Qualifying Exam: Joseph Vitti Organismic & Evolutionary Biology

Transcription

1 Qualifying Exam: Joseph Vitti Organismic & Evolutionary Biology Time Friday March 28, :00 AM 1:00 PM Location Northwest Labs 52 Cambridge St Room 425 Cambridge, MA Committee Pardis Sabeti (advisor) Hopi Hoekstra Maryellen Ruvolo John Wakeley Contact Cell: Course syllabi follow for: I. Statistical Programming for Biology II. Population Genetics of Humans III. Natural Selection: Theoretical and Empirical Approaches These syllabi reflect the past three years transitioning to science from my undergraduate background in the humanities. Taken together, they represent a condensed form of that knowledge I believe would be most critical for a newcomer entering the field of human evolutionary genomics. Each course builds on the previous course, and each is a synthesis of courses I have taken and papers I have read. In particular, Statistical Programming for Biology draws inspiration from two extension school courses I took (STAT E-102, Fundamentals of Biostatistics; BIOS E-45, Introduction to Genomics) while applying to OEB, as well as two MIT courses (6.00x: Introduction to Computer Science and Programming, which I took online, and 6.878: Computational Biology, which I took this Fall with Manolis Kellis). Population Genetics of Humans combines OEB 242 with content from HEB 1463: Molecular Evolution of the Primates, as well as several additional papers I felt were relevant. Natural Selection: Theoretical and Empirical Approaches draws on my undergraduate background in philosophy of biology, together with last year s philosophically focused Evolutionary Genomics seminar (OEB 253r) as well as literature that I reviewed in a paper for Annual Reviews Genetics published this past November. J. Vitti Qualifying Exam Course Syllabi 1

2 Statistical Programming for Biology 1 The age of big data has changed the way that we generate and examine biological hypotheses. In this course, we develop the necessary foundations in math and computer science to pursue research in computational biology. We assume no background in statistics or programming and use an integrative approach, building up to analysis of real genomic datasets. Students will learn to program in Python, with a focus on related tools for analysis (NumPy, SciPy etc.). Prerequisites: At least one course in genome biology recommended. Assigned texts: Guttag, J. V. (2013). Introduction to Computation and Programming Using Python. MIT Press. Rosner, B. (2011). Fundamentals of Biostatistics. Boston: Brooks/Cole, Cengage Learning. Jones, N. C., & Pevzner, P. (2004). An Introduction to Bioinformatics Algorithms. Cambridge, MA: MIT Press. Recommended: Gibson, G., & Muse, S. V. (2009). A Primer of Genome Science. Sunderland, MA.: Sinauer. Ross, S. (2010). A First Course in Probability. Upper Saddle River, NJ: Pearson Prentice Hall. Grading: Homework (30%), take-home midterm (30%), take-home final (30%), section participation (10%). Students may submit an original research proposal for extra credit. Module Schedule Meeting Topic Assignments Introduction: biology and big data 0 Week 1 Descriptive statistics Rosner ch. 2, (Gibson pp ) Installing Python, genomics primer Jones ch. 3 1 Week 2 Week 3 Getting started with Python: basic math Guttag ch. 2 Introduction to algorithms, functions, and control flow Writing simple programs Data structures I: ints, floats, strings, lists, list comprehension, and iteration Guttag ch. 3 HW1: Python fundamentals Guttag ch. 4 Data structures II: dictionaries and tuples Guttag ch 5 2 Week 4 Practice with data manipulation HW2: Writing programs Data classes and inheritance Guttag ch. 8 Choosing data structures and file input/output Guttag ch. 6-7 Debugging HW3: Manipulating data Introduction to Probability Rosner ch Week 5 Conditional Probability and Bayes Theorem Classification and Clustering Rosner ch (Ross, ch. 3) HW4: Probabilistic programming Jones, ch (Gibson pp ) 1 To facilitate the oral exam, I provide a condensed version of this syllabus (essentially a topic list ) at the end of this document. J. Vitti Qualifying Exam Course Syllabi 2

3 Random Variables, Probability Distributions, Bernoulli Trials Rosner ch (Ross, ch. 4) Week 6 Discrete Distributions: Binomial, Poisson, Geometric Rosner ch (Ross, ch 4) 4 Plotting distributions with matplotlib Continuous Probability Distributions I: Uniform, Normal HW5: Analysing data distributions Rosner ch (Ross, ch 5) Week 7 Continuous Probability Distributions II: Exponential, Chi-Squared Rosner ch (Ross, ch. 5) Hypothesis testing HW6: More practice with data distributions Algorithmic complexity Guttag ch. 9 5 Week 8 Hash functions and memoization Guttag ch. 10 Applications: local alignment (BLAST) Take home midterm: modules 1-4 (Gibson pp ) Maximum likelihood estimation (MLE) Rosner ch Week 9 Expectation maximization (EM) Rosner ch Applications: motif discovery HW7: Improving algorithmic complexity (Gibson, pp ) Gibbs sampling Jones ch 12.2 Week 10 Monte Carlo Methods Guttag ch Week 11 Applications: phylogenetics HW8: Parameter estimation (Gibson, pp ) Recursion Guttag ch 18 Dynamic Programming Jones ch 6 Applications: global alignment Hidden Markov Models I: scoring and the Viterbi algorithm HW9: Recursive programming Jones ch 11 Week 12 Hidden Markov Models II: Forward and Backward algorithms, Baum-Welch (Gibson pp , ) Applications: genome annotation HW10: DP and HMMs Reading period Take home final and optional extra credit research proposal due on last day of reading period. J. Vitti Qualifying Exam Course Syllabi 3

