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Open Access Publications from the University of California

Scholarly Works (23 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2014)
fwdpp is a C++ library of routines intended to facilitate the development of forward-time simulations under arbitrary mutation and fitness models. The library design provides a combination of speed, low memory overhead, and modeling flexibility not currently available from other forward simulation tools. The library is particularly useful when the simulation of large populations is required, as programs implemented using the library are much more efficient than other available forward simulation programs.
    Cover page: A C++ Template Library for Efficient Forward-Time Population Genetic Simulation of Large Populations
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2009)
    Background

    In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method.

    Results

    The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.

    Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown.

    Conclusion

    In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

      Cover page: Automating approximate Bayesian computation by local linear regressionCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2019)
      Predictions about the effect of natural selection on patterns of linked neutral variation are largely based on models involving the rapid fixation of unconditionally beneficial mutations. However, when phenotypes adapt to a new optimum trait value, the strength of selection on individual mutations decreases as the population adapts. Here, I use explicit forward simulations of a single trait with additive-effect mutations adapting to an "optimum shift." Detectable "hitchhiking" patterns are only apparent if (i) the optimum shifts are large with respect to equilibrium variation for the trait, (ii) mutation rates to large-effect mutations are low, and (iii) large-effect mutations rapidly increase in frequency and eventually reach fixation, which typically occurs after the population reaches the new optimum. For the parameters simulated here, partial sweeps do not appreciably affect patterns of linked variation, even when the mutations are strongly selected. The contribution of new mutations vs. standing variation to fixation depends on the mutation rate affecting trait values. Given the fixation of a strongly selected variant, patterns of hitchhiking are similar on average for the two classes of sweeps because sweeps from standing variation involving large-effect mutations are rare when the optimum shifts. The distribution of effect sizes of new mutations has little effect on the time to reach the new optimum, but reducing the mutational variance increases the magnitude of hitchhiking patterns. In general, populations reach the new optimum prior to the completion of any sweeps, and the times to fixation are longer for this model than for standard models of directional selection. The long fixation times are due to a combination of declining selection pressures during adaptation and the possibility of interference among weakly selected sites for traits with high mutation rates.
        Cover page: Polygenic Adaptation to an Environmental Shift: Temporal Dynamics of Variation Under Gaussian Stabilizing Selection and Additive Effects on a Single TraitCreative Commons 'BY' version 4.0 license
        • Thesis
        • Peer Reviewed
        UC Irvine Electronic Theses and Dissertations (2018)

        Most physical traits of agricultural and medical importance are complex, meaning that they are determined by multiple genetic and environmental factors. For decades it has a been a major goal in biology to be able to understand and predict complex phenotypes based on an individuals genomic sequence. Modern genotyping technologies have enabled the collection of massive samples of paired genotype-phenotype data. Despite this deluge of data, the genetic basis of complex traits remains unclear. Here I attempt to address this problem through a detailed simulation study based on explicit population genetic models for the maintenance of heritable phenotypic variation for a complex disease trait. The main conclusion of this study is that gene-based recessivity, under which compound heterozygotes can have excess disease risk, should be a leading candidate to explain some otherwise perplexing statistical properties of complex diseases. I complement this simulation study with an implementation of a statistical method found to be more powerful under a gene-based recessive genetic architecture. I then further support the assumed fitness model utilized in the simulation study through an empirical analysis of selection in a contemporary human population. Through studying the relationships between phenotypes and lifetime reproductive success, I showed that weak stabilizing selection is common on human traits and that many traits of clinical significance are under directional selection.

