Whole genome sequencing using high-throughput sequencing (HTS) technologies offers powerful opportunities to study genetic variation. Mapping the mutations responsible for different phenotypes is generally an involved and time-consuming process so researchers have developed user-friendly tools for mapping-by-sequencing, yet they are not applica- ble to organisms with non-sequenced genomes. We introduce SDM (SNP Distribution Method), a reference independent method for rapid discovery of mutagen-induced muta- tions in typical forward genetic screens. SDM aims to order a disordered collection of HTS reads or contigs such that the fragment carrying the causative mutation can be identified. SDM uses typical distributions of homozygous SNPs that are linked to a phenotype-altering SNP in a non-recombinant region as a model to order the fragments. To implement and test SDM, we created model genomes with an idealised SNP density based on Arabidop- sis thaliana chromosome 1 and analysed fragments with size distribution similar to reads or contigs assembled from HTS sequencing experiments. SDM groups the contigs by their normalised SNP density and arranges them to maximise the fit to the expected SNP distribution. We tested the procedure in existing datasets by examining SNP distributions in recent out-cross and back-cross experiments in Arabidopsis thaliana backgrounds. In all the examples we analysed, homozygous SNPs were normally distributed around the causal mutation. We used the real SNP densities obtained from these experiments to prove the efficiency and accuracy of SDM. The algorithm was able to successfully identify small sized (10-100 kb) genomic regions containing the causative mutation.

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

Barley was introduced to North America ~400 years ago but adaptation to modern production environments is more recent. Comparisons of allele frequencies among different growth habits and inflorescence types in North America indicate significant genetic differentiation has accumulated in a relatively short evolutionary time span. Allele frequency differentiation is greatest among barley with two-row versus six-row inflorescences, and then by spring versus winter growth habit. Large changes in allele frequency among breeding programs suggest a major contribution of genetic drift and linked selection on genetic variation. Despite this, comparisons of 3,613 modern North American cultivated breeding lines that differ for row type and growth habit permit the discovery of 183 SNP outliers putatively linked to targets of selection. For example, SNPs within the Cbf4, Ppd-H1, and Vrn-H1 loci which have previously been associated with agronomically-adaptive phenotypes, are identified as outliers. Analysis of extended haplotype-sharing identifies genomic regions shared within and among breeding programs, suggestive of a number of genomic regions subject to recent selection. Finally, we are able to identify recent bouts of gene flow between breeding programs that could point to the sharing of agronomically-adaptive variation. These results are supported by pedigrees and breeders understanding of germplasm sharing.

Evolutionary relationships are described by phylogenetic trees, but a central barrier in many fields is the difficulty of interpreting data containing conflicting phylogenetic signals. Obtaining credible trees that capture the relationships present in complex data is one of the fundamental challenges in evolution today. We present a way to map trees which extracts distinct alternative evolutionary relationships embedded in data and resolves phylogenetic uncertainty. Our method reveals a remarkably distinct phylogenetic signature in the VP30 gene of Ebolavirus, indicating possible recombination with significant implications for vaccine development. Moving to higher organisms, we use our approach to detect alternative histories of the evolution of anole lizards. Our approach has the capacity to resolve key areas of uncertainty in the study of evolution, and to broaden the credibility and appeal of phylogenetic methods.

With the increasing availability of functional genomic data, incorporating genomic annotations into QTL mapping has become a standard analytical procedure. However, the existing analysis methods often lack rigor and/or computational efficiency. We present a novel algorithm to perform integrative multi-SNP QTL mapping in a probabilistic hierarchical model framework that enables accurate and efficient joint enrichment analysis and the identification of multiple causal variants. The software implementation of the method is freely available at https://github.com/xqwen/dap

Increasing evidence shows that phenotypic variance is genetically controlled, and the variance itself is a quantitative trait. The precise mechanism of genetic control over the variance, however, remains to be determined. Here, using complex trait analysis of gene expression, we show that common genetic variation contributes to increasing gene expression variability via distinct modes of action–e.g., epistasis and decanalization. We focused on expression variability QTLs (evQTLs), i.e., genetic loci associated with gene express variance, in the human genome. We found that a quarter of evQTLs could be attributed to the presence of “third-party” eQTLs. These SNPs are associated with gene expression in a fraction, rather than the entire set, of samples. Many additional evQTLs do not interact with other SNPs and are thus unexplained by the epistasis model; these are attributable to the decanalizing effect of evQTL variants. Here we present the decanalization model, which predicts that evQTLs influence gene expression variability through modulating the sensitivity of transcriptional machinery to environmental perturbation. To validate the model we measured the discordant gene expression between monozygotic twins, and also estimated the amplitude of stochastic gene expression noise using repeated RT-qPCR assays on single samples. Both measures were found to be associated with genotypes of evQTLs explained by the decanalization model. Together, our results suggest that genetic variants work interactively or independently to influence gene expression variability. We anticipate our analysis to be a starting point for more sophisticated mechanistic analyses and opens a new, variability-centered research avenue for mapping complex traits.

Qualitative patterns of gene activation and repression are often conserved despite an abundance of quantitative variation in expression levels within and between species. A major challenge to interpreting patterns of expression divergence is knowing which changes in gene expression affect fitness. To characterize the fitness effects of gene expression divergence we placed orthologous promoters from eight yeast species upstream of malate synthase (MLS1) in Saccharomyces cerevisiae. As expected, we found these promoters varied in their expression level under activated and repressed conditions as well as in their dynamic response following loss of glucose repression. Despite these differences, only a single promoter driving near basal levels of expression caused a detectable loss of fitness. We conclude that the MLS1 promoter lies on a fitness plateau whereby even large changes in gene expression can be tolerated without a substantial loss of fitness.

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profiles of phylogenetic trees from their Laplacian graphs. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.

The realisation that heterozygosity can be heritable has recently generated some elegant research. However, none of this work has discussed the fact that when heterozygote advantage occurs, heterozygosity can be heritable, yet allele frequencies remain at equilibrium and do not evolve with time. From a quantitative genetic perspective this means the character is heritable, is under selection, yet no response to selection is observed. We explain why this is the case, and discuss potential implications for the study of evolution in the wild.