Characterizing the evolution of genetic variance using genetic covariance tensors

Determining how genetic variance changes under selection in natural populations has proved to be a very resilient problem in evolutionary genetics. In the same way that understanding the availability of genetic variance within populations requires the simultaneous consideration of genetic variance in sets of functionally related traits, determining how genetic variance changes under selection in natural populations will require ascertaining how genetic variance–covariance (G) matrices evolve. Here, we develop a geometric framework using higher-order tensors, which enables the empirical characterization of how G matrices have diverged among populations. We then show how divergence among populations in genetic covariance structure can then be associated with divergence in selection acting on those traits using key equations from evolutionary theory. Using estimates of G matrices of eight male sexually selected traits from nine geographical populations of Drosophila serrata, we show that much of the divergence in genetic variance occurred in a single trait combination, a conclusion that could not have been reached by examining variation among the individual elements of the nine G matrices. Divergence in G was primarily in the direction of the major axes of genetic variance within populations, suggesting that genetic drift may be a major cause of divergence in genetic variance among these populations.     

Restoration of genetic variation lost - the genetic rescue hypothesis

It has been long known that immigrants from surrounding populations might prevent the extinction of small populations, a process known as the 'rescue effect'. This focuses on the demographic effects of migration through the direct positive influence that immigrants have on abundance of the recipient population. Now, two recent papers have indicated another potentially important way that migration might rescue populations from extinction - replenishing genetic variation and reducing inbreeding depression, or what has been termed 'genetic rescue'.     

Lateral genetic transfer and the construction of genetic exchange communities

Lateral genetic transfer (LGT) is a major source of phenotypic innovation among bacteria. Determinants for antibiotic resistance and other adaptive traits can spread rapidly, particularly by conjugative plasmids, but also phages and natural transformation. Each successive step from the uptake of foreign DNA, its genetic recombination and regulatory integration, to its establishment in the host population presents differential barriers and opportunities. The emergence of successive multidrug-resistant strains of Staphylococcus aureus illustrates the ongoing role of LGT in the combinatorial assembly of pathogens. The dynamic interplay among hosts, vectors, DNA elements, combinations of genetic determinants and environments constructs communities of genetic exchange. These relations can be abstracted as a graph, within which an exchange community might correspond to a path, transitively closed set, clique or near-clique. We provide a set-based definition, and review the features of actual genetic exchange communities (GECs), adopting first a knowledge-driven approach based on literature, and then a synoptic data-centric bioinformatic approach. GECs are diverse, but share some common features. Differential opportunity and barriers to lateral genetic transfer create bacterial communities of exchange.     

Assessing population genetic structure via the maximisation of genetic distance

Background

The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.

Methods

In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.

Results

The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for F_ST ≥ 0.03, but only STRUCTURE estimates the correct number of clusters for F_ST as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.

Conclusion

This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.     

Study of the genetic code adaptability by means of a genetic algorithm

We used simulated evolution to study the adaptability level of the canonical genetic code. An adapted genetic algorithm (GA) searches for optimal hypothetical codes. Adaptability is measured as the average variation of the hydrophobicity that the encoded amino acids undergo when errors or mutations are present in the codons of the hypothetical codes. Different types of mutations and point mutation rates that depend on codon base number are considered in this study. Previous works have used statistical approaches based on randomly generated alternative codes or have used local search techniques to determine an optimum value. In this work, we emphasize what can be concluded from the use of simulated evolution considering the results of previous works. The GA provides more information about the difficulty of the evolution of codes, without contradicting previous studies using statistical or engineering approaches. The GA also shows that, within the coevolution theory, the third base clearly improves the adaptability of the current genetic code.     

Mistranslation of the genetic code

During mRNA decoding at the ribosome, deviations from stringent codon identity, or “mistranslation,” are generally deleterious and infrequent. Observations of organisms that decode some codons ambiguously, and the discovery of a compensatory increase in mistranslation frequency to combat environmental stress have changed the way we view “errors” in decoding. Modern tools for the study of the frequency and phenotypic effects of mistranslation can provide quantitative and sensitive measurements of decoding errors that were previously inaccessible. Mistranslation with non-protein amino acids, in particular, is an enticing prospect for new drug therapies and the study of molecular evolution.     

Working of the genetic code

The genetic code is used differently by different kinds of species. Each type of genome has a particular coding strategy, that is, choices among degenerate bases are consistently similar for all genes therein. This uniformity in the selection between degenerate bases within each taxonomic group has been discovered by applying new methods to the study of coding variability. It is now possible to calculate relative distances between genomes, or genome types, based on use of the codon catalog by the mRNAs therein.     

Development of genetic techniques and the genetic map of the yeast Saccharomycopis lipolytica

Summary Genetically useful strains of the hydrocarbon-utilizing yeast Saccharomycopsis lipolytica were developed through extensive inbreeding. Spore viability and the percentage of 4-spored asci were increased to the point where tetrad analysis was possible. Procedures for mutant isolation and scoring, maintenance of stocks, mating, sporulation, complementation, tetrad and random spore analysis have been developed for these inbred strains. Sixty seven mutations in fiftyeight genes have been isolated and utilized in mapping studies. Twenty-two cases of linkage have been detected among the 278 gene pairs investigated. Six linkage fragments have been established and a few genes ordered in these fragments. No centromere, linked markers have yet been detected. Evidence for gene conversion, mitotic recombination and diploidization in S. lipolytica is presented.     

