DOCSLIB.ORG

Scalable, High-Performance Forward Time Population Genetic Simulation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Scalable, High-Performance Forward Time Population Genetic Simulation Scalable, High-Performance Forward Time Population Genetic Simulation A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical Engineering and Computer Science of the College of Engineering and Applied Sciences March 26, 2018 by Patrick P. Putnam BSCS, University of Cincinnati, 2008 Thesis Advisor and Committee Chair: Dr. Philip A. Wilsey Abstract Forward-time population genetic simulators are computational tools used in the study of population genetics. Simulations aim to evolve the genetic state of a population relative to a set of genetic models that reflect the processes that occur in nature under various configurations. Often, these simulations are limited to evolutionary scales that can be represented within the memory space and feasibly computed using a standard workstation computer. This presents a general challenge of how to represent the genetics of a population to enable evolutionary scenarios of sufficient scale to be performed on a memory constrained system. In addition, as the evolutionary scales increase so too does the computational time necessary to complete the simulation. This work considers the general problems of scale and performance as they relate to forward-time population genetic simulation. It explores the representation of a population from the perspective of a graph. Improved memory utilization and computational performance are achieved through the use of a binary adjacency matrix representation of the graph. This use of this representation is generally uncommon in forward-time population genetic simulation. Further improvements are made to the performance of the simulator through the use of parallel computation. This work considers a forward-time population genetic simulation from both a task- and a data- parallel perspective. Each of these perspectives present certain challenges and offer different levels of performance gains. The utilization of the binary adjacency matrix representation enables each of these parallel approaches to be achieved. Finally, although scale and performance improvements are enabled through the use of a binary adjacency matrix representation of the graph, it does have limits in forward-time population genetic simulation. These limits are related to the density of the graph. This work offers a situation where this representation would not be beneficial. ii This page would be intentionally left blank. iii Acknowledgements I would like to express my deepest gratitude to my advisor, Dr. Philip A. Wilsey. His coninued support and guidance throughout the course of my studies has been immeasurable. He has been an invaluable asset over the years and has demonstrated an overwhelming level of patience as I worked towards completing my education while advancing my career. I would also like to thank my committee member and manager, Dr. Ge Zhang, without whom I would not have started down this topic of study. Finally, I would like to thank my committee members Dr. Fred Beyette, Dr. Yizong Cheng, and Dr. Karen Davis for their valuable time and involvement in my journey. iv Contents 1 Introduction 1 1.1 Background . 1 1.2 Computer Simulations . 2 1.3 This work . 3 2 Population Genetics 5 2.1 Change in a Population . 6 2.1.1 Mutation . 6 2.1.2 Recombination . 7 2.1.3 Selection . 8 3 Base design of a Forward-Time Population Genetic Simulation 9 3.1 Graph Interpretation of a Population . 10 3.1.1 Genome Sequence Representation . 12 3.1.2 Adjacency List Representation . 14 3.1.3 Adjacency Matrix Representation . 15 3.2 Genetic Models . 18 3.2.1 Mutation Model . 18 3.2.2 Recombination . 19 3.2.3 Phenotype Evaluation . 21 3.2.4 Fitness . 22 3.2.5 Selection . 22 3.3 Results . 22 v 3.3.1 Evolutionary Scenarios . 23 3.3.2 Simulators and Workstation Configuration . 24 3.3.3 Memory Utilization Comparison . 24 3.3.4 Medium Scale Simulation . 26 3.3.5 Large Scale Simulation . 27 4 Task Parallelism in Forward-Time Population Genetic Simulation 29 4.1 Task Parallelism . 31 4.1.1 Partitioning . 33 4.1.2 Batch Model . 33 4.1.3 Pipeline Model . 35 4.2 Results . 36 4.2.1 Simulator and Hardware Configuration . 36 4.2.2 Evolutionary Scenarios . 37 4.2.3 Small Evolutionary Scale . 37 4.2.4 Medium Evolutionary Scale . 38 4.2.5 Large Evolutionary Scale . 40 5 Data Parallelism in Forward-Time Population Genetic Simulation 43 5.1 Graphics Processing Units . 43 5.1.1 GPU vs CPU . 44 5.1.2 Kernels . 45 5.1.3 GPU Challenge . 46 5.2 Data Parallel Life Cycle Models . 47 5.2.1 Random Number Generation . 47 5.2.2 Recombination . 48 5.2.3 Orchestration . 50 5.3 Results . 51 5.3.1 Evolutionary Scenario . 51 5.3.2 Small Scale Simulation . 52 5.3.3 Medium Scale Simulation . 53 vi 5.3.4 Large Scale Simulation . 54 5.3.5 Extra Large Scale Simulation . 54 6 Limit for use 56 6.1 Allele based simulations . 57 7 Conclusion 60 vii List of Figures 2.1 Example of genetic crossover over . 7 3.1 High-level abstraction of a population as a graph . 11 3.2 Small Scale Simulation Comparison . 