Comparing analysis methods for mutation-accumulation data: a simulation study

A brief history and popularity of methods and tools used to estimate micro-evolutionary forces

Population genetics is a field of research that predates the current generations of sequencing technology. Those approaches, that were established before massively parallel sequencing methods, have been adapted to these new marker systems (in some cases involving the development of new methods) that allow genome-wide estimates of the four major micro-evolutionary forces-mutation, gene flow, genetic drift, and selection. Nevertheless, classic population genetic markers are still commonly used and a plethora of analysis methods and programs is available for these and high-throughput sequencing (HTS) data. These methods employ various and diverse theoretical and statistical frameworks, to varying degrees of success, to estimate similar evolutionary parameters making it difficult to get a concise overview across the available approaches. Presently, reviews on this topic generally focus on a particular class of methods to estimate one or two evolutionary parameters. Here, we provide a brief history of methods and a comprehensive list of available programs for estimating micro-evolutionary forces. We furthermore analyzed their usage within the research community based on popularity (citation bias) and discuss the implications of this bias for the software community. We found that a few programs received the majority of citations, with program success being independent of both the parameters estimated and the computing platform. The only deviation from a model of exponential growth in the number of citations was found for the presence of a graphical user interface (GUI). Interestingly, no relationship was found for the impact factor of the journals, when the tools were published, suggesting accessibility might be more important than visibility.

Genomic mutation rates: what high-throughput methods can tell us

High-throughput DNA analyses are increasingly being used to detect rare mutations in moderately sized genomes. These methods have yielded genome mutation rates that are markedly higher than those obtained using pre-genomic strategies. Recent work in a variety of organisms has shown that mutation rate is strongly affected by sequence context and genome position. These observations suggest that high-throughput DNA analyses will ultimately allow researchers to identify trans-acting factors and cis sequences that underlie mutation rate variation. Such work should provide insights on how mutation rate variability can impact genome organization and disease progression.

Analysis and implications of mutational variation

Variation from new mutations is important for several questions in quantitative genetics. Key parameters are the genomic mutation rate and the distribution of effects of mutations (DEM), which determine the amount of new quantitative variation that arises per generation from mutation (V(M)). Here, we review methods and empirical results concerning mutation accumulation (MA) experiments that have shed light on properties of mutations affecting quantitative traits. Surprisingly, most data on fitness traits from laboratory assays of MA lines indicate that the DEM is platykurtic in form (i.e., substantially less leptokurtic than an exponential distribution), and imply that most variation is produced by mutations of moderate to large effect. This finding contrasts with results from MA or mutagenesis experiments in which mutational changes to the DNA can be assayed directly, which imply that the vast majority of mutations have very small phenotypic effects, and that the distribution has a leptokurtic form. We compare these findings with recent approaches that attempt to infer the DEM for fitness based on comparing the frequency spectra of segregating nucleotide polymorphisms at putatively neutral and selected sites in population samples. When applied to data for humans and Drosophila, these analyses also indicate that the DEM is strongly leptokurtic. However, by combining the resultant estimates of parameters of the DEM with estimates of the mutation rate per nucleotide, the predicted V(M) for fitness is only a tiny fraction of V(M) observed in MA experiments. This discrepancy can be explained if we postulate that a few deleterious mutations of large effect contribute most of the mutational variation observed in MA experiments and that such mutations segregate at very low frequencies in natural populations, and effectively are never seen in population samples.

Partial mixture model for tight clustering of gene expression time-course

Background

Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.

Results

In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms.

Conclusion

For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the combination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion.

Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae

Although mutation rates are a key determinant of the rate of evolution they are difficult to measure precisely and global mutations rates (mutations per genome per generation) are often extrapolated from the per-base-pair mutation rate assuming that mutation rate is uniform across the genome. Using budding yeast, we describe an improved method for the accurate calculation of mutation rates based on the fluctuation assay. Our analysis suggests that the per-base-pair mutation rates at two genes differ significantly (3.80x10(-10) at URA3 and 6.44x10(-10) at CAN1) and we propose a definition for the effective target size of genes (the probability that a mutation inactivates the gene) that acknowledges that the mutation rate is nonuniform across the genome.

Mutation rate variation in multicellular eukaryotes: causes and consequences

A basic knowledge about mutation rates is central to our understanding of a myriad of evolutionary phenomena, including the maintenance of sex and rates of molecular evolution. Although there is substantial evidence that mutation rates vary among taxa, relatively little is known about the factors that underlie this variation at an empirical level, particularly in multicellular eukaryotes. Here we integrate several disparate lines of theoretical and empirical inquiry into a unified framework to guide future studies that are aimed at understanding why and how mutation rates evolve in multicellular species.

Increase of the spontaneous mutation rate in a long-term experiment with Drosophila melanogaster

In a previous experiment, the effect of 255 generations of mutation accumulation (MA) on the second chromosome viability of Drosophila melanogaster was studied using 200 full-sib MA1 lines and a large C1 control, both derived from a genetically homogeneous base population. At generation 265, one of those MA1 lines was expanded to start 150 new full-sib MA2 lines and a new C2 large control. After 46 generations, the rate of decline in mean viability in MA2 was approximately 2.5 times that estimated in MA1, while the average degree of dominance of mutations was small and nonsignificant by generation 40 and moderate by generation 80. In parallel, the inbreeding depression rate for viability and the amount of additive variance for two bristle traits in C2 were 2-3 times larger than those in C1. The results are consistent with a mutation rate in the line from which MA2 and C2 were derived about 2.5 times larger than that in MA1. The mean viability of C2 remained roughly similar to that of C1, but the rate of MA2 line extinction increased progressively, leading to mutational collapse, which can be ascribed to accelerated mutation and/or synergy after important deleterious accumulation.

Estimates of the genomic mutation rate for detrimental alleles in Drosophila melanogaster

The net rate of mutation to deleterious but nonlethal alleles and the sizes of effects of these mutations are of great significance for many evolutionary questions. Here we describe three replicate experiments in which mutations have been accumulated on chromosome 3 of Drosophila melanogaster by means of single-male backcrosses of heterozygotes for a wild-type third chromosome. Egg-to-adult viability was assayed for nonlethal homozygous chromosomes. The rates of decline in mean and increase in variance (DM and DV, respectively) were estimated. Scaled up to the diploid whole genome, the mean DM for homozygous detrimental mutations over the three experiments was between 0.8 and 1.8%. The corresponding DV estimate was approximately 0.11%. Overall, the results suggest a lower bound estimate of at least 12% for the diploid per genome mutation rate for detrimentals. The upper bound estimates for the mean selection coefficient were between 2 and 10%, depending on the method used. Mutations with selection coefficients of at least a few percent must be the major contributors to the effects detected here and are likely to be caused mostly by transposable element insertions or indels.

Behavioral degradation under mutation accumulation in Caenorhabditis elegans

Spontaneous mutations play a fundamental role in the maintenance of genetic variation in natural populations, the nature of inbreeding depression, the evolution of sexual reproduction, and the conservation of endangered species. Using long-term mutation-accumulation lines of the nematode Caenorhabditis elegans, we estimate the rate and magnitude of mutational effects for a suite of behaviors characterizing individual chemosensory responses to a repellant stimulus. In accordance with evidence that the vast majority of mutations are deleterious, we find that behavioral responses degrade over time as a result of spontaneous mutation accumulation. The rate of mutation for behavioral traits is roughly of the same order or slightly smaller than those previously estimated for reproductive traits and the average size of the mutational effects is also comparable. These results have important implications for the maintenance of genetic variation for behavior in natural populations as well as for expectations for behavioral change within endangered species and captive populations.