Biology subjects

Ramos-Onsins, S. E.

Publications and source records attributed to Ramos-Onsins, S. E..

7 recordsLinked to original sources

bioRxiv2026

Genetic signatures of adaptation to sudden, extreme, and unprecedented environmental changes

Small natural populations are increasingly exposed to environmental shifts that are unprecedented in speed and intensity on population-genetics timescales. If populations persist, adaptation to these abrupt changes is expected to leave localized footprints of selection around the loci contributing to the adaptive response, in contrast to genome-wide signals produced by demography alone. Here we provide an explicit theoretical description of genetic footprints expected under rapid adaptation from standing genetic variation. We derive exact analytical results for how single-locus sweeps and polygenic shifts driven by sudden environmental changes distort the site-frequency spectrum (SFS) of linked mutations, and we link these distortions to the expected changes in standard diversity measures and neutrality statistics. Our results provide a guideline for detecting and interpreting genetic signatures of adaptation to extreme events, including those associated with climate change, particularly in small populations where adaptation or evolutionary rescue often proceed quickly and from preexisting variation.

genetics↗

bioRxiv2026

Bias in diversity estimators and neutrality tests induced by neutral polymorphic structural variants

Estimators of genetic diversity and neutrality tests derived from the site frequency spectrum (SFS), such as Wattersons{theta} W, nucleotide diversity{pi} , Tajimas D, and Fay and Wus H, are designed to be interpreted relative to a baseline defined by the standard neutral SFS. In genomic regions strongly linked to a polymorphic structural variant (SV), deviations from these baselines occur even under strict neutrality: conditioning on an SV at known frequency partitions samples into SV and non-SV haplotypes and distorts the SFS for linked neutral mutations. These deviations are well understood for genomic inversions under long-term balancing selection. However, not all SVs are under strong selection, and the evolution of some SVs may be better approximated as neutral. Here we derive analytical expectations for the unfolded (and, when necessary, folded) SFS of single nucleotide polymorphisms conditional on neutral linked polymorphic SVs, including inversions, deletions, insertions, and introgressions. We use these expectations to quantify the resulting bias in standard diversity estimators and neutrality tests as a function of SV frequency and type. Finally, we discuss approaches to build corrected estimators of diversity and neutrality tests that are unbiased/centered after accounting for the presence and frequency of the SV.

genetics↗

bioRxiv2024

Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants

Structural variants (SVs) such as deletions, inversions, duplications, and Transposable Element (TE) Insertion Polymorphisms (TIPs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider Single Nucleotide Polymorphisms (SNPs) as the only type of genetic marker. Here, we used rice to investigate whether combining the structural and nucleotide genome-wide variation can improve prediction ability of traits when compared to using only SNPs. Moreover, we also examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models, which have been widely applied in genomic prediction. Specifically, the performance of BayesC and a Bayesian Reproducible Kernel Hilbert space regressions were compared to two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network. We further explore their prediction ability by using various marker input strategies and found that exploiting structural and nucleotide variation improves prediction ability on complex traits in rice. Also, DL models outperformed Bayesian models in 75% of the studied cases. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.

genomics↗

bioRxiv2022

Copy number variation on ABCC2-DNMBP loci impacts the diversity and composition of the gut microbiota in pigs

BackgroundGenetic variation in the pig genome partially modulates the composition of porcine gut microbial communities. Previous studies have been focused on the association between single nucleotide polymorphisms (SNPs) and the gut microbiota, but little is known about the relationship between structural variants and gut microbial traits. ResultsThe main goal of this study was to assess the effect of porcine genome copy number variants (CNVs) on the diversity and composition of pig gut microbiota. For this purpose, we used whole-genome sequencing data to undertake a comprehensive identification of CNVs followed by a genome-wide association analysis between the estimated CNV status and the gut bacterial diversity in a commercial Duroc pig population. A CNV predicted as gain (DUP) partially harboring ABCC2-DNMBP loci was associated with richness (p-value=5.41x10-5) and Shannon -diversity (p-value=1.42x10-4). The in-silico predicted gain of copies was validated by real-time quantitative PCR (qPCR), and its segregation, and positive association with the richness and Shannon -diversity of the porcine gut bacterial ecosystem was confirmed in an unrelated F1 (DurocxIberian) cross. Furthermore, despite genetic and environmental differences between both populations, the gut microbiota of DUP samples showed a significant over-abundance of the Desulfovibrio, Blautia, Phascolarctobacterium, Faecalibacterium, Succinivibrio and Anaerovibrio genera. ConclusionsIn summary, this is the first study that evaluate the putative modulatory role of CNVs on pig gut microbiota. Our results advice the relevance of considering the role of host-genome structural variants as modulators of microbial ecosystems, and suggest the ABCC2-DNMBP CNV as a host-genetic factor for the modulation of the diversity and composition of the gut microbiota in pigs.

