Bootstrap confidence levels for HIV-1 recombination

Testing genetic association with rare variants in admixed populations

Recent studies suggest that rare variants play an important role in the etiology of many traits. Although a number of methods have been developed for genetic association analysis of rare variants, they all assume a relatively homogeneous population under study. Such an assumption may not be valid for samples collected from admixed populations such as African Americans and Hispanic Americans as there is a great extent of local variation in ancestry in these populations. To ensure valid and more powerful rare variant association tests performed in admixed populations, we have developed a local ancestry-based weighted dosage test, which is able to take into account local ancestry of rare alleles, uncertainties in rare variant imputation when imputed data are included, and the direction of effect that rare variants exert on phenotypic outcome. We used simulated sequence data to show that our proposed test has controlled type I error rates, whereas naïve application of existing rare variants tests and tests that adjust for global ancestry lead to inflated type I error rates. We showed that our test has higher power than tests without proper adjustment of ancestry. We also applied the proposed method to a candidate gene study on low-density lipoprotein cholesterol. Our results suggest that it is important to appropriately control for potential population stratification induced by local ancestry difference in the analysis of rare variants in admixed populations.

StepBrothers: inferring partially shared ancestries among recombinant viral sequences

Phylogeneticists have developed several statistical methods to infer recombination among molecular sequences that are evolutionarily related. Of these methods, Markov change-point models currently provide the most coherent framework. Yet, the Markov assumption is faulty in that the inferred relatedness of homologous sequences across regions divided by recombinant events is not independent, particularly for nonrecombinant sequences as they share the same history. To correct this limitation, we introduce a novel random tips (RT) model. The model springs from the idea that a recombinant sequence inherits its characters from an unknown number of ancestral full-length sequences, of which one only observes the incomplete portions. The RT model decomposes recombinant sequences into their ancestral portions and then augments each portion onto the data set as unique partially observed sequences. This data augmentation generates a random number of sequences related to each other through a single inferable tree with the same random number of tips. While intuitively pleasing, this single tree corrects the independence assumptions plaguing previous methods while permitting the detection of recombination. The single tree also allows for inference of the relative times of recombination events and generalizes to incorporate multiple recombinant sequences. This generalization answers important questions with which previous models struggle. For example, we demonstrate that a group of human immunodeficiency type 1 recombinant viruses from Argentina, previously thought to have the same recombinant history, actually consist of 2 groups: one, a clonal expansion of a reference sequence and another that predates the formation of the reference sequence. In another example, we demonstrate that 2 hepatitis B virus recombinant strains share similar splicing locations, suggesting a common descent of the 2 viruses. We implement and run both examples in a software package called StepBrothers, freely available to interested parties.

Genotype turnover by reassortment of replication complex genes from avian influenza A virus

Reassortment among the RNA segments of Influenza A virus caused the two most recent human influenza pandemics; recently, reassortment has generated viral genotypes associated with outbreaks of avian H5N1 influenza in Asia and Europe. A statistical analysis has been developed for the systematic identification and characterization of reassortant viruses. The analysis was applied to the genes of the replication complex of 152 avian influenza A viruses isolated between 1966 and 2004 from predominantly terrestrial and domestic aquatic avian species. The results indicated that reassortment among these genes was pervasive throughout this period and throughout both the Eurasian and North American lineages of the virus. Evidence is presented that the circulating genotypes of the replication complex are being replaced continually by novel genotypes created by reassortment. No constraints for coordinated reassortment among genes of the replication complex were evident; rather, reassortment almost always proceeded one segment at a time. A maximum-likelihood estimate of the rate of reassortment was derived. For significantly diverged Asian avian influenza A viruses from the period 1991-2004, it was estimated that the median duration between creation of a new genotype and its next segment reassortment was 3 years. Reassortments that introduced previously unobserved influenza genetic material were detected. These findings point to substantial potential for rapid generation of novel avian influenza A viruses, emphasizing the importance of intensive surveillance of these host species in preparation for a possible pandemic.

Models for estimating bayes factors with applications to phylogeny and tests of monophyly

Bayes factors comparing two or more competing hypotheses are often estimated by constructing a Markov chain Monte Carlo (MCMC) sampler to explore the joint space of the hypotheses. To obtain efficient Bayes factor estimates, Carlin and Chib (1995, Journal of the Royal Statistical Society, Series B57, 473-484) suggest adjusting the prior odds of the competing hypotheses so that the posterior odds are approximately one, then estimating the Bayes factor by simple division. A byproduct is that one often produces several independent MCMC chains, only one of which is actually used for estimation. We extend this approach to incorporate output from multiple chains by proposing three statistical models. The first assumes independent sampler draws and models the hypothesis indicator function using logistic regression for various choices of the prior odds. The two more complex models relax the independence assumption by allowing for higher-lag dependence within the MCMC output. These models allow us to estimate the uncertainty in our Bayes factor calculation and to fully use several different MCMC chains even when the prior odds of the hypotheses vary from chain to chain. We apply these methods to calculate Bayes factors for tests of monophyly in two phylogenetic examples. The first example explores the relationship of an unknown pathogen to a set of known pathogens. Identification of the unknown's monophyletic relationship may affect antibiotic choice in a clinical setting. The second example focuses on HIV recombination detection. For potential clinical application, these types of analyses must be completed as efficiently as possible.

Dual multiple change-point model leads to more accurate recombination detection

MOTIVATION

We introduce a dual multiple change-point (MCP) model for recombination detection among aligned nucleotide sequences. The dual MCP model is an extension of the model introduced previously by Suchard and co-workers. In the original single MCP model, one change-point process is used to model spatial phylogenetic variation. Here, we show that using two change-point processes, one for spatial variation of tree topologies and the other for spatial variation of substitution process parameters, increases recombination detection accuracy. Statistical analysis is done in a Bayesian framework using reversible jump Markov chain Monte Carlo sampling to approximate the joint posterior distribution of all model parameters.

RESULTS

We use primate mitochondrial DNA data with simulated recombination break-points at specific locations to compare the two models. We also analyze two real HIV sequences to identify recombination break-points using the dual MCP model.