26 3.3 Medium Scale Simulation Comparison . 27 3.4 Large Scale Simulation Comparison . 28 4.1 Batch Model Concept . 34 4.2 Pipeline Model Concept . 35 4.3 QTLSimMT - Small Scale; Neutral . 38 4.4 QTLSimMT - Small Scale; Selected . 39 4.5 QTLSimMT - Medium Scale; Neutral . 39 4.6 QTLSimMT - Medium Scale; Selected . 40 4.7 QTLSimMT - Large Scale; Neutral . 41 4.8 QTLSimMT - Large Scale; Selected . 41 5.1 QTLSimCUDA - Small scale simulation performance results . 53 5.2 QTLSimCUDA - Medium scale simulation performance results . 53 5.3 QTLSimCUDA - Large scale simulation performance results . 54 5.4 QTLSimCUDA -Extra large scale simulation performance results . 55 6.1 Locus graph . 57 viii List of Tables 3.1 Simulation scales . 23 3.2 Hardware configuration - Workstation #1 . 24 3.3 Breakdown of memory footprint of genome graph representations . 25 3.4 Memory comparison of Genome Graph representations . 25 4.1 Hardware Configuration - Workstation #2 . 37 4.2 Common Population Configuration . 37 4.3 Evolutionary scales and threading configuration . 37 5.1 GPU specification . 52 5.2 Extra Large evolutionary scale . 52 ix List of Algorithms 1 Outline of a FTPGS . 10 2 Mutation Algorithm . 19 3 Unordered Adjacency Recombination Algorithm . 21 x Chapter 1 Introduction 1.1 Background Populations of organisms are complex systems. Loosely defined, a population is a collection of or- ganisms that are grouped together, typically by their physical location. Changes in the environment where a population resides help to govern the cyclic process of a population's survival. That is, as the environment changes individuals in a population are forced to adapt. Those individuals whom are better suited for the current environment, or are better fit, are more likely to produce offspring that will survive in the next generation. Offspring inherit from their parents the attributes, or traits, that have enabled them to survive. As each new generation matures and procreates the population continues to survive, changing with the environment. The phenotype is the outward manifestation of the internal genetic structure, or genome, of an individual as it interact with the environment. In other words, the phenotype is a collection of observed traits. Traits may be associated with specific heritable units of genetic material called genes. Offspring inherit one copy of each gene from each of their parents. Naturally occurring processes, such as recombination and mutation, may alter the genetic material of a parent's gene. This may lead to an offspring inheriting a variant of their parent's gene. Although, there are relatively few genetic variations between a human child's genome and its parents [1]. At the population level, however, there may exist multiple forms of the same gene. Each specific form of a gene is referred to as an allele. Alleles are a basic unit of study in Population Genetics. 1 The field of Population Genetics is generally interested in studying the genetic landscape of.
Load more

Recommended publications
  • Sequence Divergence Impedes Crossover More Than Noncrossover Events During Mitotic Gap Repair in Yeast
    Copyright Ó 2008 by the Genetics Society of America DOI: 10.1534/genetics.108.090233 Sequence Divergence Impedes Crossover More Than Noncrossover Events During Mitotic Gap Repair in Yeast Caroline Welz-Voegele and Sue Jinks-Robertson1 Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina 27710 Manuscript received April 11, 2008 Accepted for publication May 8, 2008 ABSTRACT Homologous recombination between dispersed repeated sequences is important in shaping eukaryotic genome structure, and such ectopic interactions are affected by repeat size and sequence identity. A transformation-based, gap-repair assay was used to examine the effect of 2% sequence divergence on the efficiency of mitotic double-strand break repair templated by chromosomal sequences in yeast. Because the repaired plasmid could either remain autonomous or integrate into the genome, the effect of sequence divergence on the crossover–noncrossover (CO–NCO) outcome was also examined. Finally, proteins important for regulating the CO–NCO outcome and for enforcing identity requirements during recombination were examined by transforming appropriate mutant strains. Results demonstrate that the basic CO–NCO outcome is regulated by the Rad1-Rad10 endonuclease and the Sgs1 and Srs2 helicases, that sequence divergence impedes CO to a much greater extent than NCO events, that an intact mismatch repair system is required for the discriminating identical and nonidentical repair templates, and that the Sgs1 and Srs2 helicases play additional, antirecombination roles when the interacting sequences are not identical. ECOMBINATION is a high-fidelity process that persed throughout the genome. Such ectopic inter- R copies information from one DNA duplex to re- actions are important for shaping genome structure, pair single- or double-strand discontinuities in another with NCO events likely driving the concerted evolution DNA duplex (reviewed by Krogh and Symington of multigene families and COs leading to various types 2004).
