Pointing the Way to Additional Topics

This concluding chapter highlights many of the concepts that are important to understanding modern-day population genetics research and explains that while they may not have been covered in this book, they are built on the foundations laid out in the preceding chapters. A series of small sections are provided which briefly introduce important concepts for continued learning. These focus especially on the coalescent theory but also touch on tests of neutrality, linkage disequilibrium, deleterious alleles, fixation probability, selfish genetic elements, future directions, and R packages.

Natural selection and demography as causes of molecular non-randomness

Chapter 7, “Natural selection and demography as causes of molecular non-randomness,” outlines the predictable molecular evolutionary patterns that arise when the Neutral Theory has its assumptions violated. It summarizes predictions about genetic variation, the shape of genealogies, and the accumulation of divergence between lineages when natural selection and non-standard demographic scenarios occur in populations. This chapter provides an overview of the general, qualitative impacts on molecular population genetic data by positive selection, purifying selection, and balancing selection, as well as by demographic population growth, contraction, and subdivision. It covers the concepts of selective sweeps, genetic hitchhiking, and background selection, placed in a heuristic context of skews in polymorphism, genealogies, the site frequency spectrum, and distinct metrics of divergence. This chapter also summarizes the consequences of genetic linkage to sex chromosomes and plastid genomes. This overview builds up intuition about selection, demography, and genome organization as important molecular population genetic factors that motivate further analysis with quantitative tests of neutrality.

Molecular deviants

Chapter 8, “Molecular deviants: sequence signatures of selection and demography,” dives into the logic and mechanics of some of the most common tests of neutrality to show how and why data can reveal differences from the predictions of the standard neutral model. It introduces approaches based on skewed patterns of polymorphism alone, including Tajima’s D test, and on differentiation or divergence alone, like the Lewontin-Krakauer, Population Branch Statistic (PBS), and K A / K S relative-rates tests. Chapter 8 also covers tests of neutrality that integrate information from both within and between species, including the HKA-test and McDonald-Kreitman (MK) test. The logic for other tests of neutrality also is introduced, including ABBA-BABA, Composite Likelihood Ratio (CLR), Extended Haplotype Homozygosity (EHH), and other approaches. Practical implications of ancestral polymorphism and slightly deleterious polymorphisms are discussed, for example, in calculating and interpreting the neutrality index and fraction of positively selected sites (α‎). The goal of this chapter is to explain the logic of methods applied to molecular population genetic data to read the story of evolutionary history from the genome.

Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system's dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.

Measuring the degree of starshape in genealogies – summary statistics and demographic inference

SummaryThe degree of starshape of a genealogy is readily detectable using summary statistics and can be taken as a surrogate for the effect of past demography and other non-neutral forces. Summary statistics such as Tajima's D and related measures are commonly used for this. However, it is well known that because of their neglect of the genealogy underlying a sample such neutrality tests are far from ideal. Here, we investigate the properties of two types of summary statistics that are derived by considering the genealogy: (i) genealogical ratios based on the number of mutations on the rootward branches, which can be inferred from sequence data using a simple algorithm and (ii) summary statistics that use properties of a perfectly star-shaped genealogy. The power of these measures to detect a history of exponential growth is compared with that of standard summary statistics and a likelihood method for the single and multi-locus case. Statistics that depend on pairwise measures such as Tajima's D have comparatively low power, being sensitive to the random topology of the underlying genealogy. When analysing multi-locus data, we find that the genealogical measures are most powerful. Provided reliable outgroup information is available they may constitute a useful alternative to full likelihood estimation and standard tests of neutrality.

Effective population size and tests of neutrality at cytoplasmic genes in Arabidopsis

SummaryCytoplasmic genomes typically lack recombination, implying that genetic hitch-hiking could be a predominant force structuring nucleotide polymorphism in the chloroplast and mitochondria. We test this hypothesis by analysing nucleotide polymorphism data at 28 loci across the chloroplast and mitochondria of the outcrossing plant Arabidopsis lyrata, and compare patterns with multiple nuclear loci, and the highly selfing Arabidopsis thaliana. The maximum likelihood estimate of the ratio of effective population size at cytoplasmic relative to nuclear genes in A. lyrata does not depart from the neutral expectation of 0·5. Similarly, the ratio of effective size in A. thaliana is close to unity, the neutral expectation for a highly selfing species. The results are thus consistent with neutral organelle polymorphism in these species or with comparable effects of hitch-hiking in both cytoplasmic and nuclear genes, in contrast to the results of recent studies on gynodioecious taxa. The four-gamete test and composite likelihood estimation provide evidence for very low levels of recombination in the organelles of A. lyrata, although permutation tests do not suggest that adjacent polymorphic sites are more closely linked than more distant sites across the two genomes, suggesting that mutation hotspots or very low rates of gene conversion could explain the data.