microbiology↗

bioRxiv2022

Detection of Domestication Signals through the Analysis of the Full Distribution of Fitness Effects using Forward Simulations and Polygenic Adaptation

Domestication is a process marked by complex interactions between demographic changes and selective pressures, which together shape genetic diversity. While the phenotypic outcomes of domestication are well documented, its genetic basis--particularly the dynamics of selection-- remain less well understood. To investigate these dynamics, we performed simulations designed to approximate the demographic history of large domestic mammals. These simulations used selection coefficients as a modeling tool to represent changes in selection pressures, recognizing that such coefficients are abstractions rather than direct representations of biological reality. Specifically, we analyzed site frequency spectra (SFS) under varying distributions of fitness effects (DFE) and proportions of mutations with divergent selective pressures. Our results show that the discretized deleterious DFE can be reliably inferred from the SFS of a single population, but reconstructing the beneficial DFE and demographic history remains challenging, even when using the joint SFS of both populations. We further developed a novel joint DFE inference model to estimate the proportion of mutations with divergent selection coefficients (pc), although we found that signals of classic hard sweeps can mimic increases in pc, complicating interpretation. These findings underscore both the utility and limitations of DFE inference and highlight the need for caution when interpreting demographic histories in domesticated populations based on such modeling assumptions.

evolutionary biology↗

bioRxiv2020

Pervasive selection pressure in wild and domestic pigs

Animal domestication typically affected numerous polygenic quantitative traits, such as behaviour, development and reproduction. However, uncovering the genetic basis of quantitative trait variation is challenging, since it is probably caused by small allele-frequency changes. To date, only a few causative mutations related to domestication processes have been reported, strengthening the hypothesis that small effect variants have a prominent role. So far, the studies on domestication have been limited to the detection of the global effect of domestication on deleterious mutations and on strong beneficial variants, ignoring the importance of variants with small selective effects. In addition, very often, the study of the effects of selection are conducted on genome sequences that contain a non-negligible fraction of missing data, especially in non-model organisms. Hence, appropriate methods to account for these positions are needed. To overcome these difficulties, here we propose to estimate the proportion of beneficial variants using the asymptotic MacDonald-Kreitman (MK) method based on estimates of variability that summarizes the site frequency spectrum (SFS) while accounting for missing data and use them to perform an Approximate Bayesian Computation (ABC) analysis to infer the Distribution of Fitness Effects (DFE) of each population. We applied this approach to 46 genome sequences of pigs from three different populations, one wild and two domestics, with very different demographic histories and selective pressures. The obtained results showed that domestic and wild pig populations do not differ in nonsynonymous fixed mutations. Therefore, differences in estimation among breeds are determined by their polymorphisms. The comparison of for total and exclusive mutations suggests that the different domestic populations have suffered recent divergent changes in their functional versus neutral polymorphisms ratio, while the wild population is compatible with =0. Besides, the DFE inferred with ABC indicates that both wild and domestic pigs display a large number of deleterious mutations at low frequency and a high number of neutral and/or nearly-neutral mutations that may have a significant effect on the evolution of domestic and wild populations. In addition, models not considering beneficial mutations have higher posterior probabilities, suggesting that beneficial mutations are difficult to detect or are scarce. Indeed, for all three populations, the median proportion of the strong favourable mutations are very low ([≤] 0.1%) in those models that includes positive selection, with the average values of weak beneficial mutations around 0.6% for wild boar and 0.8-1.0% for the domestic pigs. Lastly, the analysis based on exclusive mutations showed that recent demographic changes may have severely affected the fitness of populations, especially that of the local Iberian breed.

evolutionary biology↗

bioRxiv2017

The expected neutral frequency spectrum of linked sites

We introduce the conditional Site Frequency Spectrum (SFS) for a genomic region linked to a focal mutation of known frequency. An exact expression for its expected value is provided for the neutral model without recombination. Its relation with the expected SFS for two sites, 2-SFS, is discussed. These spectra derive from the coalescent approach of Fu (1995) for finite samples, which is reviewed. Remarkably simple expressions are obtained for the linked SFS of a large population, which are also solutions of the multiallelic Kolmogorov equations. These formulae are the immediate extensions of the well known single site{theta} /f neutral SFS. Besides the general interest in these spectra, they relate to relevant biological cases, such as structural variants and introgressions. As an application, a recipe to adapt Tajimas D and other SFS-based neutrality tests to a non-recombining region containing a neutral marker is presented.

genetics↗