Assembling large, complex environmental metagenomes
Adina Chuang Howe, Janet Jansson, Stephanie A. Malfatti, Susannah G. Tringe, James M. Tiedje, C. Titus Brown
(Submitted on 12 Dec 2012)

The large volumes of sequencing data required to deeply sample complex environments pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two data reduction approaches, digital normalization and partitioning, to this challenge. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.

HIV drug resistance: problems and perspectives
Pleuni S Pennings
(Submitted on 25 Nov 2012)

Many HIV patients now have access to combination antiretroviral treatment (ART). At the end of 2011, more than eight million people were receiving antiretroviral therapy in low-income and middle-income countries. ART generally works well in keeping the virus suppressed and the patient healthy. However, treatment only works as long as the virus is not resistant against the drugs used. In the last decades HIV treatments have become better and better at slowing down the evolution of drug resistance, so that some patients are treated for many years without having any resistance problems. However, for some patients, especially in low-income countries, drug resistance is still a serious threat to their health. This essay will review what is known about transmitted and acquired drug resistance, multi-class drug resistance, resistance to newer drugs, resistance due to treatment for the prevention of mother-to-child transmission, the role of minority variants (low-frequency drug-resistance mutations), and resistance due to pre-exposure prophylaxis.

The evolution of genetic architectures underlying quantitative traits
Etienne Rajon, Joshua B. Plotkin
(Submitted on 31 Oct 2012)

In the classic view introduced by R.A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in QTL mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher’s blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions to qualitative trends in the genetics of human traits, and to systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis of traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.

This guest post is by Christopher Brown, Lara Mangravite, and Barbara Engelhardt on their paper: Integrative modeling of eQTLs and cis-regulatory elements suggest mechanisms underlying cell type specificity of eQTLs arXived here.

Why do we study eQTLs? Why don’t we count bristles?

The genetic dissection of complex trait models, independent of the particular phenotype, is useful for improving our understanding of the genetic architecture underlying the biochemical function that regulates complex traits in general. In the last ten years, gene expression levels themselves have emerged as useful phenotypes amenable to genetic dissection with several advantages, most notably that it is easy to accurately quantify tens of thousands of traits simultaneously (indeed even more when we address splicing and promoter usage). While the identification of SNPs that are associated with variation in gene expression (eQTLs) is certainly interesting at this basic level, an additional critical use for eQTL data has emerged. Because the majority of common human phenotypic variation appears to be driven by non-coding sequence variants, eQTL analyses are beginning to help with the mechanistic interpretation of GWAS results. In light of these interests and applications, we believe that eQTL analyses are hampered by (at least) three important limitations, which we have attempted to address in our recent preprint:

(1) Methodological (non) uniformity. Most eQTL studies have been performed by different groups, on different genotyping and gene expression platforms, with different association methods, and using different criteria for defining significance. This lack of uniformity complicates even simple cross study comparisons; for example, what fraction of genes has one or more independently associated eQTL when analyzed across tissues? We address this issue by testing for eQTL associations across a diverse set of cell types using a uniform pipeline with standardized analysis parameters to perform all analytical steps starting from raw data. As a fairly trivial example, our analyses across the eleven studies demonstrated that nearly all of the variation in the proportion of genes with significant eQTL associations identified within each study can be explained by just two factors: study size and replicate gene expression measurements. The proportion of genes with one or more independently associated eQTLs, then, is probably not 5-10% as has been hypothesized, but most or all of them, which we can get a better picture of when we design studies with sufficient power.

(2) Undercharacterized cell specificity. It is generally agreed upon that some eQTLs regulate gene expression in a cell type specific manner. When using eQTLs to interpret the genetic contribution to complex clinical traits, it is important to consider the cell type(s) most relevant to the trait of interest. However, if we don’t know what cell type is responsible for a phenotype or if we don’t have eQTL data for the cell type of interest, we are forced to extrapolate inferences about eQTLs derived from other cell types. By enabling the simultaneous comparison of within and between cell type eQTL replication for multiple cell type combinations and integrating these results with cis-regulatory element (CRE) mapping data from ENCODE, we have addressed several unresolved questions concerning the nature of cell type specific and ubiquitous eQTL SNPs. We find that eQTL-CRE overlap is frequently cell type specific and that this information can be used to predict cell specificity of eQTLs in the absence of additional gene expression data from the cell type of interest. While these results are certainly preliminary (and indeed we see many possible improvements), we hope this will improve the utility of eQTL-GWAS comparisons, particularly in situations where the GWAS cell type of interest lacks eQTL data.

(3) Resolution, causality, and mechanism. Lead tag SNPs are probably causal variants less than 30% of the time. While larger and more diverse genomic sample sets are essential to improve the resolution for identifying causal variants, this is not always possible due to time or budget constraints. However, the application of orthogonal genomic data also has the potential to considerably refine resolution with the added benefit of providing insight into the mechanism through which a causal variant acts. We approach this (as a few other groups have – notably Dan Gaffney et al.) by integrating CRE data into our analyses, because it appears that genetic variants that overlap certain types of CREs are much more likely to be functional than those that do not. We believe that this hypothesis, and the methods used to address it, need to be validated with directed functional assays, but we see no reason to doubt the principle of understanding heritable phenotypes using genotype functional analyses. Furthermore, the analysis of cell specific eQTL data in the context of cell specific CRE data, which is now possible, enables predictions about the regulatory mechanisms that are affected by a specific eQTL, which will allow us to place GWAS hits into pathways or provide other meaningful biological insights.