4 Population Genetics of Humans The course combines foundations in the field of population genetics with frontiers in the study of human microevolution. We will examine mathematical frameworks for studying evolution and read current papers applying these frameworks to human populations. Prerequisites: At least one course in evolutionary biology. Statistical Programming for Biology recommended. Assigned texts: Hartl, D. L., & Clark, A. G. (2007). Principles of Population Genetics. Sunderland, Mass.: Sinauer Associates. Graur, D., & Li, W.-H. (2000). Fundamentals of Molecular Evolution. Sunderland, Mass.: Sinauer Associates. Plus papers available online (see reading list at end of document) Grading: weekly homework assignments (25%), midterm (25%), presentation (25%), take-home final (25%) Week Topic Foundations Frontiers Introduction: From the The 1000 Genomes Project Consortium, 1 modern synthesis to 1000 Hartl ch. 1, Graur ch Genomes 2 Variation: Hardy- Weinberg, LD Hartl ch. 2 Hartl Wigginton et al., Drift: Wright-Fisher, Intro to Coalescence Mutation: Neutral Theory, Infinite Alleles, Infinite Sites Models Hartl ch. 3 Li & Durbin, 2011 Hartl ch. 4 Conrad et al., Selection: Modeling Fitness and Equilibria Hartl ch 5 Hartl 10.7 Fu & Akey, Midterm: Weeks Population structure: inbreeding, F statistics Hartl ch Hartl 10.4, Rosenberg et al Migration and Admixture Hartl ch 6.5 Reich et al., Molecular clocks and rates of evolution Graur ch. 4, Hartl ch. 7 Kumar, Mechanisms of mutation Graur ch. 6-7 Kondrashov, Trait mapping and functional genomics Hartl ch Altshuler et al., Presentations: students will read a current paper and prepare a presentation (powerpoint optional) explaining population genetics methods and findings. Take-home final: due at the end of reading period. J. Vitti Qualifying Exam Course Syllabi 4

5 Reading list: Altshuler, D., Daly, M. J., & Lander, E. S. (2008). Genetic mapping in human disease. Science (New York, N.Y.), 322(5903), doi: /science Conrad, D. F., Keebler, J. E. M., DePristo, M. A., Lindsay, S. J., Zhang, Y., Casals, F., 1000 Genomes Project. (2011). Variation in genome-wide mutation rates within and between human families. Nature Genetics, 43(7), doi: /ng.862 Fu, W., & Akey, J. M. (2013). Selection and Adaptation in the Human Genome. Annual Review of Genomics and Human Genetics, 14(1), doi: /annurev-genom Kondrashov, F. A. (2012). Gene duplication as a mechanism of genomic adaptation to a changing environment. Proceedings. Biological Sciences / The Royal Society, 279(1749), doi: /rspb Kumar, S. (2005). Molecular clocks: four decades of evolution. Nature Reviews Genetics, 6(8), doi: /nrg1659 Li, H., & Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), doi: /nature10231 Reich, D., Thangaraj, K., Patterson, N., Price, A.L., and Singh, L. (2009). Reconstructing Indian population history. Nature 461, Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002). Genetic Structure of Human Populations. Science, 298(5602), doi: /science The 1000 Genomes Project Consortium. (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), doi: /nature11632 Wigginton, J. E., Cutler, D. J., & Abecasis, G. R. (2005). A note on exact tests of Hardy-Weinberg equilibrium. American Journal of Human Genetics, 76(5), doi: / J. Vitti Qualifying Exam Course Syllabi 5

