On Non-Random Mating, Adaptive Evolution, and Information Theory.

Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms' adaptation to their environment. In this paper, I propose a model that begins with the distribution of mating based on mutual fitness and progresses to viable adult genotype distribution.

iHDSel software: The price equation and the population stability index to detect genomic patterns compatible with selective sweeps. An example with SARS-CoV-2.

A large number of methods have been developed and continue to evolve for detecting the signatures of selective sweeps in genomes. Significant advances have been made, including the combination of different statistical strategies and the incorporation of artificial intelligence (machine learning) methods. Despite these advances, several common problems persist, such as the unknown null distribution of the statistics used, necessitating simulations and resampling to assign significance to the statistics.

Unifying quantification methods for sexual selection and assortative mating using information theory.

Sexual selection plays a crucial role in modern evolutionary theory, offering valuable insight into evolutionary patterns and species diversity. Recently, a comprehensive definition of sexual selection has been proposed, defining it as any selection that arises from fitness differences associated with nonrandom success in the competition for access to gametes for fertilization. Previous research on discrete traits demonstrated that non-random mating can be effectively quantified using Jeffreys (or symmetrized Kullback-Leibler) divergence, capturing information acquired through mating influenced by mutual mating propensities instead of random occurrences.

1LocusSim a mobile-friendly simulator for teaching population genetics.

Motivation: Biology students often struggle with the fundamental concepts of evolutionary genetics, including genetic drift, mutation and selection. To address this problem, 1LocusSim was developed to simulate the interaction of different factors, such as population size, mutation, selection and dominance, to study their effect on allelic frequency during evolution. With 1LocusSim, students can compare theoretical results with simulation outputs and solve and analyze different problems of population genetics.

Detecting Local Adaptation between North and South European Atlantic Salmon Populations.

Pollution and other anthropogenic effects have driven a decrease in Atlantic salmon () in the Iberian Peninsula. The restocking effort carried out in the 1980s, with salmon from northern latitudes with the aim of mitigating the decline of native populations, failed, probably due to the deficiency in adaptation of foreign salmon from northern Europe to the warm waters of the Iberian Peninsula. This result would imply that the Iberian populations of Atlantic salmon have experienced local adaptation in their past evolutionary history, as has been described for other populations of this species and other salmonids.

A Simulation Study of the Ecological Speciation Conditions in the Galician Marine Snail .

In the last years, the interest in evolutionary divergence at small spatial scales has increased and so did the study of speciation caused by ecologically based divergent natural selection. The evolutionary interplay between gene flow and local adaptation can lead to low-dispersal locally adapted specialists. When this occurs, the evolutionary interplay between gene flow and local adaptation could eventually lead to speciation.

Evaluation of Existing Methods for High-Order Epistasis Detection.

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions.

Toxo: a library for calculating penetrance tables of high-order epistasis models.

Background: Epistasis is defined as the interaction between different genes when expressing a specific phenotype. The most common way to characterize an epistatic relationship is using a penetrance table, which contains the probability of expressing the phenotype under study given a particular allele combination. Available simulators can only create penetrance tables for well-known epistasis models involving a small number of genes and under a large number of limitations.

Data and models from multi-model inference of non-random mating from an information theoretic approach.

This is a co-submission with Multi-model inference of non-random mating from an information theoretic approach [1]. These data corresponds to the complete simulated data set jointly with the set of models defined for analysing the data. The simulated data set was obtained using the program MateSim [2].

Multi-model inference of non-random mating from an information theoretic approach.

Non-random mating has a significant impact on the evolution of organisms. Here, I developed a modelling framework for discrete traits (with any number of phenotypes) to explore different models connecting the non-random mating causes (mate competition and/or mate choice) and their consequences (sexual selection and/or assortative mating). I derived the formulaefor the maximum likelihood estimates of each model and used information criteria to perform multi-model inference.

Geographic and Temporal Variation of Distinct Intracellular Endosymbiont Strains of Wolbachia sp. in the Grasshopper Chorthippus parallelus: a Frequency-Dependent Mechanism?

Wolbachia is an intracellular endosymbiont that can produce a range of effects on host fitness, but the temporal dynamics of Wolbachia strains have rarely been experimentally evaluated. We compare interannual strain frequencies along a geographical region for understanding the forces that shape Wolbachia strain frequency in natural populations of its host, Chorthippus parallelus (Orthoptera, Acrididae). General linear models show that strain frequency changes significantly across geographical and temporal scales.

A generalization of the informational view of non-random mating: Models with variable population frequencies.