On the origin of the genetic code

It is argued that three chemical criteria determined the evolution of the genetic code: codon-anticodon pairing; codon-amino acid pairing; amino acid pairing. The first criterium determined the set of interactive nucleotides; the second, the set of nucleotides interactive with amino acids; the third, the set of mutually interactive amino acids. The code resulted from the intersection of these sets. This hypothesis explains the specificity and universality of the code as well as the “choice” of the standard amino acids and nucleotides from among those available in nature. The specific mechanism for codon-amino acid pairing assumed here is the “backwards” (Crick, 1967) Pelc-Welton (1966) models. Three types of evidence support “backwards” pairing: parallel genetic coding of amino acid pairs (Root-Bernstein, 1982); results of binding experiments by Saxinger and Ponnamperuma (1974); reinterpretation of Jungck's (1978) correlations between the properties of amino acids and their respective anticodon nucleotides. The inversion of the code to its present state occurred as a result of the evolution of tRNA molecules which supplanted parallel codon-amino acid interactions with antiparallel codon-anticodon ones. The paper concludes with suggestions for testing the hypothesis.     

Likelihood of a particular order of genetic markers and the construction of genetic maps

We model the recombination process of fungal systems via chromatid exchange in meiosis, which accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers, for both random spore and tetrad data. First, a probability model framework is developed for two genes and then generalized for an arbitrary number of genes. Maximum likelihood estimators (MLEs) for both random and tetrad data are developed. It is shown that the MLE of recombination for tetrad data is uniformly more efficient over that from random spore data by a factor of at least 4 usually. The MLE for the generalized probability framework is computed using the expectation-maximization (EM) algorithm. Pearson's chi-squared statistic is computed as a measure of goodness of fit using a product-multinomial setup. We implement our model with genetic marker data on the whole genome of Neurospora crassa. Simulated annealing is used to search for the best order of genetic markers for each chromosome, and the goodness of fit value is evaluated for model assumptions. Inferred map orders are corroborated by genomic sequence, with the exception of linkage groups I, II, and V.     

High genetic diversity in the remnant island population of hihi and the genetic consequences of re-introduction

The maintenance of genetic diversity is thought to be fundamental for the conservation of threatened species. It is therefore important to understand how genetic diversity is affected by the re-introduction of threatened species. We use establishment history and genetic data from the remnant and re-introduced populations of a New Zealand endemic bird, the hihi Notiomystis cincta, to understand genetic diversity loss and quantify the genetic effects of re-introduction. Our data do not support any recent bottleneck events in the remnant population. Furthermore, all genetic diversity measures indicate the remnant hihi population has retained high levels of genetic diversity relative to other New Zealand avifauna with similar histories of decline. Genetic diversity (N(A) , alleles per locus, allelic richness, F(IS) and H(S) ) did not significantly decrease in new hihi populations founded through re-introduction when compared to their source populations, except in the Kapiti Island population (allelic richness and H(S) ) which had very slow post-re-introduction population growth. The N(e) /N(c) ratio in the remnant population was high, but decreased in first-level re-introductions, which together with significant genetic differentiation between populations (F(ST) & Fisher's exact tests) suggest that extant populations are diverging as a result of founder effects and drift. Importantly, simulations of future allele loss predict that the number of alleles lost will be higher in populations with a slow population growth, fewer founding individuals and with nonrandom mating. Interestingly, this species has very high levels of extra-pair paternity which may reduce reproductive variance by allowing social and floater males to reproduce a life history trait that together with a large remnant population size may help maintain higher levels of genetic diversity than expected.     

The relation between the genetic architecture of quantitative traits and long-term genetic response

The genetic architecture of a quantitative trait refers to the number of genetic variants, allele frequencies, and effect sizes of variants that affect a trait and their mode of gene action. This study was conducted to investigate the effect of four shapes of allelic frequency distributions (constant, uniform, L-shaped and U-shaped) and different number of trait-affecting loci (50, 100, 200, 500) on allelic frequency changes, long term genetic response, and maintaining genetic variance. To this end, a population of 440 individuals composed of 40 males and 400 females as well as a genome of 200 cM consisting of two chromosomes and with a mutation rate of 2.5?×?10^?5 per locus was simulated. Selection of superior animals was done using best linear unbiased prediction (BLUP) with assumption of infinitesimal model. Selection intensity was constant over 30 generations of selection. The highest genetic progress obtained when the allelic frequency had L-shaped distribution and number of trait-affecting loci was high (500). Although quantitative genetic theories predict the extinction of genetic variance due to artificial selection in long time, our results showed that under L- and U-shapped allelic frequency distributions, the additive genetic variance is persistent after 30 generations of selection. Further, presence or absence of selection limit can be an indication of low (<50) or high (>100) number of trait-affecting loci, respectively. It was concluded that the genetic architecture of complex traits is an important subject which should be considered in studies concerning long-term response to selection.