Evolutionary and Ecological Genetics

Course Guide 2020/2021

Course Overview 3

Learning outcomes from this course 3 Learning outcomes will be achieved through: 4

Staff 4 Course Organiser 4 Course Secretary 4 Lecturers 4

Teaching Period 5

Course Background 5

Possible Honours Courses 5

Workload Expectation 5

Assessment and Deadlines 5

Continuous assessment and essay [50%] 5

Examination [50%] 6

Assessment Policy 6

Adjustment schedules 6

LEARN discussion boards and open ‘Collaborate’ room 7

Representation, Feedback and Appeals 7

EEG timetable and checklist 2020-2021 8

Reading Material 12

Pre-recorded Lecture Modules 13 Introduction to the course (DJO) 13

Population genetics and molecular evolution (DJO) 13 Module 1: Mutations, Diversity and Drift 14 Module 2: Natural Selection 16

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Module 3: Balancing Selection and Population structure 18 Module 4: Sex and Recombination 20

Genetics of Complex Traits (Craig Walling) 21 Module 5: Introduction to the genetics of complex traits and heritability 21 Module 6. Estimation of heritability and an introduction to selection on complex traits 23 Module 7. The nature of complex trait variation 24 Module 8: Molecular Phylogenetics (Paul Sharp) 25 8.1 Introduction to phylogenetics 25 Module 9: Speciation (Simon Martin) 26 Module 10: Genetic parasites (Darren Obbard) 27

Computer Practicals and Quizzes 28

Problem Tutorial Sessions 28

In-course Essay 29 Part 1: What makes a good essay? 29 Part 2: Engagement with the starter-references [3%] 29 Part 3: Writing the Essay [27%] 30 Essay Titles 31

Appendix – Practical advice for essay writing 36

Further Information 39

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Course Overview This course will introduce you to the processes that underlie evolutionary change in natural populations. It is intended to give an integrated view, showing how theoretical approaches can be applied to investigate natural evolutionary processes. We will deal with subjects ranging from the evolutionary fate of molecular sequences to the genetic consequences of interactions between species, and from variation at single genes to speciation.

The course starts with population genetics and molecular evolution (Weeks 1-4) emphasising its importance in understanding the process of biological evolution. This is followed by an introduction to the genetics of quantitative characters (Weeks 5-7). The course ends with lecture modules on molecular phylogenetics, mechanisms of speciation, and genetic parasites (Weeks 8-10).

The first three sections of the course are associated with computer-based practical classes (which are assessed through digital quizzes online) and live digital “problem” tutorials. These sessions are designed to assist with understanding of key concepts, to give practice in problem solving and to develop competence in quantitative analysis. An essay and associated tutorials provide the opportunity for assimilating and synthesising information from the primary literature and discussing its interpretation. Learning outcomes from this course

University of Edinburgh courses follow the Scottish Credit and Qualification Framework (SCQF), which has 12 levels for each of 6 specified ‘characteristics’.

1) Knowledge and Understanding Know that Biological Evolution is the result of interaction between processes such as mutation, genetic drift, natural selection, migration, and recombination, and understand in qualitative terms how these processes occur and interact

2) Practice: Applied Knowledge, Skills and Understanding Be able to apply simple equations and statistical methods to quantify the processes that underlie evolution, and to test for departures from neutral or null expectations

3) Generic Cognitive Skills Be able to integrate distinct views of the evolutionary process, and to assess and evaluate the assumptions behind models and model-based analyses

4) Communication, ICT, and Numeracy Use graphs to display and interpret genetic and phenotypic data in an evolutionary or ecological context, and use simple statistical methods such as the chi-squared test, regression and ANOVA to analyse them.

5) Autonomy, Accountability, and Working with others Be able to work in small groups to propose and evaluate standard methods for analysing genetic and phenotypic data in an evolutionary or ecological context, and present your solutions and reasoning to peers.

EEG3 is a level 9-10 course (Ordinary to Honours degree level).

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Learning outcomes will be achieved through:

Pre-recorded lecture modules and live digital discussions  the basic concepts and quantitative foundation of population genetics  the importance of population genetics in population management  how molecular evolution links evolutionary biology and population genetics  what can be learned about natural selection through molecular population genetics  the fundamentals of quantitative genetics Live digital computer practical classes  to study the use of computer simulations to understand the behaviour of genes in populations  to become familiar with phylogenetic analysis of molecular sequence data  to become familiar with experimental and analytical techniques in current use in evolutionary genetics  to appreciate the importance of statistical techniques in the analysis and interpretation of biological and genetic data Live digital problem sessions and discussion  to develop skills in solving quantitative problems in evolutionary genetics Essay writing  to develop skills in understanding, assimilating and presenting scientific arguments in a critical and original fashion

Staff Course Organiser Dr. Darren Obbard (Institute of Evolutionary Biology) Ashworth 2, room 2.09, [email protected]

Course Secretary Janna James (BTO) for any questions regarding times, dates, deadlines, rooms or paperwork, please email [email protected]

Lecturers Dr. Darren Obbard (IEB) ([email protected]) Dr. Craig Walling (IEB) ([email protected] ) Prof. Paul Sharp (IEB) ([email protected]) Dr. Simon Martin (IEB) ([email protected])

All teaching staff are actively involved in research in evolutionary genetics. Our areas of research are described below (use the pdf version of this document to access the hyperlinks!):

Darren Obbard link Genome evolution (invertebrates, RNAi and viruses) Craig Walling link Quantitative genetics and life history evolution (red deer, Drosophila) Simon Martin link Population genomics and speciation in Butterflies Paul Sharp link Molecular evolution (viruses, malaria parasites)

Course email address (for all general enquiries) - [email protected]

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Teaching Period Semester 1

Course Background Preferred background: Evolution in Action 2

Possible Honours Courses The course is appropriate for students intending to take any one of a wide range of Honours courses, including Evolutionary Biology, Zoology, Ecology, Genetics, Molecular Genetics, and Plant Sciences.

