426 research outputs found
The climate of Mt Wilhelm
In 1966, the Australian National University with assistance
from the Bernice P. Bishop Museum, Hawaii, established a field station
beside the lower Pindaunde Lake at an altitude of 3480 m on the southeast
flank of Mt Wilhelm, the highest point in Papua New Guinea. The
field station has been used by a number of workers in the natural
sciences, many of whose publications are referred to later in this
work. The present volume arises from observations made by three
botanists and their collaborators when members of the Department of
Biogeography and Geomorphology, during the course of their work on
Mt Wilhelm while based on the ANU field station. It is the second in
the Departmental series describing the environment and biota of the
mountain, and will be followed by others dealing with different aspects
of its natural history
A Genealogical Interpretation of Principal Components Analysis
Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference
Recombination rate and selection strength in HIV intra-patient evolution
The evolutionary dynamics of HIV during the chronic phase of infection is
driven by the host immune response and by selective pressures exerted through
drug treatment. To understand and model the evolution of HIV quantitatively,
the parameters governing genetic diversification and the strength of selection
need to be known. While mutation rates can be measured in single replication
cycles, the relevant effective recombination rate depends on the probability of
coinfection of a cell with more than one virus and can only be inferred from
population data. However, most population genetic estimators for recombination
rates assume absence of selection and are hence of limited applicability to
HIV, since positive and purifying selection are important in HIV evolution.
Here, we estimate the rate of recombination and the distribution of selection
coefficients from time-resolved sequence data tracking the evolution of HIV
within single patients. By examining temporal changes in the genetic
composition of the population, we estimate the effective recombination to be
r=1.4e-5 recombinations per site and generation. Furthermore, we provide
evidence that selection coefficients of at least 15% of the observed
non-synonymous polymorphisms exceed 0.8% per generation. These results provide
a basis for a more detailed understanding of the evolution of HIV. A
particularly interesting case is evolution in response to drug treatment, where
recombination can facilitate the rapid acquisition of multiple resistance
mutations. With the methods developed here, more precise and more detailed
studies will be possible, as soon as data with higher time resolution and
greater sample sizes is available.Comment: to appear in PLoS Computational Biolog
Inference of population splits and mixtures from genome-wide allele frequency data
Many aspects of the historical relationships between populations in a species
are reflected in genetic data. Inferring these relationships from genetic data,
however, remains a challenging task. In this paper, we present a statistical
model for inferring the patterns of population splits and mixtures in multiple
populations. In this model, the sampled populations in a species are related to
their common ancestor through a graph of ancestral populations. Using
genome-wide allele frequency data and a Gaussian approximation to genetic
drift, we infer the structure of this graph. We applied this method to a set of
55 human populations and a set of 82 dog breeds and wild canids. In both
species, we show that a simple bifurcating tree does not fully describe the
data; in contrast, we infer many migration events. While some of the migration
events that we find have been detected previously, many have not. For example,
in the human data we infer that Cambodians trace approximately 16% of their
ancestry to a population ancestral to other extant East Asian populations. In
the dog data, we infer that both the boxer and basenji trace a considerable
fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to
domestication, and that East Asian toy breeds (the Shih Tzu and the Pekingese)
result from admixture between modern toy breeds and "ancient" Asian breeds.
Software implementing the model described here, called TreeMix, is available at
http://treemix.googlecode.comComment: 28 pages, 6 figures in main text. Attached supplement is 22 pages, 15
figures. This is an updated version of the preprint available at
http://precedings.nature.com/documents/6956/version/
Inference of Population Structure using Dense Haplotype Data
The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this “chromosome painting” can be summarized as a “coancestry matrix,” which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/
Sexual selection protects against extinction
Reproduction through sex carries substantial costs, mainly because only half of sexual adults produce offspring. It has been theorised that these costs could be countered if sex allows sexual selection to clear the universal fitness constraint of mutation load. Under sexual selection, competition between (usually) males, and mate choice by (usually) females create important intraspecific filters for reproductive success, so that only a subset of males gains paternity. If reproductive success under sexual selection is dependent on individual condition, which depends on mutation load, then sexually selected filtering through ‘genic capture’ could offset the costs of sex because it provides genetic benefits to populations. Here, we test this theory experimentally by comparing whether populations with histories of strong versus weak sexual selection purge mutation load and resist extinction differently. After evolving replicate populations of the flour beetle Tribolium castaneum for ~7 years under conditions that differed solely in the strengths of sexual selection, we revealed mutation load using inbreeding. Lineages from populations that had previously experienced strong sexual selection were resilient to extinction and maintained fitness under inbreeding, with some families continuing to survive after 20 generations of sib × sib mating. By contrast, lineages derived from populations that experienced weak or non-existent sexual selection showed rapid fitness declines under inbreeding, and all were extinct after generation 10. Multiple mutations across the genome with individually small effects can be difficult to clear, yet sum to a significant fitness load; our findings reveal that sexual selection reduces this load, improving population viability in the face of genetic stress
The Evolution of Bat Vestibular Systems in the Face of Potential Antagonistic Selection Pressures for Flight and Echolocation
PMCID: PMC3634842This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Genomics of Divergence along a Continuum of Parapatric Population Differentiation
MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1)
Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies
There is strong evidence that rare variants are involved in complex disease etiology. The first step in implicating rare variants in disease etiology is their identification through sequencing in both randomly ascertained samples (e.g., the 1,000 Genomes Project) and samples ascertained according to disease status. We investigated to what extent rare variants will be observed across the genome and in candidate genes in randomly ascertained samples, the magnitude of variant enrichment in diseased individuals, and biases that can occur due to how variants are discovered. Although sequencing cases can enrich for casual variants, when a gene or genes are not involved in disease etiology, limiting variant discovery to cases can lead to association studies with dramatically inflated false positive rates
Theoretical Formulation of Principal Components Analysis to Detect and Correct for Population Stratification
The Eigenstrat method, based on principal components analysis (PCA), is commonly used both to quantify population relationships in population genetics and to correct for population stratification in genome-wide association studies. However, it can be difficult to make appropriate inference about population relationships from the principal component (PC) scatter plot. Here, to better understand the working mechanism of the Eigenstrat method, we consider its theoretical or “population” formulation. The eigen-equation for samples from an arbitrary number () of populations is reduced to that of a matrix of dimension , the elements of which are determined by the variance-covariance matrix for the random vector of the allele frequencies. Solving the reduced eigen-equation is numerically trivial and yields eigenvectors that are the axes of variation required for differentiating the populations. Using the reduced eigen-equation, we investigate the within-population fluctuations around the axes of variation on the PC scatter plot for simulated datasets. Specifically, we show that there exists an asymptotically stable pattern of the PC plot for large sample size. Our results provide theoretical guidance for interpreting the pattern of PC plot in terms of population relationships. For applications in genetic association tests, we demonstrate that, as a method of correcting for population stratification, regressing out the theoretical PCs corresponding to the axes of variation is equivalent to simply removing the population mean of allele counts and works as well as or better than the Eigenstrat method
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