insect population of the non-biting midge Chironomus riparius by monitoring genome-wide allele frequencies before and after a cold snap event. Whole genome pooled sequencing of time series samples revealed 10 selected haplotypes carrying ancient polymorphisms, partially with signatures of balancing selection. By constantly cold exposing genetically variable individuals in the laboratory, we could demonstrate with whole genome resequencing (i) that among the survivors, the same alleles rose in frequency as in the wild, and (ii) that the identified variants additively predicted fitness (survival time) of its bearers. Finally, by simultaneously sequencing the genome and the transcriptome of cold exposed individuals we could tentatively link some of the selected SNPs to the cis- and trans-regulation of genes and pathways known to be involved in cold response of insects, such as cytochrome P450 and fatty acid metabolism. Altogether, our results shed light on the strength and speed of selection in natural populations and the genomic architecture of its underlying polygenic trait. Population genomic time series data thus appear as promising tool for measuring the selective tracking of fluctuating selection in natural populations.

Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance.

With the increasing number of sequencing projects involving families, quality control tools optimized for family genome sequencing are needed. However, accurately quantifying contamination in a DNA mixture is particularly difficult when genetically related family members are the sources. We developed TrioMix, a maximum likelihood estimation (MLE) framework based on Mendel's law of inheritance, to quantify DNA mixture between family members in genome sequencing data of parent-offspring trios. TrioMix can accurately deconvolute any intrafamilial DNA contamination, including parent-offspring, sibling-sibling, parent-parent, and even multiple familial sources. In addition, TrioMix can be applied to detect genomic abnormalities that deviate from Mendelian inheritance patterns, such as uniparental disomy (UPD) and chimerism. A genome-wide depth and variant allele frequency plot generated by TrioMix facilitates tracing the origin of Mendelian inheritance deviations. We showed that TrioMix could accurately deconvolute genomes in both simulated and real data sets.

Genomic architecture of adaptive radiation and hybridization in Alpine whitefish

Adaptive radiations represent some of the most remarkable explosions of diversification across the tree of life. However, the constraints to rapid diversification and how they are sometimes overcome, particularly the relative roles of genetic architecture and hybridization, remain unclear. Here, we address these questions in the Alpine whitefish radiation, using a whole-genome dataset that includes multiple individuals of each of the 22 species belonging to six ecologically distinct ecomorph classes across several lake-systems. We reveal that repeated ecological and morphological diversification along a common environmental axis is associated with both genome-wide allele frequency shifts and a specific, larger effect, locus, associated with the gene edar. Additionally, we highlight the possible role of introgression between species from different lake-systems in facilitating the evolution and persistence of species with unique trait combinations and ecology. These results highlight the importance of both genome architecture and secondary contact with hybridization in fuelling adaptive radiation.

A Malaria Parasite Cross Reveals Genetic Determinants of Plasmodium falciparum Growth in Different Culture Media.

What genes determine in vitro growth and nutrient utilization in asexual blood-stage malaria parasites? Competition experiments between NF54, clone 3D7, a lab-adapted African parasite, and a recently isolated Asian parasite (NHP4026) reveal contrasting outcomes in different media: 3D7 outcompetes NHP4026 in media containing human serum, while NHP4026 outcompetes 3D7 in media containing AlbuMAX, a commercial lipid-rich bovine serum formulation. To determine the basis for this polymorphism, we conducted parasite genetic crosses using humanized mice and compared genome-wide allele frequency changes in three independent progeny populations cultured in media containing human serum or AlbuMAX. This bulk segregant analysis detected three quantitative trait loci (QTL) regions [on chromosome (chr) 2 containing aspartate transaminase AST; chr 13 containing EBA-140; and chr 14 containing cysteine protease ATG4] linked with differential growth in serum or AlbuMAX in each of the three independent progeny pools. Selection driving differential growth was strong (s = 0.10 – 0.23 per 48-hour lifecycle). We conducted validation experiments for the strongest QTL on chr 13: competition experiments between ΔEBA-140 and 3D7 wildtype parasites showed fitness reversals in the two medium types as seen in the parental parasites, validating this locus as the causative gene. These results (i) demonstrate the effectiveness of bulk segregant analysis for dissecting fitness traits in P. falciparum genetic crosses, and (ii) reveal intimate links between red blood cell invasion and nutrient composition of growth media. Use of parasite crosses combined with bulk segregant analysis will allow systematic dissection of key nutrient acquisition/metabolism and red blood cell invasion pathways in P. falciparum.