    [Show full text]
  • IN EVOLUTION JACK LESTER KING UNIVERSITY of CALIFORNIA, SANTA BARBARA This Paper Is Dedicated to Retiring University of California Professors Curt Stern and Everett R
    THE ROLE OF MUTATION IN EVOLUTION JACK LESTER KING UNIVERSITY OF CALIFORNIA, SANTA BARBARA This paper is dedicated to retiring University of California Professors Curt Stern and Everett R. Dempster. 1. Introduction Eleven decades of thought and work by Darwinian and neo-Darwinian scientists have produced a sophisticated and detailed structure of evolutionary ,theory and observations. In recent years, new techniques in molecular biology have led to new observations that appear to challenge some of the basic theorems of classical evolutionary theory, precipitating the current crisis in evolutionary thought. Building on morphological and paleontological observations, genetic experimentation, logical arguments, and upon mathematical models requiring simplifying assumptions, neo-Darwinian theorists have been able to make some remarkable predictions, some of which, unfortunately, have proven to be inaccurate. Well-known examples are the prediction that most genes in natural populations must be monomorphic [34], and the calculation that a species could evolve at a maximum rate of the order of one allele substitution per 300 genera- tions [13]. It is now known that a large proportion of gene loci are polymorphic in most species [28], and that evolutionary genetic substitutions occur in the human line, for instance, at a rate of about 50 nucleotide changes per generation [20], [24], [25], [26]. The puzzling observation [21], [40], [46], that homologous proteins in different species evolve at nearly constant rates is very difficult to account for with classical evolutionary theory, and at the very least gives a solid indication that there are qualitative differences between the ways molecules evolve and the ways morphological structures evolve.
    [Show full text]
  • Experiments on the Role of Deleterious Mutations As Stepping Stones In
    Experiments on the role of deleterious mutations as PNAS PLUS stepping stones in adaptive evolution Arthur W. Covert IIIa,b,c,d, Richard E. Lenskia,c,e,1, Claus O. Wilkec,d, and Charles Ofriaa,b,c,1 aProgram in Ecology, Evolutionary Biology and Behavior, Departments of bComputer Science and Engineering and eMicrobiology and Molecular Genetics, and cBEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824; and dCenter for Computational Biology, Institute for Cellular and Molecular Biology, and Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712 Contributed by Richard E. Lenski, July 16, 2013 (sent for review April 22, 2013) Many evolutionary studies assume that deleterious mutations spread unless they are tightly linked (5, 6). In an asexual pop- necessarily impede adaptive evolution. However, a later mutation ulation, however, the fortuitous combination, once formed, will that is conditionally beneficial may interact with a deleterious be stably inherited, thereby providing a simple way to traverse a predecessor before it is eliminated, thereby providing access to fitness valley (5–10). adaptations that might otherwise be inaccessible. It is unknown Previous research on a variety of systems, both biological and whether such sign-epistatic recoveries are inconsequential events computational, has documented many examples of epistatic inter- or an important factor in evolution, owing to the difficulty of actions between mutations (3, 6, 11, 14–24), including a few cases monitoring the effects and fates of all mutations during experi- of mutational pairs that are individually deleterious but jointly ments with biological organisms.
    [Show full text]
  • The Neutral Theory of Molecular Evolution
    Copyright 2000 by the Genetics Society of America Perspectives Anecdotal, Historical and Critical Commentaries on Genetics Edited by James F. Crow and William F. Dove Thomas H. Jukes (1906±1999) James F. Crow Genetics Department, University of Wisconsin, Madison, Wisconsin 53706 OM Jukes accepted our invitation to write a Perspec- duced an immediate outcry from traditional students Ttives on the early history of molecular evolution, of evolution, undoubtedly abetted by the title. In the and in August 1999 he sent a rough beginning con- ensuing polemics, Kimura played the major role. King taining some now-forgotten early history. He planned an died prematurely in 1983 and Jukes wrote mainly about extensive revision and continuation, but on November 1 other things, although he did participate in one joint his death intervened. We have decided to publish his paper (Jukes and Kimura 1984). One of his interests early draft, realizing that it was but a start toward the was the evolution of the genetic code (Jukes 1983). I article that he had planned. particularly liked his showing how, in an orderly sequen- Tom, along with Jack L. King and Motoo Kimura, tial way, mutation pressure in the codon and anti-codon formulated the neutral theory of molecular evolution. could produce the unexpected codes in bacteria and Earlier, the idea had been foreshadowed by Sueoka mitochondria (Jukes 1985). He also developed a widely (1962) and Freese (1962). They had each suggested used correction for multiple undetected changes in evo- mutation pressure of near-neutral changes to account lutionary base substitutions (Jukes and Cantor 1969).
    [Show full text]
  • Population Genetics of Neutral Mutations in Exponentially
    The Annals of Applied Probability 2013, Vol. 23, No. 1, 230–250 DOI: 10.1214/11-AAP824 c Institute of Mathematical Statistics, 2013 POPULATION GENETICS OF NEUTRAL MUTATIONS IN EXPONENTIALLY GROWING CANCER CELL POPULATIONS By Rick Durrett1 Duke University In order to analyze data from cancer genome sequencing projects, we need to be able to distinguish causative, or “driver,” mutations from “passenger” mutations that have no selective effect. Toward this end, we prove results concerning the frequency of neutural mutations in exponentially growing multitype branching processes that have been widely used in cancer modeling. Our results yield a simple new population genetics result for the site frequency spectrum of a sample from an exponentially growing population. 1. Introduction. It is widely accepted that cancers result from an accu- mulation of mutations that increase the fitness of tumor cells compared to the cells that surround them. A number of studies [Sj¨oblom et al. (2006), Wood et al. (2007), Parsons et al. (2008), The Cancer Genome Atlas (2008) and Jones et al. (2008, 2010)] have sequenced the genomes of tumors in or- der to find the causative or “driver” mutations. However, due to the large number of genes being sequenced, one also finds a large number of “passen- ger” mutations that are genetically neutral and hence have no role in the disease. To explain the issues involved in distinguishing the two types of mutations, it is useful to take a look at a data set. Wood et al. (2007) did a “discovery” screen in which 18,191 genes were sequenced in 11 colorectal cancers, and then a “validation” screen in which the top candidates were sequenced in 96 additional tumors.
    [Show full text]
  • Methods from Statistical Computing for Genetic Analysis of Complex Traits
    Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1373 Methods from Statistical Computing for Genetic Analysis of Complex Traits BEHRANG MAHJANI ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-554-9574-9 UPPSALA urn:nbn:se:uu:diva-284378 2016 Dissertation presented at Uppsala University to be publicly examined in 2446, Lägerhyddsvägen 2, Uppsala, Tuesday, 7 June 2016 at 13:15 for the degree of Doctor of Philosophy. The examination will be conducted in English. Faculty examiner: Assistant Professor Silvia Delgado Olabarriaga (BioInformatics Laboratory, Clinical Epidemiology, Biostatistics and Bioinformatics Department (KEBB), Academic Medical Centre of the University of Amsterdam (AMC)). Abstract Mahjani, B. 2016. Methods from Statistical Computing for Genetic Analysis of Complex Traits. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1373. 42 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9574-9. The goal of this thesis is to explore, improve and implement some advanced modern computational methods in statistics, focusing on applications in genetics. The thesis has three major directions. First, we study likelihoods for genetics analysis of experimental populations. Here, the maximum likelihood can be viewed as a computational global optimization problem. We introduce a faster optimization algorithm called PruneDIRECT, and explain how it can be parallelized for permutation testing using the Map-Reduce framework. We have implemented PruneDIRECT as an open source R package, and also Software as a Service for cloud infrastructures (QTLaaS). The second part of the thesis focusses on using sparse matrix methods for solving linear mixed models with large correlation matrices.
    [Show full text]
  • Effectively Neutral Mutations
    Proc. Natl. Acad. Sci. USA Vol. 76, No. 7, pp. 3440-3444, July 1979 Genetics Model of effectively neutral mutations in which selective constraint is incorporated (molecular evolution/protein polymorphism/population genetics/neutral mutation theory) MOTOO KIMURA National Institute of Genetics, Mishima 411, Japan Contributed by Motoo Kimura, April 25, 1979 ABSTRACT Based on the idea that selective neutrality is however, Ohta's model has a drawback in that it cannot ac- the limit when the selective disadvantage becomes indefinitely commodate enough mutations that behave effectively as neutral small, a model of neutral (and nearly neutral) mutations is pro- when the population size gets large. This difficulty can be posed that assumes that the selection coefficient (s') against the overcome by assuming that the selection coefficients follow a mutant at various sites within a cistron (gene) follows a r dis- r distribution. tribution; Ass') = aPe-as's.-l/P(,B), in which a = fi/i and s' is the mean selection coefficient against the mutants (?'> 0; 1 _ ft > 0). The mutation rate for alleles whose selection coef- MODEL OF EFFECTIVELY NEUTRAL ficients s' lie in the range between 0 and 1/(2Ne), in which Ne MUTATIONS is the effective population size, is termed the effectively neutral mutation rate (denoted by ye). Using the model of "infinite Let us assume that the frequency distribution of the selective sites" in population genetics, formulas are derived giving the disadvantage (denoted by s') of mutants among different sites average heterozygosity (he) and evolutionary rate per generation follows the r distribution (kg) in terms of mutant substitutions.
    [Show full text]
  • Theoretical Population Genetics of Repeated Genes Forming a Multigene Family *
    THEORETICAL POPULATION GENETICS OF REPEATED GENES FORMING A MULTIGENE FAMILY * TOMOKO OHTA National Institute of Genetics, Mishima, 411 Japan Manuscript received September 23, 1977 Revised copy received December 19, 1977 ABSTRACT The evolution of repeated genes forming a multigene family in a finite population is studied with special reference to the probability of gene identity, i.e., the identity probability of two gene units chosen from the gene family. This quantity is called clonality and is defined as the sum of squares of the frequencies of gene lineages in the family. The multigene family is under- going continuous unequal somatic crossing over, ordinary interchromosomal crossing over, mutation and random frequency drift. Two measures of clonality are used: clonality within one chromosome and that between two different chromosomes. The equilibrium properties of the means, the variances and the covariance of the two measures of clonality are investigated by using the diffusion equation method under the assumption of constant number of gene units in the multigene family. Some models of natural selection based on clonality are considered. The possible significance of the variance and covari- ance of clonality among the chromosomes on the adaptive differentiation of gene families such as those producing antibodies is discussed. INrecent years, much attention has been paid to the problem of evolution of repeated genes. Although how repeated genes emerged and what role they play in evolution are far from being elucidated, their universal existence in the genome of higher organisms is well demonstrated (see, Proc. Cold Spring Harbor Symp. 1974). HOOD,CAMPBELL and ELGIN (1975) have recently classified repeated genes into three classes: simple-sequence multigene families (highly repeated sequences), multiplicational multigene families (genes for ribosomal RNA, transfer RNA and histone) and informational multigene families (genes for immunoglobulins).
    [Show full text]
  • Intragenic Sex-Chromosomal Crossovers of Xmrk Oncogene Alleles Affect Pigment Pattern Formation and the Severity of Melanoma in Xiphophorus
    Copyright 1999 by the Genetics Society of America Intragenic Sex-Chromosomal Crossovers of Xmrk Oncogene Alleles Affect Pigment Pattern Formation and the Severity of Melanoma in Xiphophorus Heidrun Gutbrod and Manfred Schartl Physiological Chemistry I, Biocenter, University of WuÈrzburg, D-97074 WuÈrzburg, Germany Manuscript received July 23, 1998 Accepted for publication October 21, 1998 ABSTRACT The X and Y chromosomes of the platy®sh (Xiphophorus maculatus) contain a region that encodes several important traits, including the determination of sex, pigment pattern formation, and predisposition to develop malignant melanoma. Several sex-chromosomal crossovers were identi®ed in this region. As the melanoma-inducing oncogene Xmrk is the only molecularly identi®ed constituent, its genomic organization on both sex chromosomes was analyzed in detail. Using X and Y allele-speci®c sequence differences a high proportion of the crossovers was found to be intragenic in the oncogene Xmrk, concentrating in the extracellular domain-encoding region. The genetic and molecular data allowed establishment of an order of loci over z0.6 cM. It further revealed a sequence located within several kilobases of the extracellular domain-encoding region of Xmrk that regulates overexpression of the oncogene. N the platy®sh Xiphophorus maculatus, three sex chro- Two pigment pattern loci of X. maculatus also locate to I mosomes coexist. They have been characterized as the SD and P-containing region. The red-yellow pattern X, Y, and W. Over a large geographic range the individ- (RY) locus [this locus has been referred to by other ual populations are polymorphic for these three chro- research groups as XANT (see Morizot et al.
    [Show full text]
  • Genomic Insights Into Positive Selection
    Review TRENDS in Genetics Vol.22 No.8 August 2006 Genomic insights into positive selection Shameek Biswas and Joshua M. Akey Department of Genome Sciences, University of Washington, 1705 NE Pacific, Seattle, WA 98195, USA The traditional way of identifying targets of adaptive more utilitarian benefits, each target of positive selection evolution has been to study a few loci that one has a story to tell about the historical forces and events hypothesizes a priori to have been under selection. that have shaped the history of a population. This approach is complicated because of the confound- Several genome-wide analyses for positive selection ing effects that population demographic history and have been performed in a variety of species. In this review, selection have on patterns of DNA sequence variation. In we summarize some of the recent studies, primarily principle, multilocus analyses can facilitate robust focusing on humans, critically evaluate what genome- inferences of selection at individual loci. The deluge of wide scans for selection are and are not likely to find and large-scale catalogs of genetic variation has stimulated suggest future avenues of research. A brief overview of many genome-wide scans for positive selection in statistical methods used to detect deviations from several species. Here, we review some of the salient neutrality is summarized in Box 1. For more detailed observations of these studies, identify important chal- discussions, see Refs [6,7]. lenges ahead, consider the limitations of genome-wide scans for selection and discuss the potential significance Thinking genomically of a comprehensive understanding of genomic patterns Positive selection perturbs patterns of genetic variation of selection for disease-related research.
    [Show full text]
  • The Chromosomal Basis of Inheritance Worksheet Answers
    The Chromosomal Basis Of Inheritance Worksheet Answers Immanuel crayoning least while microseismic Linoel intervenes disguisedly or obumbrate interspatially. Bifacial and coseismal Chandler know, but Edmond obstetrically inflame her sequencer. Pierce gulls wherein? The inheritance was inherited together as a philadelphia chromosome pair consists of reasons, answer worksheet answer key in. Therefore, mutations can explain failure of six rare outcomes that misery to defy truth common genetic rules. Morgan made changes in humans is called epistasis describes what was a allele comes in tetraploids have such contains one gene inheritance of confusion about chromosomes? This is why alleles on a given chromosome are not always inherited together. Please can an institutional email address. Explain how these data generates genetic evidence leading to the chromosomal basis of inheritance and one another gamete is known as. Polyploids are inherited in experiments with answers you must work answer worksheet. This inheritance pattern of inheritance chromosomal basis of one gene to answer worksheet answer key worksheets are. Law of all the mother or a dominant and other diseases of certain genes are usually develops as violet flowers to assort independently of hla genes lie on a multiple of the chromosomal basis of haploid. Genetic Disorders goes spare the DNA mutations and couples each by them master a disease as people are affected by. In order on nonhomologous chromosome can not display mendelian inheritance? In the case of organ development, and therefore our genes must be located on chromosomes. The numbers of additive alleles in parentheses. DNA and associated proteins. The homozygous recessive parents will be unable to cross over.
    [Show full text]
  • Evolution on Neutral Networks Accelerates the Ticking Rate of the Molecular Clock Arxiv:1307.0968V2 [Q-Bio.PE] 24 Sep 2014
    Evolution on neutral networks accelerates the ticking rate of the molecular clock Susanna Manrubia1 and Jos´eA. Cuesta2;3 Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid 1 Dept. de Biolog´ıade Sistemas, Centro Nacional de Biotecnolog´ıa(CSIC) c/ Darwin 3, 28045 Madrid, Spain. 2 Dept. de Matem´aticas,Universidad Carlos III de Madrid 28911 Legan´es,Madrid, Spain. 3 Instituto de Biocomputaci´ony F´ısicade Sistemas Complejos (BIFI) Universidad de Zaragoza, 50009 Zaragoza, Spain. June 17, 2021 Abstract Large sets of genotypes give rise to the same phenotype because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, geno- types organize themselves into neutral networks (NN). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NN with arbitrary topology we analyze the arXiv:1307.0968v2 [q-bio.PE] 24 Sep 2014 effect of assortativity, of NN (phenotype) fitness, and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive \phenotypic entrapment" entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives.
    [Show full text]