Linkage disequilibrium between rare mutations

The statistical associations between mutations, collectively known as linkage disequilibrium (LD), encode important information about the evolutionary forces acting within a population. Yet in contrast to single-site analogues like the site frequency spectrum, our theoretical understanding of linkage disequilibrium remains limited. In particular, little is currently known about how mutations with different ages and fitness costs contribute to expected patterns of LD, even in simple settings where recombination and genetic drift are the major evolutionary forces. Here, I introduce a forward-time framework for predicting linkage disequilibrium between pairs of neutral and deleterious mutations as a function of their present-day frequencies. I show that the dynamics of linkage disequilibrium become much simpler in the limit that mutations are rare, where they admit a simple heuristic picture based on the trajectories of the underlying lineages. I use this approach to derive analytical expressions for a family of frequency-weighted LD statistics as a function of the recombination rate, the frequency scale, and the additive and epistatic fitness costs of the mutations. I find that the frequency scale can have a dramatic impact on the shapes of the resulting LD curves, reflecting the broad range of time scales over which these correlations arise. I also show that the differences between neutral and deleterious LD are not purely driven by differences in their mutation frequencies, and can instead display qualitative features that are reminiscent of epistasis. I conclude by discussing the implications of these results for recent LD measurements in bacteria. This forward-time approach may provide a useful framework for predicting linkage disequilibrium across a range of evolutionary scenarios.

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We show an application of our method to the UK10K phased haplotype dataset of individuals.

Mechanisms of sex determination and X-chromosome dosage compensation

Abnormalities in chromosome number have the potential to disrupt the balance of gene expression and thereby decrease organismal fitness and viability. Such abnormalities occur in most solid tumors and also cause severe developmental defects and spontaneous abortions. In contrast to the imbalances in chromosome dose that cause pathologies, the difference in X-chromosome dose used to determine sexual fate across diverse species is well tolerated. Dosage compensation mechanisms have evolved in such species to balance X-chromosome gene expression between the sexes, allowing them to tolerate the difference in X-chromosome dose. This review analyzes the chromosome counting mechanism that tallies X-chromosome number to determine sex (XO male and XX hermaphrodite) in the nematode Caenorhabditis elegans and the associated dosage compensation mechanism that balances X-chromosome gene expression between the sexes. Dissecting the molecular mechanisms underlying X-chromosome counting has revealed how small quantitative differences in intracellular signals can be translated into dramatically different fates. Dissecting the process of X-chromosome dosage compensation has revealed the interplay between chromatin modification and chromosome structure in regulating gene expression over vast chromosomal territories.

Male and female recombination landscapes of diploid Arabidopsis arenosa

The number and placement of meiotic crossover events during meiosis has important implications for the fidelity of chromosome segregation as well as patterns of inheritance. Despite the functional importance of recombination, recombination landscapes vary widely among and within species, and this can have a strong impact on evolutionary processes. A good knowledge of recombination landscapes is important for model systems in evolutionary and ecological genetics, since it can improve interpretation of genomic patterns of differentiation and genome evolution, and provides an important starting point for understanding the causes and consequences of recombination rate variation. Arabidopsis arenosa is a powerful evolutionary genetic model for studying the molecular basis of adaptation and recombination rate evolution. Here we generate genetic maps for two diploid A. arenosa individuals from distinct genetic lineages where we have prior knowledge that meiotic genes show evidence of selection. We complement the genetic maps with cytological approaches to map and quantify recombination rates, and test the idea that these populations might have distinct patterns of recombination. We explore how recombination differs at the level of populations, individuals, sexes and genomic regions. We show that the positioning of crossovers along a chromosome correlates with their number, presumably a consequence of crossover interference, and discuss how this effect can cause differences in recombination landscape among sexes or species. We identify several instances of female segregation distortion. We found that averaged genome-wide recombination rate is lower and sex differences subtler in A. arenosa than in A. thaliana.

Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Despite the value of Recombinant Inbred Lines (RILs) for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to RILs for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here we describe such an extreme quantitative trait locus, or X-QTL, mapping strategy that builds on an existing multiparental population, the DSPR (Drosophila Synthetic Population Resource), and involves phenotyping and genotyping a population derived by mixing hundreds of DSPR RILs. Simulations demonstrate that challenging, yet experimentally tractable X-QTL designs ( > =4 replicates, > =5000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional RIL-based QTL mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated X-QTL experiment that identifies 7 QTL for caffeine resistance. Two mapped X-QTL factors replicate loci previously identified in RILs, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping X-QTL design has considerable advantages.

Evolution of polygenic traits under global vs local adaptation

Observations about the number, frequency, effect size, and genomic distribution of alleles associated with complex traits must be interpreted in light of evolutionary process. These characteristics, which constitute a trait’s genetic architecture, can dramatically affect evolutionary outcomes in applications from agriculture to medicine, and can provide a window into how evolution works. Here, I review theoretical predictions about the evolution of genetic architecture under spatially homogeneous, global adaptation as compared with spatially heterogeneous, local adaptation. Due to the tension between divergent selection and migration, local adaptation can favor “concentrated” genetic architectures that are enriched for alleles of larger effect, clustered in a smaller number of genomic regions, relative to expectations under global adaptation. However, the evolution of such architectures may be limited by many factors, including the genotypic redundancy of the trait, mutation rate, and temporal variability of environment. I review the circumstances in which predictions differ for global vs local adaptation and discuss where progress can be made in testing hypotheses using data from natural populations and lab experiments. As the field of comparative population genomics expands in scope, differences in architecture among traits and species will provide insights into how evolution works, and such differences must be interpreted in light of which kind of selection has been operating.

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.

FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.