Long evolutionary history of an emerging fungal pathogen of diverse tree species in eastern Asia, Australia and the Pacific Islands.

Emerging plant pathogens have been increasing exponentially over the last century. To address this issue, it is critical to determine whether these pathogens are native to ecosystems or have been recently introduced. Understanding the ecological and evolutionary processes fostering emergence can help to manage their spread and predict epidemics/epiphytotics. Using restriction site-associated DNA sequencing data, we studied genetic relationships, pathways of spread and the evolutionary history of Phellinus noxius, an emerging root-rotting fungus of unknown origin, in eastern Asia, Australia and the Pacific Islands. We analysed patterns of genetic variation using Bayesian inference, maximum-likelihood phylogeny, population splits and mixtures measuring correlations in allele frequencies and genetic drift, and finally applied coalescent-based theory using Approximate Bayesian computation (ABC) with supervised machine learning. Population structure analyses revealed five genetic groups with signatures of complex recent and ancient migration histories. The most probable scenario of ancient pathogen spread is movement from an unsampled population to Malaysia and the Pacific Islands, with subsequent spread to Taiwan and Australia. Furthermore, ABC analyses indicate P. noxius spread occurred thousands of generations ago, contradicting previous assumptions that this pathogen was recently introduced to multiple geographical regions. Our results suggest that recent emergence of P. noxius in eastern Asia, Australia and the Pacific Islands has probably been driven by anthropogenic and natural disturbances, such as deforestation, land-use change, severe weather events and/or introduction of exotic plants. This study provides a novel example of applying genome-wide allele frequency data to unravel the dynamics of pathogen emergence under changing ecosystem conditions.

Whole genome sequencing and RNA-seq evaluation allowed to detect Cd adaptation footprint in Chironomus riparius

Evolutionary adaptation and phenotypic plasticity are important processes on how organisms respond to pollutant exposure. We dissected here the contribution of both processes to increased tolerance in Chironomus riparius to cadmium (Cd) exposure in a multi-generation experiment and inferred the underlying genomic basis. We simulated environmentally realistic conditions by continuously increasing contaminant concentration in six replicates initiated with 1000 larvae each, three pre-exposed to Cd and three not exposed to Cd (no-Cd) over eight generations. We measured life-cycle traits, transcriptomic responses and genome-wide allele frequency changes from this evolve and resequencing (E&R) experiment. Overall, life cycle tests revealed little phenotypic adaptation to Cd exposure, but a slightly increase in survival in the first larval stage was observed. Population genomic analyses showed a strong genome-wide selective response in all replicates, highlighting two main biological functions involved in development and growth of the chironomids. Emphasizing that laboratory conditions continually exert selective pressure. However, the integration of the transcriptomic to the genomic data allowed to distinguish pathways specifically selected by the Cd exposure related to microtubules and organelles and cellular movement. Those pathways could be functionally related to an excretion of metals. Thus, our results indicate that genetic adaptation to Cd in C. riparius can happen within few generations under an environmentally relevant exposure scenario, but substantial phenotypic tolerance might take more time to arise. With our approach, we introduce an experimental setup to fill the existing gap in evolutionary ecotoxicology to investigate these early signs of genetic adaptation.

Genomic Analyses of Globodera pallida, A Quarantine Agricultural Pathogen in Idaho.

