Genetic studies of African-Americans identify functional variants, elucidate historical and genealogical mysteries, and reveal basic biology. However, African-Americans have been under-represented in genetic studies, and little is known about nation-wide patterns of genomic diversity in the population. Here, we present a comprehensive assessment of African-American genomic diversity using genotype data from nationally and regionally representative cohorts. We find higher African ancestry in southern United States compared to the North and West. We show that relatedness patterns track north- and west-bound routes followed during the Great Migration, suggesting that admixture occurred predominantly in the South prior to the Civil War and that ancestry-biased migration is responsible for regional differences in ancestry. Rare genetic traits among African-Americans can therefore be shared over long geographic distances along the Great Migration routes, yet their distribution over short distances remains highly structured. This study clarifies the role of re- cent demography in shaping African-American genomic diversity.

Many questions about human genetic history can be addressed by examining the patterns of shared genetic variation between sets of populations. A useful methodological framework for this purpose are F-statistics, that measure shared genetic drift between sets of two, three and four populations, and can be used to test simple and complex hypotheses about admixture between populations. Here, we put these statistics in context of phylogenetic and population genetic theory. We show how measures of genetic drift can be interpreted as branch lengths, paths through an admixture graph or in terms of the internal branches in coalescent trees. We show that the admixture tests can be interpreted as testing general properties of phylogenies, allowing us to generalize applications for arbitrary phylogenetic trees. Furthermore, we derive novel expressions for the F-statistics, which enables us to explore the behavior of F-statistic under population structure models. In particular, we show that population substructure may complicate inference.

Phenotypic plasticity is an evolutionary driving force in diverse biological processes, including the adaptive immune system, the development of neoplasms, and the bacterial acquisition of drug resistance. It is essential, therefore, to understand the evolutionary advantage of an allele that confers cells the ability to express a range of phenotypes. Of particular importance is to understand how this advantage of phenotypic plasticity depends on the degree of heritability of non-genetically encoded phenotypes between generations, which can induce irreversible evolutionary changes in the population. Here, we study the fate of a new mutation that allows the expression of multiple phenotypic states, introduced into a finite population otherwise composed of individuals who can express only a single phenotype. We analyze the fixation probability of such an allele as a function of the strength of inter-generational phenotypic heritability, called memory, the variance of expressible phenotypes, the rate of environmental changes, and the population size. We find that the fate of a phenotypically plastic allele depends fundamentally on the environmental regime. In a constant environment, the fixation probability of a plastic allele always increases with the degree of phenotypic memory. In periodically fluctuating environments, by contrast, there is an optimum phenotypic memory that maximizes the probability of the plastic allele’s fixation. This same optimum value of phenotypic memory also maximizes geometric mean fitness, in steady state. We interpret these results in the context of previous studies in an infinite-population framework. We also discuss the implications of our results for the design of therapies that can overcome resistance, in a variety of diseases.

Expression quantitative trait locus (eQTL) analysis relates genetic variation to gene expression, and it has been shown that power to detect eQTLs is substantially increased by adjustment for measures of expression variability derived from singular value decomposition-based procedures (referred to as expression factors, or EFs). A potential downside to this approach is that power will be reduced for eQTL that are correlated with one or more EFs, but these approaches are commonly used in human eQTL studies on the assumption that this risk is low for cis (i.e. local) eQTL associations. Using two independent blood eQTL datasets, we show that this assumption is incorrect and that, in fact, 10-25% of eQTL that are significant without adjustment for EFs are no longer detected after EF adjustment. In addition, the majority of these lost eQTLs replicate in independent data, indicating that they are not spurious associations. Thus, in the ideal case, EFs would be re-estimated for each eQTL association test, as has been suggested by others; however, this is computationally infeasible for large datasets with densely imputed genotype data. We propose an alternative, buffet-style approach in which a series of EF and non-EF eQTL analyses are performed and significant eQTL discoveries are collected across these analyses. We demonstrate that standard methods to control the false discovery rate perform similarly between the single EF and buffet-style approaches, and we provide biological support for eQTL discovered by this approach in terms of immune cell-type specific enhancer enrichment in Roadmap Epigenomics and ENCODE cell lines.

