Genetics in Harry Potter's World: Lesson 2
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Biology Incomplete Dominance 5E Model Lesson
BIOLOGY INCOMPLETE DOMINANCE 5E MODEL LESSON Teacher: Expected Length of Lesson Topic: Unit: Heather Lesson: Incomplete Genetics Yarbrough 1 - 90 minute class Dominance SB3. Obtain, evaluate, and communicate information to analyze how biological traits are passed on to successive generations. Targeted Content Standards/ b. Use mathematical models to predict and explain patterns of inheritance. Element: (Clarification statement: Students should be able to use Punnett squares (Include the entire standard) (monohybrid and dihybrid crosses) and/or rules of probability, to analyze the following inheritance patterns: dominance, codominance, incomplete dominance.) L9-10RST2: Determine the central ideas or conclusions of a text; trace the text’s explanation or depiction of a complex process, phenomenon, or concept; provide an accurate summary of the text. L9-10RST7: Translate quantitative or technical information expressed in words in a text into visual form (e.g., a table or chart) and translate information expressed visually or mathematically (e.g., in an equation) into words. L9-10RST9: Compare and contrast findings presented in a text to those Targeted Literacy Skills or Standards: (include as many as your from other sources (including their own experiments), noting when the lesson incorporates) findings support or contradict previous explanations or accounts. L9-10WHST2: Write informative/explanatory texts, including the narration of historical events, scientific procedures/ experiments, or technical processes. d. Use precise language and domain-specific vocabulary to manage the complexity of the topic and convey a style appropriate to the discipline and context as well as to the expertise of likely readers. L9-10WHST4: Produce clear and coherent writing in which the development, organization, and style are appropriate to task, purpose, and audience. -
Interpreting Noncoding Genetic Variation in Complex Traits and Human Disease
REVIEW Interpreting noncoding genetic variation in complex traits and human disease Lucas D Ward1,2 & Manolis Kellis1,2 Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and molecular quantitative trait loci all provide complementary information about the function of noncoding sequences. These functional maps can help with prioritizing variants on risk haplotypes, filtering mutations encountered in the clinic and performing systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable data-set integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis and treatment. Understanding the genetic basis of disease can transform medicine ture of the human genome, and its systematic mapping by the HapMap by elucidating relevant biochemical pathways for drug targets and by Project9, allowed single-nucleotide polymorphisms (SNPs) to be used enabling personalized risk assessments1,2. With the evolution of technol- as markers for common haplotypes, which could be genotyped using ogies over the past century, geneticists are no longer limited to studying chip technology. The stage was set for a flood of unbiased, genome-wide Mendelian disorders and can tackle complex phenotypes. -
The Genetics of Complex Traits Programme Course 7.5 Credits Genetiska Mekanismer Bakom Komplexa Egenskaper 8MEA14 Valid From: 2019 Autumn Semester
DNR LIU-2019-00662 1(5) The genetics of complex traits Programme course 7.5 credits Genetiska mekanismer bakom komplexa egenskaper 8MEA14 Valid from: 2019 Autumn semester Determined by The Board for First and Second Cycle Programmes at the Faculty of Medicine and Health Sciences Date determined 2019-03-07 LINKÖPING UNIVERSITY FACULTY OF MEDICINE AND HEALTH SCIENCES LINKÖPING UNIVERSITY THE GENETICS OF COMPLEX TRAITS FACULTY OF MEDICINE AND HEALTH SCIENCES 2(5) Main field of study Medical Biology Course level Second cycle Advancement level A1X Course offered for Master's Programme in Experimental and Medical Biosciences Entry requirements Degree of Bachelor of Science, 180 ECTS in a major subject area such as medicine, biology, technology/natural sciences, odontology or veterinary medicine. 90 ECTS of courses included in the Bachelor degree should be in subjects such as biochemistry, cell biology, molecular biology, genetics, gene technology, microbiology, immunology, physiology, histology, anatomy or pathology. Documented skills in English corresponding to level 6/B. Intended learning outcomes The student will learn and understand the basis of quantitative genetic techniques, in particular how they pertain to the identification of genes underlying complex traits and diseases. Knowledge and understanding On completion of the course, the student shall be able to: Describe and understand statistical quantitative genetic techniques and how they apply to complex traits. Explain the statistical basis of quantitative traits. Explain and distinguish between linkage and linkage disequilibrium and their uses. Analyse the genetic architecture of different behavioral and disease-related traits. Analyse and understand the theory and steps required to identify the genetic components of a quantitative trait. -
Roadmap • Optimal Mutation Rate • Dominance and Its Implications
Roadmap • Optimal mutation rate • Dominance and its implications { Why is an allele dominant or recessive? { Overdominance (heterozygote advantage) { Underdominance (heterozygote inferiority) One minute responses • Q: I don't understand degrees of freedom! (About six of these....) • Q: Show the calculation of µ and ν Degrees of freedom revisited Thanks to Patrick Runkel: http://blog.minitab.com/blog/statistics-and-quality-data-analysis/ what-are-degrees-of-freedom-in-statistics A a Total A 20 a 10 Total 15 15 One more look at degrees of freedom Fictional data for sickle-cell hemoglobin (alleles A and S) in African-American adults Normal AA 400 Carrier AS 90 Affected SS 10 • Suppose I told you: { How many people I sampled { How many of each allele I found { How many AS carriers I found • Are there any possible surprises left in the data? (AA? SS?) • This is why there is only 1 df Mu and nu • µ (mu, forward mutation rate) { mutation rate per site is observed { rate per significant site in gene is: { rate per site x number of significant sites • ν (nu, back mutation rate) { mutation rate per site is observed { need the right nucleotide (1/3 chance) { rate per site x 1/3 Is mutation good or bad? • Most mutations have no fitness effect • Of those that do, most are bad • Most organisms expend significant energy trying to avoid mutations (DNA proofreading, etc) • Are organisms trying (and failing) to reach a mutation rate of zero? • Could there be selection in favor of a non-zero rate? Transposons as mutagens • Transposons are genetic elements that -
Dominance Hierarchy Arising from the Evolution of a Complex Small RNA Regulatory Network Eléonore Durand, Raphaël Méheust, Marion Soucaze, Pauline M
RESEARCH ◥ between S alleles (6). Selection is expected to RESEARCH ARTICLE favor genetic elements (“dominance modifiers”), which establish dominance-recessivity interac- tion rather than codominance, because individ- PLANT GENETICS uals with a codominant genotype can produce pollen rejected by more potential mates than would occur in a dominant-recessive system (7, 8). Dominance hierarchy arising from On the basis of modeling (8), the large non- recombining region composing the S locus (9–11) is a strong candidate region for hosting such ge- the evolution of a complex small netic elements. Until recently, the dominance modifiers as- RNA regulatory network sumed in models (7, 8) remained hypothetical. A particular small RNA (sRNA) has been identified Eléonore Durand,1,2* Raphaël Méheust,1* Marion Soucaze,1 Pauline M. Goubet,1† (12) within the S locus of dominant alleles in Sophie Gallina,1 Céline Poux,1 Isabelle Fobis-Loisy,2 Eline Guillon,2 Thierry Gaude,2 Brassica (called Smi). This sRNA acts as a trans- Alexis Sarazin,3 Martin Figeac,4 Elisa Prat,5 William Marande,5 Hélène Bergès,5 modifier of the gene controlling pollen specificity Xavier Vekemans,1 Sylvain Billiard,1 Vincent Castric1‡ via de novo methylation of the promoter of re- cessive alleles, which leads to transcriptional si- The prevention of fertilization through self-pollination (or pollination by a close relative) lencing of recessive alleles by dominant alleles in the Brassicaceae plant family is determined by the genotype of the plant at the (13, 14). However, the mechanism in the more self-incompatibility locus (S locus). The many alleles at this locus exhibit a dominance complex dominance-recessivity networks in spe- Downloaded from hierarchy that determines which of the two allelic specificities of a heterozygous genotype cies that have many levels in the dominance is expressed at the phenotypic level. -
An Introduction to Quantitative Genetics I Heather a Lawson Advanced Genetics Spring2018 Outline
An Introduction to Quantitative Genetics I Heather A Lawson Advanced Genetics Spring2018 Outline • What is Quantitative Genetics? • Genotypic Values and Genetic Effects • Heritability • Linkage Disequilibrium and Genome-Wide Association Quantitative Genetics • The theory of the statistical relationship between genotypic variation and phenotypic variation. 1. What is the cause of phenotypic variation in natural populations? 2. What is the genetic architecture and molecular basis of phenotypic variation in natural populations? • Genotype • The genetic constitution of an organism or cell; also refers to the specific set of alleles inherited at a locus • Phenotype • Any measureable characteristic of an individual, such as height, arm length, test score, hair color, disease status, migration of proteins or DNA in a gel, etc. Nature Versus Nurture • Is a phenotype the result of genes or the environment? • False dichotomy • If NATURE: my genes made me do it! • If NURTURE: my mother made me do it! • The features of an organisms are due to an interaction of the individual’s genotype and environment Genetic Architecture: “sum” of the genetic effects upon a phenotype, including additive,dominance and parent-of-origin effects of several genes, pleiotropy and epistasis Different genetic architectures Different effects on the phenotype Types of Traits • Monogenic traits (rare) • Discrete binary characters • Modified by genetic and environmental background • Polygenic traits (common) • Discrete (e.g. bristle number on flies) or continuous (human height) -
Lesson Plan Mendelian Inheritance
Dolan DNA Learning Center Mendelian Inheritance __________________________________________________________________________________________ Overview • Computer with internet access This 90 minute lesson (two class periods of 45 minutes) is an introduction to Mendel’s Laws of Inheritance for students in Lesson Structure grades 5 through 8. By studying inherited traits in humans such as tasting PTC paper and inherited traits in plants such as Pre-lab (45 minutes) – Day 1 maize, we can understand how traits are passed down through Teacher Prep generations. A discussion of dominant and recessive traits in humans will encourage students to further explore their • Become familiar with Lab Center inheritance as well as their family inheritance. http://www.dnalc.org/labcenter/mendeliangenetics/m endeliangenetics_d.html Learning Outcomes • Print and copy Background Reading from the Students will be able to: Student Lab Notebook on the Lab center. • discuss the contributions of Gregor Mendel and his • Print and copy Student Pre-lab Worksheets from experiments with the garden pea. the Student Lab Notebook on the Lab center. • review the structure of DNA and chromosomes. • Cut paper strips for Sentence Strips activity. • compare a dominant trait to a recessive trait. • Make sure computers with Internet access are • compare a homozygous trait to a heterozygous trait. available. • identify traits in themselves that are either dominant or recessive. Before class • use maize as a model organism to study Mendelian Students will receive the background reading to read for inheritance. homework the night before starting lab. They will write 2 to 3 • demonstrate Mendel’s Law of Dominance and Law questions they have about the background information. -
Genetic Analysis of Complex Traits in the Emerging Collaborative Cross
Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Research Genetic analysis of complex traits in the emerging Collaborative Cross David L. Aylor,1 William Valdar,1,13 Wendy Foulds-Mathes,1,13 Ryan J. Buus,1,13 Ricardo A. Verdugo,2,13 Ralph S. Baric,3,4 Martin T. Ferris,1 Jeff A. Frelinger,4 Mark Heise,1 Matt B. Frieman,4 Lisa E. Gralinski,4 Timothy A. Bell,1 John D. Didion,1 Kunjie Hua,1 Derrick L. Nehrenberg,1 Christine L. Powell,1 Jill Steigerwalt,5 Yuying Xie,1 Samir N.P. Kelada,6 Francis S. Collins,6 Ivana V. Yang,7 David A. Schwartz,7 Lisa A. Branstetter,8 Elissa J. Chesler,2 Darla R. Miller,1 Jason Spence,1 Eric Yi Liu,9 Leonard McMillan,9 Abhishek Sarkar,9 Jeremy Wang,9 Wei Wang,9 Qi Zhang,9 Karl W. Broman,10 Ron Korstanje,2 Caroline Durrant,11 Richard Mott,11 Fuad A. Iraqi,12 Daniel Pomp,1,14 David Threadgill,5,14 Fernando Pardo-Manuel de Villena,1,14 and Gary A. Churchill2,14 1Department of Genetics, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 2The Jackson Laboratory, Bar Harbor, Maine 04609, USA; 3Department of Epidemiology, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 4Department of Microbiology and Immunology, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina 27599, USA; 5Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA; 6Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland -
Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples
HIGHLIGHTED ARTICLE | INVESTIGATION Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples Daniel A. Skelly,* Narayanan Raghupathy,* Raymond F. Robledo,* Joel H. Graber,*,† and Elissa J. Chesler*,1 *The Jackson Laboratory, Bar Harbor, Maine 04609 and †MDI Biological Laboratory, Bar Harbor, Maine 04609 ORCID IDs: 0000-0002-2329-2216 (D.A.S.); 0000-0002-5642-5062 (E.J.C.) ABSTRACT Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. -
Genetics and Human Traits
Help Me Understand Genetics Genetics and Human Traits Reprinted from MedlinePlus Genetics U.S. National Library of Medicine National Institutes of Health Department of Health & Human Services Table of Contents 1 Are fingerprints determined by genetics? 1 2 Is eye color determined by genetics? 3 3 Is intelligence determined by genetics? 5 4 Is handedness determined by genetics? 7 5 Is the probability of having twins determined by genetics? 9 6 Is hair texture determined by genetics? 11 7 Is hair color determined by genetics? 13 8 Is height determined by genetics? 16 9 Are moles determined by genetics? 18 10 Are facial dimples determined by genetics? 20 11 Is athletic performance determined by genetics? 21 12 Is longevity determined by genetics? 23 13 Is temperament determined by genetics? 26 Reprinted from MedlinePlus Genetics (https://medlineplus.gov/genetics/) i Genetics and Human Traits 1 Are fingerprints determined by genetics? Each person’s fingerprints are unique, which is why they have long been used as a way to identify individuals. Surprisingly little is known about the factors that influence a person’s fingerprint patterns. Like many other complex traits, studies suggest that both genetic and environmental factors play a role. A person’s fingerprints are based on the patterns of skin ridges (called dermatoglyphs) on the pads of the fingers. These ridges are also present on the toes, the palms of the hands, and the soles of the feet. Although the basic whorl, arch, and loop patterns may be similar, the details of the patterns are specific to each individual. Dermatoglyphs develop before birth and remain the same throughout life. -
A Model and Test for Coordinated Polygenic Epistasis in Complex Traits
A model and test for coordinated polygenic epistasis in complex traits Brooke Shepparda,1, Nadav Rappoporta,b,1, Po-Ru Lohc,d, Stephan J. Sandersa, Noah Zaitlena,e,f,1,2, and Andy Dahle,f,g,1,2 aDepartment of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143; bBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143; cProgram in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142; dDivision of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115; eDepartment of Neurology, University of California Los Angeles, Los Angeles, CA 90095; fDepartment of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095; and gSection of Genetic Medicine, University of Chicago, Chicago, IL 60637 Edited by Andrew G. Clark, Cornell University, Ithaca, NY, and approved December 2, 2020 (received for review February 19, 2020) Interactions between genetic variants—epistasis—is pervasive in of epistasis. For example, Zuk et al. describe a limiting pathway model systems and can profoundly impact evolutionary adaption, pop- model of human disease that directly implies negative interactions ulation disease dynamics, genetic mapping, and precision medicine between SNPs contributing to different pathways (29). Also, the efforts. In this work, we develop a model for structured polygenic HSP90 community has discussed the possibility of a polygenic epistasis, called coordinated epistasis (CE), and prove that several re- version of chaperon function (30), which would induce coordi- cent theories of genetic architecture fall under the formal umbrella of nated interactions between HSP90 buffer SNPs and exonic mis- CE. -
Incomplete Dominance, Codominance, and ABO Blood Types
Incomplete Dominance, Codominance, and ABO Blood Types Review of Simple Mendelian Genetics Law of Segregation: each gene has two different alleles that are separated when gametes form One allele goes to one gamete and the other allele to a different gamete Law of Independent Assortment: genes for different traits are inherited independently from each other Review of Simple Mendelian Genetics Dominant vs. Recessive alleles for a gene The dominant allele masks the recessive one, so you see the dominant trait (for RR or Rr) The only way to see a recessive trait is to have two recessive alleles (rr) Dominant allele is represented as a capital letter (R) Recessive allele is represented as a lowercase letter (r) Unfortunately, it’s not all that easy… Incomplete Dominance Sometimes neither allele is fully dominant over the other Incomplete Dominance: neither allele is dominant but combine and display a new trait that is a mixing of the two alleles Incomplete Dominance RR R’R’ RR’ RR’ RR RR’ R’R’ Incomplete Dominance In incomplete dominance, the two alleles are represented as Two capital letters When these two alleles come together, they portray a mixing of the two phenotypes! Codominance Other times both alleles are fully dominant Codominance: both alleles of a gene are dominant and the heterozygous phenotype has both traits equally expressed Codominance x BB WW BW Codominance In codominance the two alleles are represented as Two capital letters: Use the first letter of one trait (B for Brown) and the first letter of the other trait (W for White) When they come together as a heterozygote, both traits show as the phenotype Let’s Stop and Think… Let’s say there are two alleles for the hair color trait- red and blue What would be the resulting phenotype of a heterozygous pair if the alleles showed incomplete dominance? A.