        • 2 supplemental files
        Cover page: Gene-based Dominance and Stabilizing Selection on Human Complex TraitsCreative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2010)
        Several recent studies have focused on the evolution of recently duplicated genes in Drosophila. Currently, however, little is known about the evolutionary forces acting upon duplications that are segregating in natural populations. We used a high-throughput, paired-end sequencing platform (Illumina) to identify structural variants in a population sample of African D. melanogaster. Polymerase chain reaction and sequencing confirmation of duplications detected by multiple, independent paired-ends showed that paired-end sequencing reliably uncovered the break points of structural rearrangements and allowed us to identify a number of tandem duplications segregating within a natural population. Our confirmation experiments show that rates of confirmation are very high, even at modest coverage. Our results also compare well with previous studies using microarrays (Emerson J, Cardoso-Moreira M, Borevitz JO, Long M. 2008. Natural selection shapes genome wide patterns of copy-number polymorphism in Drosophila melanogaster. Science. 320:1629–1631. and Dopman EB, Hartl DL. 2007. A portrait of copy-number polymorphism in Drosophila melanogaster. Proc Natl Acad Sci U S A. 104:19920–19925.), which both gives us confidence in the results of this study as well as confirms previous microarray results.We were also able to identify whole-gene duplications, such as a novel duplication of Or22a, an olfactory receptor, and identify copy-number differences in genes previously known to be under positive selection, like Cyp6g1, which confers resistance to dichlorodiphenyltrichloroethane. Several “hot spots” of duplications were detected in this study, which indicate that particular regions of the genome may be more prone to generating duplications. Finally, population frequency analysis of confirmed events also showed an excess of rare variants in our population, which indicates that duplications segregating in the population may be deleterious and ultimately destined to be lost from the population.
          Cover page: Validation of Rearrangement Break Points Identified by Paired-End Sequencing in Natural Populations of Drosophila melanogasterCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2007)
          Genome-wide scanning for signals of recent positive selection is essential for a comprehensive and systematic understanding of human adaptation. Here, we present a genomic survey of recent local selective sweeps, especially aimed at those nearly or recently completed. A novel approach was developed for such signals, based on contrasting the extended haplotype homozygosity (EHH) profiles between populations. We applied this method to the genome single nucleotide polymorphism (SNP) data of both the International HapMap Project and Perlegen Sciences, and detected widespread signals of recent local selection across the genome, consisting of both complete and partial sweeps. A challenging problem of genomic scans of recent positive selection is to clearly distinguish selection from neutral effects, given the high sensitivity of the test statistics to departures from neutral demographic assumptions and the lack of a single, accurate neutral model of human history. We therefore developed a new procedure that is robust across a wide range of demographic and ascertainment models, one that indicates that certain portions of the genome clearly depart from neutrality. Simulations of positive selection showed that our tests have high power towards strong selection sweeps that have undergone fixation. Gene ontology analysis of the candidate regions revealed several new functional groups that might help explain some important interpopulation differences in phenotypic traits.
            Cover page: A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human GenomeCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2008)
            The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to distinguish between these models, and apply it to multi-locus data from Drosophila melanogaster. We investigate the extent to which inference about the strength of selection is sensitive to assumptions about the underlying distributions of the rates of substitution and recombination, the strength of selection, heterogeneity in mutation rate, as well as the population's demographic history. We show that assuming fixed values of selection parameters in estimation leads to overestimates of the strength of selection and underestimates of the rate. We estimate parameters for an African population of D. melanogaster ((s) over cap similar to 2E-03, 2N (lambda) over cap similar to 2E-04) and compare these to previous estimates. Finally, we show that surveying larger genomic regions is expected to lend much more discriminatory power to the approach. It will thus be of great interest to apply this method to emerging whole-genome polymorphism data sets in many taxa.
              Cover page: An Approximate Bayesian Estimator Suggests Strong, Recurrent Selective Sweeps in DrosophilaCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2014)
              A novel approach for dissecting complex traits is to experimentally evolve laboratory populations under a controlled environment shift, resequence the resulting populations, and identify single nucleotide polymorphisms (SNPs) and/or genomic regions highly diverged in allele frequency. To better understand the power and localization ability of such an evolve and resequence (E&R) approach, we carried out forward-in-time population genetics simulations of 1 Mb genomic regions under a large combination of experimental conditions, then attempted to detect significantly diverged SNPs. Our analysis indicates that the ability to detect differentiation between populations is primarily affected by selection coefficient, population size, number of replicate populations, and number of founding haplotypes. We estimate that E&R studies can detect and localize causative sites with 80% success or greater when the number of founder haplotypes is over 500, experimental populations are replicated at least 25-fold, population size is at least 1,000 diploid individuals, and the selection coefficient on the locus of interest is at least 0.1. More achievable experimental designs (less replicated, fewer founder haplotypes, smaller effective population size, and smaller selection coefficients) can have power of greater than 50% to identify a handful of SNPs of which one is likely causative. Similarly, in cases where s ≥ 0.2, less demanding experimental designs can yield high power.
                Cover page: The Power to Detect Quantitative Trait Loci Using Resequenced, Experimentally Evolved Populations of Diploid, Sexual Organisms
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2016)
                Genome-wide association studies (GWAS) have associated many single variants with complex disease, yet the better part of heritable complex disease risk remains unexplained. Analytical tools designed to work under specific population genetic models are needed. Rare variants are increasingly shown to be important in human complex disease, but most existing GWAS data do not cover rare variants. Explicit population genetic models predict that genes contributing to complex traits and experiencing recurrent, unconditionally deleterious, mutation will harbor multiple rare, causative mutations of subtle effect. It is difficult to identify genes harboring rare variants of large effect that contribute to complex disease risk via the single marker association tests typically used in GWAS. Gene/region-based association tests may have the power detect associations by combining information from multiple markers, but have yielded limited success in practice. This is partially because many methods have not been widely applied. Here, we empirically demonstrate the utility of a procedure based on the rank truncated product (RTP) method, filtered to reduce the effects of linkage disequilibrium. We apply the procedure to the Wellcome Trust Case Control Consortium (WTCCC) data set, and uncover previously unidentified associations, some of which have been replicated in much larger studies. We show that, in the absence of significant rare variant coverage, RTP based methods still have the power to detect associated genes. We recommend that RTP-based methods be applied to all existing GWAS data to maximize the usefulness of those data. For this, we provide efficient software implementing our procedure.
                  Cover page: Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2015)
                  Transposable elements are a common source of genetic variation that may play a substantial role in contributing to gene expression variation. However, the contribution of transposable elements to expression variation thus far consists of a handful of examples. We used previously published gene expression data from 37 inbred Drosophila melanogaster lines from the Drosophila Genetic Reference Panel to perform a genome-wide assessment of the effects of transposable elements on gene expression. We found thousands of transcripts with transposable element insertions in or near the transcript and that the presence of a transposable element in or near a transcript is significantly associated with reductions in expression. We estimate that within this example population, ∼2.2% of transcripts have a transposable element insertion, which significantly reduces expression in the line containing the transposable element. We also find that transcripts with insertions within 500 bp of the transcript show on average a 0.67 standard deviation decrease in expression level. These large decreases in expression level are most pronounced for transposable element insertions close to transcripts and the effect diminishes for more distant insertions. This work represents the first genome-wide analysis of gene expression variation due to transposable elements and suggests that transposable elements are an important class of mutation underlying expression variation in Drosophila and likely in other systems, given the ubiquity of these mobile elements in eukaryotic genomes.
                    Cover page: Gene Expression Variation in Drosophila melanogaster Due to Rare Transposable Element Insertion Alleles of Large Effect
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