Massively parallel sequencing has revolutionized many areas of biology but sequencing large amounts of DNA in many individuals is cost-prohibitive and unnecessary for many studies. Genomic complexity reduction techniques such as sequence capture and restriction enzyme-based methods enable the analysis of many more individuals per unit cost. Despite their utility, current complexity reduction methods have limitations, especially when large numbers of individuals are analyzed. Here we develop a much improved restriction site associated DNA (RAD) sequencing protocol and a new combinatorial method called Rapture (RAD capture). The new RAD protocol improves versatility by separating RAD tag isolation and sequencing library preparation into two distinct steps. This protocol also recovers more unique (non-clonal) RAD fragments and produces better data for both standard RAD and Rapture analysis. Rapture then uses an in-solution capture of chosen RAD tags to target sequencing reads to desired loci. Rapture combines the benefits of both RAD and sequence capture, i.e. very inexpensive and rapid library preparation for many individuals as well as high specificity in the number and location of genomic loci analyzed. Our results demonstrate that Rapture is a rapid and flexible technology capable of analyzing a very large number of individuals with minimal sequencing and library preparation cost. The methods presented here should improve the efficiency of genetic analysis for many aspects of agricultural, environmental, and medical science.

Leishmania, a genus of parasites transmitted to human hosts and mammalian/reptilian reservoirs by an insect vector, is the causative agent of the human disease complex leishmaniasis. The evolutionary relationships within the genus Leishmania and its origins are the source of ongoing debate, reflected in conflicting phylogenetic and biogeographic reconstructions. This study employs a recently described bioinformatics method, SISRS, to identify over 200,000 informative sites across the genome from newly sequenced and publicly available Leishmania data. This dataset is used to reconstruct the evolutionary relationships of this genus. Additionally, we constructed a large multi-gene dataset; we used this dataset to reconstruct the phylogeny and estimate divergence dates for species. We conclude that the genus Leishmania evolved at least 90-100 million years ago. Our results support the hypothesis that Leishmania clades separated prior to, and during, the breakup of Gondwana. Additionally, we confirm that reptile-infecting Leishmania are derived from mammalian forms, and that the species that infect porcupines and sloths form a clade long separated from other species. We also firmly place the guinea-pig infecting species, L. enrietti, the globally dispersed L. siamensis, and the newly identified Australia species from kangaroos as sibling species whose distribution arises from the ancient connection between Australia, Antarctica, and South America.

Recently, a new Chlamydia-related organism, Protochlamydia naegleriophila KNic, was discovered within a Naegleria amoeba. To decipher the mechanisms at play in the modeling of genomes from the Protochlamydia genus, we sequenced de novo the full genome of Pr. naegleriophila combining the advantages of two second-generation sequencing technologies. The assembled complete genome comprises a 2,885,111 bp chromosome and a 145,285 bp megaplasmid. For the first time within the Chlamydiales order, a CRISPR system, the immune system of bacteria, was discovered on the chromosome. It is composed of a small CRISPR locus comprising eight repeats and the associated cas and cse genes of the subtype I-E. A CRISPR locus was also found within Chlamydia sp. Diamant, another Pr. naegleriophila strain whose genome was recently released, suggesting that the CRISPR system was acquired by a common ancestor of these two members of Pr. naegleriophila, after the divergence from Pr. amoebophila. The plasmid encodes an F-type conjugative system similar to that found in the Pam100G genomic island of Pr. amoebophila suggesting an acquisition of this conjugative system before the divergence of both Protochlamydia species and the integration of a putative Pr. amoebophila plasmid into its main chromosome giving rise to the Pam100G genomic island. Overall, this new Pr. naegleriophila genome sequence enables to investigate further the dynamic processes shaping the genomes of Chlamydia-related bacteria.

We evaluated the fraction of variation in HIV-1 set point viral load attributable to viral or human genetic factors by using joint host/pathogen genetic data from 541 HIV infected individuals. We show that viral genetic diversity explains 29% of the variation in viral load while host factors explain 8.4%. Using a joint model including both host and viral effects, we estimate a total of 30% heritability, indicating that most of the host effects are reflected in viral sequence variation.

Over the last decade tremendous progress has been made towards a comparative understanding of gene regulatory evolution. However, we know little about how gene regulation evolves in birds, and how divergent genomes interact in their hybrids. Because of unique features of birds – female heterogamety, a highly conserved karyotype, and the slow evolution of reproductive incompatibilities – an understanding of regulatory evolution in birds is critical to a comprehensive understanding of regulatory evolution and its implications for speciation. Using a novel complement of analyses of replicated RNA-seq libraries, we demonstrate abundant divergence in gene expression between subspecies of zebra finches Taeniopygia guttata. By comparing parental populations and their F1 hybrids, we also show that gene misexpression is relatively rare, a pattern that may partially explain the slow buildup of postzygotic reproductive isolation observed in birds relative to other taxa. Although we expected that the action of genetic drift on the island-dwelling zebra finch subspecies would be manifested in a high rate of trans regulatory divergence, we found that most divergence was in cis regulation, following a pattern commonly observed in other taxa. Thus our study highlights both unique and shared features of avian regulatory evolution.