6 Natural Selection: Theoretical and Empirical Approaches It has been argued that natural selection is the most powerful natural theory because it bears enormous explanatory potential while only invoking a few simple assumptions. In this course, we will examine this theory in depth. Starting from historical and theoretical accounts, we will scrutinize what is meant by natural selection and discuss the various ways that this process manifests itself in nature. Then, following the molecular turn in biology, we will explore the ways that genome science is revolutionizing the study of selection. In particular, we will survey methods for analyzing patterns of genomic diversity in order to identify variants under positive selection. Prerequisites: Statistical Programming for Biology, Population Genetics of Humans recommended Required texts: Darwin, C. (1859). On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. London: John Murray. 5 th Edition. Dawkins, R. (2006). The Selfish Gene. Oxford; New York: Oxford University Press. Godfrey-Smith, P. (2009). Darwinian Populations and Natural Selection. Oxford; New York: Oxford University Press. Plus papers available online (see reading list at end of document) Grading: 5-10 pg. paper on units 1-4 (20%), problem sets due at the ends of units 6, 8 and 10 (30%), final exam (25%), presentation (25%) Week Topic Reading 1 Historical foundations Darwin, ch. 3, 4, 14 Theoretical 2 Replicators and the gene s eye view Dawkins, ch Units of selection Lewontin, Selection at different levels Godfrey-Smith, ch Gene-based methods: K a /K s, McDonald-Kreitman Hurst, 2002 McDonald & Kreitman, Rate-based methods: Hudson- Kreitman-Aguade, lineage-specific acceleration Hudson, Kreitman & Aguade, 1987 Pollard et al., Differentiation methods: F st and derivatives 8 Frequency spectrum methods: Tajima s D, Fay and Wu s H Holsinger & Weir, 2009 Tajima, 1989 Fu & Li, 1993 Fay & Wu, 2000 Empirical 9 Linkage disequilibrium-based methods: EHH and derivatives, Identity-by-descent Sabeti et al., 2002 Voight et al., 2006 Han & Abney, Composite methods 11 Alternative modes of selection 12 Applying tests for selection Kim & Stephan, 2002 Grossman et al., 2010 Barrett & Schluter, 2008 Charlesworth 2006 Hancock et al Presentation: students should prepare a chalk talk (5-10 mins) on a results paper of their choice (e.g. Colosimo et al. 2005; Enard et al. 2002) J. Vitti Qualifying Exam Course Syllabi 6

7 Reading list: Barrett RDH, Schluter D Adaptation from standing genetic variation. Trends Ecol. Evol.23(1):38 44 Charlesworth D Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet. 2(4):e64 Colosimo PF, Hosemann KE, Balabhadra S, Villarreal G Jr, Dickson M, et al Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles. Science 307(5717): Enard W, Przeworski M, Fisher SE, Lai CSL, Wiebe V, et al Molecular evolution of FOXP2, a gene involved in speech and language. Nature 418(6900): Fay JC, Wu CI Hitchhiking under positive Darwinian selection. Genetics 155(3): Fu YX, Li WH Statistical tests of neutrality of mutations. Genetics 133(3): Grossman SR, Shylakhter I, Karlsson EK, Byrne EH, Morales S, et al A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327(5967): HanL,AbneyM.2012.Using identity by descent estimation with dense genotype data to detect positive selection. Eur. J. Hum. Genet. 21(2): Hancock AM, Witonsky DB, Ehler E, Alkorta-Aranburu G, Beall C, et al Colloquium paper: human adaptations to diet, subsistence, and ecoregion are due to subtle shifts in allele frequency. Proc. Natl. Acad. Sci. USA 107(Suppl. 2): HermissonJ,PenningsPS.2005.Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics 169(4): Holsinger, K.E., and Weir, B.S. (2009). Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat. Rev. Genet. 10, Hudson RR, Kreitman M, Aguade M A test of neutral molecular evolution based on nucleotide data. Genetics 116(1): Hurst LD The Ka/Ks ratio: diagnosing the form of sequence evolution. Trends Genet. 18(9):486 Kim, Y., and Stephan, W. (2002). Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics 160, Lewontin, R. C. (1970). The Units of Selection. Annual Review of Ecology and Systematics, 1(1), doi: /annurev.es McDonald JH, Kreitman M Adaptive protein evolution at the Adh locus in Drosophila. Nature 351(6328): Pollard KS, Salama SR, King B, Kern AD, Dreszer T, et al Forces shaping the fastest evolving regions in the human genome. PLoS Genet. 2(10):e168 Sabeti PC, Reich DE, Higgins JM, Levine HZP, Richter DJ, et al Detecting recent positive selection in the human genome from haplotype structure. Nature 419(6909): Tajima F Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123(3): Voight BF, Kudaravalli S, Wen X, Pritchard JK A map of recent positive selection in the human genome. PLoS Biol. 4(3):e72 J. Vitti Qualifying Exam Course Syllabi 7

8 Statistical Programming for Biology (condensed) Module 1 Topic Writing Python programs: algorithms, objects, variables, functions, control flow 2 Manipulating data: abstract data types (ADTs), classes, inheritance, input/output, debugging 3 Fundamentals of probability: combinatorics, independence, conditional probability, Bayes Theorem Applications: classification, phylogenies 4 Probability distributions: random variables, probability density/mass functions (PDF/PMFs), cumulative density functions (CDFs), expected value, variance Bernoulli, Binomial, Poisson, Geometric Uniform, Normal, Exponential, χ 2 5 Algorithmic complexity: O-notation, hash functions, memoization, plotting data Applications: local alignment 6 Parameter estimation: maximum likelihood estimation (MLE), expectation maximization (EM), Gibbs sampling, Monte Carlo methods Applications: motif discovery, phylogenetics 7 More sophisticated methods: recursion, dynamic programming, (DP) Hidden Markov Models (HMMs) Applications: global alignment, genome annotation J. Vitti Qualifying Exam Course Syllabi 8