The mating distribution caused by mate choice can be expressed as a gain in information with respect to random mating. In that view, the population phenotype frequencies had been considered constant during the breeding season. Here, such restriction was relaxed to consider encounter-mating processes in which first, the encounter between partners depends on the phenotype distribution of the population, and second, the mating after the encounter depends on the mutual mating propensities.

MateSim: Monte Carlo simulation for the generation of mating tables.

In species with sexual reproduction, the mating pattern is an important element for understanding evolutionary and speciation processes. Given a mating pool where individuals can encounter each other randomly, the individual mating preferences define the mating frequencies in the population. However, in every mating process we can distinguish two different steps.

Non-random mating and information theory.

In this work, mate choice is modeled by means of the abstract concept of mutual mating propensity. This only assumes that different types of couples can have different mating success. The model is adequate for any population where mating occurs among distinct types.

Myriads: P-value-based multiple testing correction.

Motivation: There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P-values as currently demanded by microarray and other omic technologies.

A novel method for estimating the strength of positive mating preference by similarity in the wild.

Mating preference can be a driver of sexual selection and assortative mating and is, therefore, a key element in evolutionary dynamics. Positive mating preference by similarity is the tendency for the choosy individual to select a mate which possesses a similar variant of a trait. Such preference can be modelled using Gaussian-like mathematical functions that describe the strength of preference, but such functions cannot be applied to empirical data collected from the field.

HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations.

The detection of genomic regions involved in local adaptation is an important topic in current population genetics. There are several detection strategies available depending on the kind of genetic and demographic information at hand. A common drawback is the high risk of false positives.

The scale-of-choice effect and how estimates of assortative mating in the wild can be biased due to heterogeneous samples.

The mode in which sexual organisms choose mates is a key evolutionary process, as it can have a profound impact on fitness and speciation. One way to study mate choice in the wild is by measuring trait correlation between mates. Positive assortative mating is inferred when individuals of a mating pair display traits that are more similar than those expected under random mating while negative assortative mating is the opposite.

Detecting the Genomic Signature of Divergent Selection in Presence of Gene Flow.

The study of local adaptation is a main focus of evolutionary biology since it may contribute to explain the current species diversity. The genomic scan procedures permit for the first time to study the connection between specific DNA patterns and processes as natural selection, genetic drift, recombination, mutation and gene flow. Accordingly, the information on genomes from non-model organisms increases and the interest on detecting the signal of natural selection in the DNA sequences of different populations also raises.

Impact of deep coalescence and recombination on the estimation of phylogenetic relationships among species using AFLP markers.

Deep coalescence and the nongenealogical pattern of descent caused by recombination have emerged as a common problem for phylogenetic inference at the species level. Here we use computer simulations to assess whether AFLP-based phylogenies are robust to the uncertainties introduced by these factors. Our results indicate that phylogenetic signal can prevail even in the face of extensive deep coalescence allowing recovering the correct species tree topology.

Teaching the fluctuation test in silico by using mutate: a program to distinguish between the adaptive and spontaneous mutation hypotheses.

Mutate is a program developed for teaching purposes to impart a virtual laboratory class for undergraduate students of Genetics in Biology. The program emulates the so-called fluctuation test whose aim is to distinguish between spontaneous and adaptive mutation hypotheses in bacteria. The plan is to train students in certain key multidisciplinary aspects of current genetics such as sequence databases, DNA mutations, and hypothesis testing, while introducing the fluctuation test.

AFLPMax: a user-friendly application for computing the optimal number of amplified fragment length polymorphism markers needed in phylogenetic reconstruction.

Amplified fragment length polymorphisms (AFLPs) are widely used for phylogenetic inference especially in non-model species. Frequently, trees obtained with other nuclear or mitochondrial markers or with morphological information need additional resolution, increased branch support, or independent data sources (i.e.

Assessing significance in high-throughput experiments by sequential goodness of fit and q-value estimation.

We developed a new multiple hypothesis testing adjustment called SGoF+ implemented as a sequential goodness of fit metatest which is a modification of a previous algorithm, SGoF, taking advantage of the information of the distribution of p-values in order to fix the rejection region. The new method uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. This new approach shows a better power/pFDR ratio than SGoF.

Multiple hypothesis testing in proteomics: a strategy for experimental work.

In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate method that deal with this problem have been disseminated for more than one decade.

'SGoFicance Trace': assessing significance in high dimensional testing problems.

Recently, an exact binomial test called SGoF (Sequential Goodness-of-Fit) has been introduced as a new method for handling high dimensional testing problems. SGoF looks for statistical significance when comparing the amount of null hypotheses individually rejected at level γ = 0.05 with the expected amount under the intersection null, and then proceeds to declare a number of effects accordingly.