Why did we submit the paper to arXiv and Haldane’s Sieve?

We are big proponents of open access publication, open data, and transparent methods and analysis. At least part of what we’ve done here is to create a resource that we hope will be useful to the broader community. We are open to pre and post publication review of and commentary on our motivations and methods. Furthermore, we have submitted all of the eQTLs we identify to a database of eQTLs (eqtl.uchicago.edu), and we are currently securing funding to develop open access, online tools to help GWAS researchers follow up specific functional variants using our methods.

Christopher Brown, Lara Mangravite, Barbara Engelhardt

Species Identification and Unbiased Profiling of Complex Microbial Communities Using Shotgun Illumina Sequencing of 16S rRNA Amplicon Sequences

Swee Hoe Ong, Vinutha Uppoor Kukkillaya, Andreas Wilm, Christophe Lay, Eliza Xin Pei Ho, Louie Low, Martin Lloyd Hibberd, Niranjan Nagarajan
(Submitted on 12 Oct 2012)

The high throughput and cost-effectiveness afforded by short-read sequencing technologies, in principle, enable researchers to perform 16S rRNA profiling of complex microbial communities at unprecedented depth and resolution. Existing Illumina sequencing protocols are, however, limited by the fraction of the 16S rRNA gene that is interrogated and therefore limit the resolution and quality of the profiling. To address this, we present the design of a novel protocol for shotgun Illumina sequencing of the bacterial 16S rRNA gene, optimized to capture more than 90% of sequences in the Greengenes database and with nearly twice the resolution of existing protocols. Using several in silico and experimental datasets, we demonstrate that despite the presence of multiple variable and conserved regions, the resulting shotgun sequences can be used to accurately quantify the diversity of complex microbial communities. The reconstruction of a significant fraction of the 16S rRNA gene also enabled high precision (>90%) in species-level identification thereby opening up potential application of this approach for clinical microbial characterization.

Efficient Forward Simulation of Fisher-Wright Populations with Stochastic Population Size and Neutral Single Step Mutations in Haplotypes
Mikkel Meyer Andersen, Poul Svante Eriksen
(Submitted on 5 Oct 2012)

In both population genetics and forensic genetics it is important to know how haplotypes are distributed in a population. Simulation of population dynamics helps facilitating research on the distribution of haplotypes. In forensic genetics, the haplotypes can for example consist of lineage markers such as short tandem repeat loci on the Y chromosome (Y-STR). A dominating model for describing population dynamics is the simple, yet powerful, Fisher-Wright model. We describe an efficient algorithm for exact forward simulation of exact Fisher-Wright populations (and not approximative such as the coalescent model). The efficiency comes from convenient data structures by changing the traditional view from individuals to haplotypes. The algorithm is implemented in the open-source R package ‘fwsim’ and is able to simulate very large populations. We focus on a haploid model and assume stochastic population size with flexible growth specification, no selection, a neutral single step mutation process, and self-reproducing individuals. These assumptions make the algorithm ideal for studying lineage markers such as Y-STR.

A phylogeny of birds based on over 1,500 loci collected by target enrichment and high-throughput sequencing
John E. McCormack, Michael G. Harvey, Brant C. Faircloth, Nicholas G. Crawford, Travis C. Glenn, Robb T. Brumfield
(Submitted on 4 Oct 2012)

Evolutionary relationships among birds in Neoaves, a clade including the vast majority of avian diversity, have vexed systematists due to the ancient, rapid radiation of numerous lineages. We applied a new phylogenomic approach to resolve relationships in Neoaves using target enrichment (sequence capture) and high-throughput sequencing of ultraconserved elements (UCEs) in avian genomes. We collected sequence data from UCE loci for 32 members of Neoaves and one outgroup (chicken) and analyzed data sets that differed in amount of missing data. An alignment of 1,541 loci that allowed missing data was 87% complete and resulted in a highly resolved phylogeny with broad agreement between the Bayesian and maximum-likelihood (ML) trees. Although the 100% complete matrix of 416 UCE loci was broadly similar, the Bayesian and ML trees differed to a greater extent in this analysis, suggesting that increasing from 416 to 1,541 loci led to increased stability and resolution of the tree. Novel results of our study include surprisingly close relationships between phenotypically divergent bird families, such as tropicbirds (Phaethontidae) and the sunbittern (Eurypygidae) as well as a sister relationship between bustards (Otididae) and turacos (Musophagidae). This phylogeny bolsters support for monophyletic waterbird and landbird clades and also strongly supports controversial relationships from previous studies, including the sister relationship between passerines and parrots and the non-monophyly of raptorial birds in the hawk and falcon families. Although significant challenges remain to fully resolving some of the deep relationships in Neoaves, especially among lineages outside the waterbirds and landbirds, this study suggests that increased data will yield an increasingly resolved avian phylogeny.