Monthly Archives: December 2014

Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction

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Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction

Chia-Yen Chen, Jiali Han, David J. Hunter, Peter Kraft, Alkes L. Price
doi: http://dx.doi.org/10.1101/012005

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color, tanning ability and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRS) and Best Linear Unbiased Prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R2 for hair color increased by 66% (0.0456 to 0.0755; p<10-16), the R2 for tanning ability increased by 123% (0.0154 to 0.0344; p<10-16) and the liability-scale R2 for BCC increased by 68% (0.0138 to 0.0232; p<10-16) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being over-weighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be under-weighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.

Detecting the anomaly zone in species trees and evidence for a misleading signal in higher-level skink phylogeny (Squamata: Scincidae).

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Detecting the anomaly zone in species trees and evidence for a misleading signal in higher-level skink phylogeny (Squamata: Scincidae).

Charles W Linkem, Vladimir N. Minin, Adam D Leache
doi: http://dx.doi.org/10.1101/012096

The anomaly zone presents a major challenge to the accurate resolution of many parts of the Tree of Life. The anomaly zone is defined by the presence of a gene tree topology that is more probable than the true species tree. This discrepancy can result from consecutive rapid speciation events in the species tree. Similar to the problem of long-branch attraction, including more data (loci) will only reinforce the support for the incorrect species tree. Empirical phylogenetic studies often implement coalescent based species tree methods to avoid the anomaly zone, but to this point these studies have not had a method for providing any direct evidence that the species tree is actually in the anomaly zone. In this study, we use 16 species of lizards in the family Scincidae to investigate whether nodes that are difficult to resolve are located within the anomaly zone. We analyze new phylogenomic data (429 loci), using both concatenation and coalescent based species tree estimation, to locate conflicting topological signal. We then use the unifying principle of the anomaly zone, together with estimates of ancestral population sizes and species persistence times, to determine whether the observed phylogenetic conflict is a result of the anomaly zone. We identify at least three regions of the Scindidae phylogeny that provide demographic signatures consistent with the anomaly zone, and this new information helps reconcile the phylogenetic conflict in previously published studies on these lizards. The anomaly zone presents a real problem in phylogenetics, and our new framework for identifying anomalous relationships will help empiricists leverage their resources appropriately for overcoming this challenge.

The infant airway microbiome in health and disease impacts later asthma development

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The infant airway microbiome in health and disease impacts later asthma development

Shu Mei Teo, Danny Mok, Kym Pham, Merci Kusel, Michael Serralha, Niamh Troy, Barbara J Holt, Belinda J Hales, Michael L Walker, Elysia Hollams, Yury H Bochkov, Kristine Grindle, Sebastian L Johnston, James E Gern, Peter D Sly, Patrick G Holt, Kathryn E Holt, Michael Inouye
doi: http://dx.doi.org/10.1101/012070

The nasopharynx (NP) is a reservoir for microbes associated with acute respiratory illnesses (ARI). The development of asthma is initiated during infancy, driven by airway inflammation associated with infections. Here, we report viral and bacterial community profiling of NP aspirates across a birth cohort, capturing all lower respiratory illnesses during their first year. Most infants were initially colonized with Staphylococcus or Corynebacterium before stable colonization with Alloiococcus or Moraxella, with transient incursions of Streptococcus, Moraxella or Haemophilus marking virus-associated ARIs. Our data identify the NP microbiome as a determinant for infection spread to the lower airways, severity of accompanying inflammatory symptoms, and risk for future asthma development. Early asymptomatic colonization with Streptococcus was a strong asthma predictor, and antibiotic usage disrupted asymptomatic colonization patterns.