Globodera pallida is among the most significant plant-parasitic nematodes worldwide, causing major damage to potato production. Since it was discovered in Idaho in 2006, eradication efforts have aimed to contain and eradicate G. pallida through phytosanitary action and soil fumigation. In this study, we investigated genome-wide patterns of G. pallida genetic variation across Idaho fields to evaluate whether the infestation resulted from a single or multiple introduction(s) and to investigate potential evolutionary responses since the time of infestation. A total of 53 G. pallida samples (~1,042,000 individuals) were collected and analyzed, representing five different fields in Idaho, a greenhouse population, and a field in Scotland that was used for external comparison. According to genome-wide allele frequency and fixation index (Fst) analyses, most of the genetic variation was shared among the G. pallida populations in Idaho fields pre-fumigation, indicating that the infestation likely resulted from a single introduction. Temporal patterns of genome-wide polymorphisms involving (1) pre-fumigation field samples collected in 2007 and 2014 and (2) pre- and post-fumigation samples revealed nucleotide variants (SNPs, single-nucleotide polymorphisms) with significantly differentiated allele frequencies indicating genetic differentiation. This study provides insights into the genetic origins and adaptive potential of G. pallida invading new environments.

Comparative Transcriptomics and RNA-Seq-Based Bulked Segregant Analysis Reveals Genomic Basis Underlying Cronartium ribicola vcr2 Virulence.

Breeding programs of five-needle pines have documented both major gene resistance (MGR) and quantitative disease resistance (QDR) to Cronartium ribicola (Cri), a non-native, invasive fungal pathogen causing white pine blister rust (WPBR). WPBR is one of the most deadly forest diseases in North America. However, Cri virulent pathotypes have evolved and can successfully infect and kill trees carrying resistance (R) genes, including vcr2 that overcomes MGR conferred by the western white pine (WWP, Pinus monticola) R gene (Cr2). In the absence of a reference genome, the present study generated a vcr2 reference transcriptome, consisting of about 20,000 transcripts with 1,014 being predicted to encode secreted proteins (SPs). Comparative profiling of transcriptomes and secretomes revealed vcr2 was significantly enriched for several gene ontology (GO) terms relating to oxidation-reduction processes and detoxification, suggesting that multiple molecular mechanisms contribute to pathogenicity of the vcr2 pathotype for its overcoming Cr2. RNA-seq-based bulked segregant analysis (BSR-Seq) revealed genome-wide DNA variations, including about 65,617 single nucleotide polymorphism (SNP) loci in 7,749 polymorphic genes shared by vcr2 and avirulent (Avcr2) pathotypes. An examination of the distribution of minor allele frequency (MAF) uncovered a high level of genomic divergence between vcr2 and Avcr2 pathotypes. By integration of extreme-phenotypic genome-wide association (XP-GWAS) analysis and allele frequency directional difference (AFDD) mapping, we identified a set of vcr2-associated SNPs within functional genes, involved in fungal virulence and other molecular functions. These included six SPs that were top candidate effectors with putative activities of reticuline oxidase, proteins with common in several fungal extracellular membrane (CFEM) domain or ferritin-like domain, polysaccharide lyase, rds1p-like stress responsive protein, and two Cri-specific proteins without annotation. Candidate effectors and vcr2-associated genes provide valuable resources for further deciphering molecular mechanisms of virulence and pathogenicity by functional analysis and the subsequent development of diagnostic tools for monitoring the virulence landscape in the WPBR pathosystems.

OptM: estimating the optimal number of migration edges on population trees using Treemix.

The software Treemix has become extensively used to estimate the number of migration events, or edges (m), on population trees from genome-wide allele frequency data. However, the appropriate number of edges to include remains unclear. Here, I show that an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. Repurposed from its original use to estimate the number of population clusters in the software Structure (ΔK), I show using simulated populations that Δm performs equally as well as current recommendations for Treemix. A demonstration of an empirical dataset from domestic dogs indicates that this method may be preferable in large, complex population histories and can prioritize migration events for subsequent investigation. The method has been implemented in a freely available R package called “OptM” and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix.