Detection of Selective Sweeps in Cattle Using Genome-Wide SNP Data 2 3 Holly R
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Approximating Selective Sweeps
Approximating Selective Sweeps by Richard Durrett and Jason Schweinsberg Dept. of Math, Cornell U. Corresponding Author: Richard Durrett Dept. of Mathematics 523 Malott Hall Cornell University Ithaca NY 14853 Phone: 607-255-8282 FAX: 607-255-7149 email: [email protected] 1 ABSTRACT The fixation of advantageous mutations in a population has the effect of reducing varia- tion in the DNA sequence near that mutation. Kaplan, Hudson, and Langley (1989) used a three-phase simulation model to study the effect of selective sweeps on genealogies. However, most subsequent work has simplified their approach by assuming that the number of individ- uals with the advantageous allele follows the logistic differential equation. We show that the impact of a selective sweep can be accurately approximated by a random partition created by a stick-breaking process. Our simulation results show that ignoring the randomness when the number of individuals with the advantageous allele is small can lead to substantial errors. Key words: selective sweep, hitchhiking, coalescent, random partition, paintbox construction 2 When a selectively favorable mutation occurs in a population and is subsequently fixed (i.e., its frequency rises to 100%), the frequencies of alleles at closely linked loci are altered. Alleles present on the chromosome on which the original mutation occurred will tend to increase in frequency, and other alleles will decrease in frequency. Maynard Smith and Haigh (1974) referred to this as the ‘hitchhiking effect,’ because an allele can get a lift in frequency from selection acting on a neighboring allele. They considered a situation with a neutral locus with alleles A and a and a second locus where allele B has a fitness of 1 + s relative to b. -
Livestock Genomics Comes of Age Michel Georges and Leif Anclersson 2
Downloaded from genome.cshlp.org on September 29, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Livestock Genomics Comes of Age Michel Georges and Leif Anclersson 2 1Department of Genetics, Faculty of Veterinary Medicine, University of Liege, 4000-Liege, Belgium; 2Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala Biomedical Centre, 24 Uppsala, Sweden It is estimated that man 1 first domesticated ani- as by an undefined number of polygenes or quan- mals as early as 10,000 BP. Since then, farmers titative trait loci (QTL). Their heritabilities typi- have been unwittingly manipulating livestock cally range from less than 5% to over 50%. Ani- genes by selective breeding. This early genetic en- mal geneticists therefore have had a long-stand- gineering generated a wealth of variation for a ing interest in the genetics of complex inherit- myriad of traits in the different livestock species. ance, the relevance of which is being recognized The dramatic size difference between the Shire increasingly in medical genetics as well. and Shetland pony or the plethora of dog breeds The implementation of breeding schemes are just two illustrations of the diversification ob- that proved so efficient would have been impos- tained by artificial selection. The resulting carica- sible without the organization of extensive phe- ture of naturally occurring variation has played a notypic record keeping. Particularly illustrative major role in Darwin's formalization of his in this respect is the collection of individual re- theory of the evolution of species by natural se- cords (milk yield and composition, type traits, lection. -
A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome
PLoS BIOLOGY A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome Kun Tang1*, Kevin R. Thornton2, Mark Stoneking1 1 Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, 2 Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, United States of America 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. -
Refining the Use of Linkage Disequilibrium As A
HIGHLIGHTED ARTICLE | INVESTIGATION Refining the Use of Linkage Disequilibrium as a Robust Signature of Selective Sweeps Guy S. Jacobs,*,†,1 Timothy J. Sluckin,* and Toomas Kivisild‡ *Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom, †Complexity Institute, Nanyang Technological University, Singapore 637723, and ‡Department of Biological Anthropology, University of Cambridge, Cambridge CB2 1QH, United Kingdom ORCID IDs: 0000-0002-4698-7758 (G.S.J.); 0000-0002-9163-0061 (T.J.S.); 0000-0002-6297-7808 (T.K.) ABSTRACT During a selective sweep, characteristic patterns of linkage disequilibrium can arise in the genomic region surrounding a selected locus. These have been used to infer past selective sweeps. However, the recombination rate is known to vary substantially along the genome for many species. We here investigate the effectiveness of current (Kelly’s ZnS and vmax) and novel statistics at inferring hard selective sweeps based on linkage disequilibrium distortions under different conditions, including a human-realistic demographic model and recombination rate variation. When the recombination rate is constant, Kelly’s ZnS offers high power, but is outperformed by a novel statistic that we test, which we call Za: We also find this statistic to be effective at detecting sweeps from standing variation. When recombination rate fluctuations are included, there is a considerable reduction in power for all linkage disequilibrium-based statistics. However, this can largely be reversed by appropriately controlling for expected linkage disequilibrium using a genetic map. To further test these different methods, we perform selection scans on well-characterized HapMap data, finding that all three statistics—vmax; Kelly’s ZnS; and Za—are able to replicate signals at regions previously identified as selection candidates based on population differentiation or the site frequency spectrum. -
Identification of Selective Sweeps, Major Genes, and Genotype by Diet Interactions Melanie D
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Theses and Dissertations in Animal Science Animal Science Department 12-2015 Genomic Analysis of Sow Reproductive Traits: Identification of Selective Sweeps, Major Genes, and Genotype by Diet Interactions Melanie D. Trenhaile University of Nebraska-Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/animalscidiss Part of the Meat Science Commons Trenhaile, Melanie D., "Genomic Analysis of Sow Reproductive Traits: Identification of Selective Sweeps, Major Genes, and Genotype by Diet Interactions" (2015). Theses and Dissertations in Animal Science. 114. http://digitalcommons.unl.edu/animalscidiss/114 This Article is brought to you for free and open access by the Animal Science Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Theses and Dissertations in Animal Science by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. GENOMIC ANALYSIS OF SOW REPRODUCTIVE TRAITS: IDENTIFICATION OF SELECTIVE SWEEPS, MAJOR GENES, AND GENOTYPE BY DIET INTERACTIONS By Melanie Dawn Trenhaile A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science Major: Animal Science Under the Supervision of Professor Daniel Ciobanu Lincoln, Nebraska December, 2015 GENOMIC ANALYSIS OF SOW REPRODUCTIVE TRAITS: IDENTIFICATION OF SELECTIVE SWEEPS, MAJOR GENES, AND GENOTYPE BY DIET INTERACTIONS Melanie D. Trenhaile, M.S. University of Nebraska, 2015 Advisor: Daniel Ciobanu Reproductive traits, such as litter size and reproductive longevity, are economically important. However, selection for these traits is difficult due to low heritability, polygenic nature, sex-limited expression, and expression late in life. -
The Genome Sequence of Taurine Cattle: a Window to Ruminant Biology and Evolution the Bovine Genome Sequencing and Analysis Consortium, Christine G
REPORTS second model, the two main conditions were para- different issues. In particular, Bhatt and Camerer found 34. W. Seeley et al., J. Neurosci. 27, 2349 (2007). metrically modulated by the two categories, higher insula and ACC activity when comparing choices to 35. J. Downar, A. Crawley, D. Mikulis, K. Davis, Nat. Neurosci. first-order beliefs in dominance-solvable games. 3, 277 (2000). respectively (SOM, S5.1). The activation of the 3. We are considering here coordination without visual or 36. J. Downar, A. Crawley, D. Mikulis, K. Davis, J. Neurophysiol. precuneus was higher for hard dominance-solvable other contact. Nonhuman primates seem able to 87, 615 (2002). games than for easy ones (Fig. 4A and table S10). coordinate their actions (simultaneously pulling on bars 37. K. Davis et al., J. Neurosci. 25, 8402 (2005). The activation of the insula was higher for the to obtain food) when they are in visual contact (45). 38. K. Taylor, D. Seminowicz, K. Davis, Hum. Brain Mapp., 4. J. Mehta, C. Starmer, R. Sugden, Am. Econ. Rev. 84, 658 in press; published online 15 December 2008; highly focal coordination games than for less fo- (1994). 10.1002/hbm.20705. cal ones (Fig. 4B and table S11). Previous studies 5. T. Schelling, J. Conflict Resolution 2, 203 (1958), p. 211. 39. See (47). The NCI can be interpreted as the probability also found that precuneus activity increased when 6. D. Kahneman, Am. Psychol. 58, 697 (2003). that two randomly chosen individuals make the same the number of planned moves increased (40, 41). 7. K. -
Bovinehd Genotyping Beadchip More Than 777,000 Snps That Deliver the Densest Coverage Available for the Bovine Genome
Data Sheet: Agrigenomics BovineHD Genotyping BeadChip More than 777,000 SNPs that deliver the densest coverage available for the bovine genome. of the markers are mapped to the UMD3 bovine genome assembly,3 Highlights which include coverage of autosomal, mitochondrial, and sex-linked • Comprehensive and Uniform Coverage (X/Y) SNPs. Uniform genomic coverage, with an average gap size Evenly distributed polymorphic SNPs with a median < 3 kb of 3.43 kb and a median gap size of 2.68 kb, provides excellent gap spacing SNP density to power robust genome-association studies and CNV detection in cattle (Figure 2).4 The BovineHD BeadChip is the most • Unrivaled Call Rates and Accuracy comprehensive tool in the Illumina portfolio of bovine products. > 99% average call rates and > 99.9% reproducibility • Simple Workflow More than 93% of SNPs featured on the BovineHD BeadChip target PCR- and ligation-free protocol novel SNP loci that were discovered by sequencing > 20 individual breeds of economically important beef and dairy cattle (Table 3). Using • High-Throughput Format Illumina next-generation paired-end sequencing technology, > 90% Up to 8 samples can be interrogated in parallel of the included SNPs were identified from over 180× coverage of the mappable Btt genome. Prioritization for SNP selection included the following parameters: 1) breed-specific expected MAF, 2) Infinium HD Introduction design score, 3) unmapped contig coverage, 4) breed weighting, 5) breed‐specific spacing, and 6) and position based on region of The BovineHD BeadChip (Figure 1) is the most comprehensive genome (ie, exon, repetitive, segmental duplication/CNV). genome-wide genotyping array, providing superior power to interrogate genetic variation across any breed of beef and dairy cattle. -
Rapid Evolution of a Skin-Lightening Allele in Southern African Khoesan
Rapid evolution of a skin-lightening allele in southern African KhoeSan Meng Lina,1,2, Rebecca L. Siforda,b,3, Alicia R. Martinc,d,e,3, Shigeki Nakagomef, Marlo Möllerg, Eileen G. Hoalg, Carlos D. Bustamanteh, Christopher R. Gignouxh,i,j, and Brenna M. Henna,2,4 aDepartment of Ecology and Evolution, State University of New York at Stony Brook, Stony Brook, NY 11794; bThe School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287; cAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114; dProgram in Medical and Population Genetics, Broad Institute, Cambridge, MA 02141; eStanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02141; fSchool of Medicine, Trinity College Dublin, Dublin 2, Ireland; gDST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 8000, South Africa; hDepartment of Genetics, Stanford University, Stanford, CA 94305; iColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045; and jDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 Edited by Nina G. Jablonski, The Pennsylvania State University, University Park, PA, and accepted by Editorial Board Member C. O. Lovejoy October 18, 2018 (received for review February 2, 2018) Skin pigmentation is under strong directional selection in northern Amhara or another group who themselves are substantially European and Asian populations. The indigenous KhoeSan popula- admixed with Near Eastern people has been proposed (13, 14). -
Bovine Genomic Sequencing Initiative Cattle-Izing the Human Genome
Bovine Genomic Sequencing Initiative Cattle-izing the Human Genome Richard Gibbs and George Weinstock, Baylor College of Medicine, Human Genome Sequencing Center; Steven Kappes, USDA-ARS, US Meat Animal Research Center; Lawrence Schook, University of Illinois; Loren Skow and James Womack, Texas A&M University Rationale and Objective. Completion of the human genome sequence provides the starting point for understanding the genetic complexity of man and how genetic variation contributes to diverse phenotypes and disease. It is clear that model organisms have played an invaluable role in capturing this information. It is also noted that additional species must be sequenced to resolve the genetic complexity of human evolution and to effectively extrapolate genetic information from comparative (veterinary) medicine to human medicine. The selection of animals representing evolutionary clades distinct from primates or rodents provides considerable power for analysis of the human genomic sequence. The domesticated, eutherian mammals that have co-evolved with man (cows, pigs, dogs and cats), represent taxa with diverse selected phenotypes and have in place strong scientific and veterinary medical communities to support and utilize genome sequence information. Thus, this “white paper” provides support for sequencing the bovine genome (6X coverage) to identify new genes and novel regulatory elements in humans, mice and rats. The bovine genome will serve as a reference non-primate, non- rodent, eutherian genome. The bovine genome is uniquely positioned for genomic sequencing because of the advanced stage of the reagents. An international bovine BAC map with a 15X coverage will be fingerprinted and all fingerprinted clones end-sequenced by February 2003. A minimum tilling path of BAC clones will be sequenced with a whole genome shotgun sequencing effort using random insert libraries. -
Identifying the Favored Mutation in a Positive Selective Sweep
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Methods Manuscript Author . Author manuscript; Manuscript Author available in PMC 2018 November 12. Published in final edited form as: Nat Methods. 2018 April ; 15(4): 279–282. doi:10.1038/nmeth.4606. Identifying the Favored Mutation in a Positive Selective Sweep Ali Akbari1, Joseph J. Vitti2,3, Arya Iranmehr1, Mehrdad Bakhtiari4, Pardis C. Sabeti2,3, Siavash Mirarab1, and Vineet Bafna4 1Department of Electrical & Computer Engineering, University of California San Diego, La Jolla, California, USA 2Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA 3Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA 4Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USA Abstract Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but mostly do not identify the specific mutation favored by selection. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to accurately pinpoint the favored mutation in a large region (~5 Mbp). iSAFE does not require any knowledge of demography, specific phenotype under selection, or functional annotations of mutations. Human genetic data have revealed a multitude of genomic regions believed to be evolving under positive selection. Methods for detecting regions under selection from genetic variations exploit a variety of genomic signatures. Allele frequency based methods analyze the distortion in site frequency spectrums; Linkage Disequilibrium (LD) based methods use extended homozygosity in haplotypes; other methods use differences in allele frequency between populations; and finally, composite methods combine multiple test scores to improve the resolution1–3. -
Selective Sweep Biotechnology Intelligence Unit Medical Intelligence Unit Molecular Biology Intelligence Unit
MOLECULAR BIOLOGY INTELLIGENCE UNIT MOLECULAR BIOLOGY INTELLIGENCE UNIT Landes Bioscience, a bioscience publisher, is making a transition to the internet as Dmitry Nurminsky Eurekah.com. NURMINSKY INTELLIGENCE UNITS Selective Sweep Biotechnology Intelligence Unit Medical Intelligence Unit Molecular Biology Intelligence Unit Neuroscience Intelligence Unit MBIU Tissue Engineering Intelligence Unit The chapters in this book, as well as the chapters of all of the five Intelligence Unit series, are available at our website. S elective Sweep elective ISBN 0-306-48235-5 9780306 482359 MOLECULAR BIOLOGY INTELLIGENCE UNIT MOLECULAR BIOLOGY INTELLIGENCE UNIT Landes Bioscience, a bioscience publisher, is making a transition to the internet as Dmitry Nurminsky Eurekah.com. NURMINSKY INTELLIGENCE UNITS Selective Sweep Biotechnology Intelligence Unit Medical Intelligence Unit Molecular Biology Intelligence Unit Neuroscience Intelligence Unit MBIU Tissue Engineering Intelligence Unit The chapters in this book, as well as the chapters of all of the five Intelligence Unit series, are available at our website. S elective Sweep elective ISBN 0-306-48235-5 9780306 482359 MOLECULAR BIOLOGY INTELLIGENCE UNIT Selective Sweep Dmitry Nurminsky, Ph.D. Department of Anatomy and Cellular Biology Tufts University School of Medicine Boston, Massachusetts, U.S.A. LANDES BIOSCIENCE / EUREKAH.COM KLUWER ACADEMIC / PLENUM PUBLISHERS GEORGETOWN, TEXAS NEW YORK, NEW YORK U.S.A. U.S.A. SELECTIVE SWEEP Molecular Biology Intelligence Unit Landes Bioscience / Eurekah.com Kluwer Academic / Plenum Publishers Copyright ©2005 Eurekah.com and Kluwer Academic / Plenum Publishers All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. -
Unlocking the Bovine Genome
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/24358846 Unlocking the Bovine Genome Article in BMC Genomics · May 2009 DOI: 10.1186/1471-2164-10-193 · Source: PubMed CITATIONS READS 48 142 6 authors, including: Ross L Tellam Danielle Lemay The Commonwealth Scientific and Industrial Research Organisation University of California, Davis 247 PUBLICATIONS 7,657 CITATIONS 148 PUBLICATIONS 3,445 CITATIONS SEE PROFILE SEE PROFILE Kim Carlyle Worley Baylor College of Medicine 411 PUBLICATIONS 86,467 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Vaccines against ticks and myiasis flies View project Milk and innate immunity View project All content following this page was uploaded by Danielle Lemay on 13 May 2014. The user has requested enhancement of the downloaded file. BMC Genomics BioMed Central Commentary Open Access Unlocking the bovine genome Ross L Tellam*1, Danielle G Lemay2, Curtis P Van Tassell3, Harris A Lewin4, Kim C Worley5 and Christine G Elsik6 Address: 1CSIRO Livestock Industries, 306 Carmody Rd, St Lucia 4067, QLD, Australia, 2Department of Food Science and Technology, University of California, One Shields Ave, Davis, CA 95616, USA, 3Bovine Functional Genomics laboratory, USDA/ARS, BARC-East, Beltsville, MD, 20705, USA, 4Institute for Genomic Biology and Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, 5Human Genome Sequencing Center, Department of Molecular