The rate and molecular spectrum of spontaneous mutations in the GC-rich multi-chromosome genome of Burkholderia cenocepacia

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The rate and molecular spectrum of spontaneous mutations in the GC-rich multi-chromosome genome of Burkholderia cenocepacia
Marcus M Dillon, Way Sung, Michael Lynch, Vaughn S Cooper
doi: http://dx.doi.org/10.1101/011841
Spontaneous mutations are ultimately essential for evolutionary change and are also the root cause of nearly all disease. However, until recently, both biological and technical barriers have prevented detailed analyses of mutation profiles, constraining our understanding of the mutation process to a few model organisms and leaving major gaps in our understanding of the role of genome content and structure on mutation. Here, we present a genome-wide view of the molecular mutation spectrum in Burkholderia cenocepacia, a clinically relevant pathogen with high %GC content and multiple chromosomes. We find that B. cenocepacia has low genome-wide mutation rates with insertion-deletion mutations biased towards deletions, consistent with the idea that deletion pressure reduces prokaryotic genome sizes. Unlike previously assayed organisms, B. cenocepacia exhibits a GC-mutation bias, which suggests that at least some genomes with high GC content may be driven to this point by unusual base-substitution mutation pressure. Notably, we also observed variation in both the rates and spectra of mutations among chromosomes, and a significant elevation of G:C>T:A transversions in late-replicating regions. Thus, although some patterns of mutation appear to be highly conserved across cellular life, others vary between species and even between chromosomes of the same species, potentially influencing the evolution of nucleotide composition and genome architecture.

Most viewed on Haldane’s Sieve: November 2014

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The most viewed posts on Haldane’s Sieve this month were:

A new FST-based method to uncover local adaptation using environmental variables

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A new $F_{\text{ST}}$-based method to uncover local adaptation using environmental variables
 Pierre de Villemereuil, Oscar E. Gaggiotti
Comments: 18 pages, 5 figures, Supplementary Information at the end of the document
Subjects: Populations and Evolution (q-bio.PE)

Genome-scan methods are used for screening genome-wide patterns of DNA polymorphism to detect signatures of positive selection. There are two main types of methods: (i) “outlier” detection methods based on $F_{\text{ST}}$ that detect loci with high differenciation compared to the rest of the genomes and, (ii) environmental association methods that test the association between allele frequencies and environmental variables. In this article, we present a new $F_{\text{ST}}$-based genome scan method, BayeScEnv, which incorporates environmental information in the form of “environmental differentiation”. It is based on the F model but as opposed to existing approaches it considers two locus-specific effects, one due to divergent selection and another due to other processes such as differences in mutation rates across loci or background selection. Simulation studies showed that our method has a much lower false positive rate than an existing $F_{\text{ST}}$-based method, BayeScan, under a wide range of demographic scenarios. Although it had lower power, it leads to a better compromise between power and false positive rate. We apply our method to Human and Salmon datasets and show that it can be used successfully to study local adaptation. The method was developped in C++ and is avaible at this http URL

Visualizing spatial population structure with estimated effective migration surfaces

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Visualizing spatial population structure with estimated effective migration surfaces
Desislava Petkova, John Novembre, Matthew Stephens
doi: http://dx.doi.org/10.1101/011809

Genetic data often exhibit patterns that are broadly consistent with “isolation by distance” – a phenomenon where genetic similarity tends to decay with geographic distance. In a heterogeneous habitat, decay may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate the genetic differentiation between groups located close in space. We use the concept of “effective migration” to model the relationship between genetics and geography: in this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to quantify and visualize variation in effective migration across the habitat, which can be used to identify potential barriers to gene flow, from geographically indexed large-scale genetic data. Our approach uses a population genetic model to relate underlying migration rates to expected pairwise genetic dissimilarities, and estimates migration rates by matching these expectations to the observed dissimilarities. We illustrate the potential and limitations of our method using simulations and data from elephant, human, and Arabidopsis thaliana populations. The resulting visualizations highlight important features of the spatial population structure that are difficult to discern using existing methods for summarizing genetic variation such as principal components analysis.

