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Results for “pool-seq”

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PubMed2025

Adaptation of the Cyst Nematode Globodera pallida to the Colinear Potato Resistant QTLs GpaVvrn and GpaVspl Involved Distinct Genomic Regions and Absence of Cross-Virulence.

The use of alternative methods to control cyst nematode populations has accelerated since the ban of chemical nematicides in Europe. The resistant QTL GpaVvrn, derived from the wild species Solanum vernei, is widely present in resistant European potato cultivars and provides strong protection against Globodera pallida populations although a risk of resistance breakdown has already been demonstrated in both experimental evolution studies and field populations. The wild relative S. sparsipilum, harbouring the resistant QTL GpaVspl, would be an interesting alternative source of resistance to control virulent G. pallida. The goal of the present study was to understand the genomics of adaptation of the nematode to these two colinear resistant QTLs. Starting with two natural populations, an experimental evolution approach allowed, after 10 generations on resistant potato genotypes, selecting independent nematode lineages adapted to each QTL. These virulent lineages were analysed through a combination of phenotyping and genome scans approaches. Phenotyping enabled the quantification of virulence levels and confirmed resistance breakdowns. Pool-Seq whole genome sequencing followed by genome scan analyses identified genomic regions under selection, potentially involved in the adaptive mechanisms to each resistance factor. Candidate genes within these regions provided insights into the genetic basis of adaptation, revealing effectors known to suppress plant immunity. As genome scans highlighted distinct genomic regions for the adaptation to both resistant factors, we were able to predict and phenotypically confirm the absence of cross-virulence between nematode lineages evolving on GpaVvrn and GpaVspl. These findings have significant implications for the design of effective and sustainable resistance management strategies.

Animals↗

PubMed2026

Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.

BACKGROUND: Detecting signals of polygenic adaptation remains a significant challenge in population genomics, as traditional methods often struggle to identify the associated subtle, multi-locus allele-frequency shifts. Here, we introduced and tested several novel approaches combining machine learning techniques with traditional statistical tests to detect polygenic adaptation patterns in time-series of allele frequency changes from whole genome data. We implemented a Naive Bayesian Classifier (NBC) and One-Class Support Vector Machines (OCSVM), and compared their performance against the classical Fisher's Exact Test (FET). Furthermore, we combined machine learning and statistical models (OCSVM-FET and NBC-FET), resulting in 5 competing approaches. The framework is mainly designed and validated for evolve-and-resequence (EaR) experimental designs, where defined selection pressures and temporal sampling are feasible, but might be applicable for certain natural experiments as well. RESULTS: Using a simulated dataset based on empirical C. riparius Pool-Seq data, we evaluated methods across evolutionary scenarios varying in generation, selection strength, and number of loci under selection. Our results demonstrate that the combined OCSVM-FET approach consistently outperformed competing methods, achieving the lowest false positive rate, highest area under the curve, and high accuracy. The performance peak aligned with what we term the 'late dynamic phase' of adaptation - the period after initial selection has occurred but before fixation - highlighting the method's sensitivity to ongoing selective processes. CONCLUSIONS: Furthermore, we emphasize the critical role of parameter tuning, balancing biological assumptions with methodological rigor. While broader applicability remains an important direction for future work, the present benchmarking is intentionally scoped to EaR experimental contexts.

Machine Learning↗

PubMed2024

Rapid vertebrate speciation via isolation, bottlenecks, and drift.

Speciation is often driven by selective processes like those associated with viability, mate choice, or local adaptation, and "speciation genes" have been identified in many eukaryotic lineages. In contrast, neutral processes are rarely considered as the primary drivers of speciation, especially over short evolutionary timeframes. Here, we describe a rapid vertebrate speciation event driven primarily by genetic drift. The White Sands pupfish (Cyprinodon tularosa) is endemic to New Mexico's Tularosa Basin where the species is currently managed as two Evolutionarily significant units (ESUs) and is of international conservation concern (Endangered). Whole-genome resequencing data from each ESU showed remarkably high and uniform levels of differentiation across the entire genome (global FST ≈ 0.40). Despite inhabiting ecologically dissimilar springs and streams, our whole-genome analysis revealed no discrete islands of divergence indicative of strong selection, even when we focused on an array of candidate genes. Demographic modeling of the joint allele frequency spectrum indicates the two ESUs split only ~4 to 5 kya and that both ESUs have undergone major bottlenecks within the last 2.5 millennia. Our results indicate the genome-wide disparities between the two ESUs are not driven by divergent selection but by neutral drift due to small population sizes, geographic isolation, and repeated bottlenecks. While rapid speciation is often driven by natural or sexual selection, here we show that isolation and drift have led to speciation within a few thousand generations. We discuss these evolutionary insights in light of the conservation management challenges they pose.

Animals↗