Reveel: large-scale population genotyping using low-coverage sequencing data

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Reveel: large-scale population genotyping using low-coverage sequencing data
Lin Huang, Bo Wang, Ruitang Chen, Sivan Bercovici, Serafim Batzoglou
doi: http://dx.doi.org/10.1101/011882

Population low-coverage whole-genome sequencing is rapidly emerging as a prominent approach for discovering genomic variation and genotyping a cohort. This approach combines substantially lower cost than full-coverage sequencing with whole-genome discovery of low-allele-frequency variants, to an extent that is not possible with array genotyping or exome sequencing. However, a challenging computational problem arises when attempting to discover variants and genotype the entire cohort. Variant discovery and genotyping are relatively straightforward on a single individual that has been sequenced at high coverage, because the inference decomposes into the independent genotyping of each genomic position for which a sufficient number of confidently mapped reads are available. However, in cases where low-coverage population data are given, the joint inference requires leveraging the complex linkage disequilibrium patterns in the cohort to compensate for sparse and missing data in each individual. The potentially massive computation time for such inference, as well as the missing data that confound low-frequency allele discovery, need to be overcome for this approach to become practical. Here, we present Reveel, a novel method for single nucleotide variant calling and genotyping of large cohorts that have been sequenced at low coverage. Reveel introduces a novel technique for leveraging linkage disequilibrium that deviates from previous Markov-based models. We evaluate Reveel???s performance through extensive simulations as well as real data from the 1000 Genomes Project, and show that it achieves higher accuracy in low-frequency allele discovery and substantially lower computation cost than previous state-of-the-art methods.

Bet-hedging, seasons and the evolution of behavioral diversity in Drosophila

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Bet-hedging, seasons and the evolution of behavioral diversity in Drosophila
Jamey Kain, Sarah Zhang, Mason Klein, Aravinthan Samuel, Benjamin de Bivort
doi: http://dx.doi.org/10.1101/012021

Organisms use various strategies to cope with fluctuating environmental conditions. In diversified bet-hedging, a single genotype exhibits phenotypic heterogeneity with the expectation that some individuals will survive transient selective pressures. To date, empirical evidence for bet-hedging is scarce. Here, we observe that individual Drosophila melanogaster flies exhibit striking variation in light- and temperature-preference behaviors. With a modeling approach that combines real world weather and climate data to simulate temperature preference-dependent survival and reproduction, we find that a bet-hedging strategy may underlie the observed inter-individual behavioral diversity. Specifically, bet-hedging outcompetes strategies in which individual thermal preferences are heritable. Animals employing bet-hedging refrain from adapting to the coolness of spring with increased warm-seeking that inevitably becomes counterproductive in the hot summer. This strategy is particularly valuable when mean seasonal temperatures are typical, or when there is considerable fluctuation in temperature within the season. The model predicts, and we experimentally verify, that the behaviors of individual flies are not heritable. Finally, we model the effects of historical weather data, climate change, and geographic seasonal variation on the optimal strategies underlying behavioral variation between individuals, characterizing the regimes in which bet-hedging is advantageous.

>msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding

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msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding
Anil Raj, Heejung Shim, Yoav Gilad, Jonathan K Pritchard, Matthew Stephens
doi: http://dx.doi.org/10.1101/012013

Motivation: Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework underestimates the substantial variation in the DNase I cleavage profiles across factor-bound genomic locations and across replicate measurements of chromatin accessibility. Results: In this work, we adapt a multi-scale modeling framework for inhomogeneous Poisson processes to better model the underlying variation in DNase I cleavage patterns across genomic locations bound by a transcription factor. In addition to modeling variation, we also model spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-Seq peaks for those factors. Finally, we propose an extension to this framework that allows for a more flexible background model and evaluate the additional gain in accuracy achieved when the background model parameters are estimated using DNase-seq data from naked DNA. The proposed model can also be applied to paired-end ATAC-seq and DNase-seq data in a straightforward manner. Availability: msCentipede, a Python implementation of an algorithm to infer transcription factor binding using this model, is made available at https://github.com/rajanil/msCentipede