Workload Expectation This is a 20 credit course, which is intended to equate to 200 hours of your time. We think a reasonable breakdown of those 200 hours could be:

Component Approximate number Total time Watching pre-recorded lecture modules 10 x 2h 20h Answering lecture module practice quizzes 10 x 3h 30h Attending live Q&A sessions 20 x 1h 20h Computer practicals & assessed quizzes 5 x 3h 15h Problem sessions & practice quizzes 5 x 3h 15h Course essay preparation 30h Writing the course essay 20h Reading, Revision, and Exam practice 50h Total 200h

Assessment and Deadlines Continuous assessment contributes 50% of the total marks for the course. The open-book examination, which will be taken within a 24-hour window at the end of the semester, contributes the other 50%.

Continuous assessment and essay [50%] The continuous assessment marks comprise 27% for the in-course essay, 3% for engagement with the essay reading, and 20% for four short online quizzes associated with computer practicals (5% each). Questions for the assessed practicals are directly related to the practical questions, and should only take a few minutes.

Note that the quiz for practical 1 does not count towards your overall course mark, but it is still important, as this is an opportunity to practice. Also remember that the more challenging lecture-module and problem-session quizzes are not assessed – those are just to help you learn

 The course essay must be submitted in electronic form through turnitin, using your exam number for identification.  Deadlines for submission are provided via LEARN.  Marked and annotated essays will be returned electronically  Any hard-copy submissions must have a barcode attached.

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Examination [50%] The examination will be done digitally, open-book, in a 24-hour window toward the end of semester 1. This means that there will be little time for revision between the end of the course and the examination. You are therefore encouraged to learn the course material as you go along.

The examination paper will contain 3 sections.

 Section 1 will be done electronically online. It will contain two equally weighted quantitative / calculation ‘problem’ questions, both of which must be answered. [20%]

 Section 2 will be done offline. It will contain two short answer questions, and one of these questions must be answered. [15%]

 Section 3 will be done offline. It will contain two short answer questions, and one of these questions must be answered. [15%]

Material will be distributed between sections 2 and 3 to ensure that questions covering both population-genetics (molecular evolution) and quantitative genetics must be answered. Note that this is a new structure from 2020 onward, and past papers from before 2019 have a different structure (and longer questions). External examiner

The External Examiner for EEG is Prof Daniela Delneri. Her role is to scrutinise the examination papers and a sample of your examination scripts. The External Examiner has the important role of ensuring that all of our courses meet satisfactory standards, equivalent to those in other universities of similar status to this university.

Assessment Policy

Penalties for late submission of course work It is a requirement of this course that you submit the required in-course assessed components. If you fail to submit a piece of in-course assessed work by the stated deadline you will receive a marks penalty unless you can provide a legitimate, validated reason (medical certificate or similar evidence). Remember that extensions cannot extend past the date at which marks are returned or answers are discussed. In these cases, special circumstances are likely to apply.

This course will apply the standard conditions for submission of course work, in accordance with common practice across the University. For further information on these conditions, please refer to the essential guide. Information on applying for extensions to course work deadlines can also be found in the essential guide. If you have any questions regarding the submission of work please don’t hesitate to contact the course secretary.

Adjustment schedules If you have any adjustments that you wish to be implemented in EEG (and haven’t been), please bring them to the attention of the relevant lecturer – preferably in

Page | 6 advance. This can be done in person, or by email. Alternatively, please contact your personal tutor as early as possible.

LEARN discussion boards and open ‘Collaborate’ room

Many of the lecturers on this course will make use of the LEARN discussion boards. Relevant forums have been created already – please feel free to post your thoughts and questions (anonymously, if you prefer). Some lecturers may choose to respond to direct email questions by posting the answers to the discussion board so that everyone can benefit. There is also a permanently open collaborate room where you can meet with other students on the course, and where you can arrange to meet lecturers. Such online discussion is likely to be particularly important this year, please make use of it!

Representation, Feedback and Appeals

Honours programme representatives are arranged by the honours programme, and are nominated each year to act as the primary route of communication with the course organisers and lecturers. Urgent matters can be raised directly with the lecturer involved, and we welcome any and all feedback, in any form (including anonymous feedback) – this is used to improve the course each year. Students may contact the course organiser by e-mail to [email protected].

Course notices are posted on LEARN.

You can also give formal feedback on the course through the course questionnaire. This can be filled in from around week 10 onwards. We strive to improve this course each year, and over the past 3 years it has received some very good feedback (including teaching award nominations). We want to continue this improvement, and your suggestions are an essential part of this.

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EEG timetable and checklist 2020-2021

At a glance: Week Number 1 2 3 4 5 6 7 8 9 10 11 12 Stream Week starting 21-Sep 28-Sep 05-Oct 12-Oct 19-Oct 26-Oct 02-Nov 09-Nov 16-Nov 23-Nov 30-Nov 07-Dec Pre-Recorded: Course Introduction DJO Tue 2pm Live digital: Welcome to EEG (collaborate) Course DJO Live digital: Revision Q&A (collaborate) Fri 2pm Tue 2pm Pre-recorded: Lecture modules and quizzes Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Module 7 Module 8 Module 9 Module 10 (Quiz deadline start of the following Week) (DJO) (DJO) (DJO) (DJO) (CW) (CW) (CW) (PS) (SM) (DJO) Live digital: Lecture module Q&A Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm Tue 2pm

Lecture Modules (collaborate) (DJO 1) (DJO 2) (DJO 3) (DJO 4) (CW 5) (CW 6) (CW 7) (PS 8) (SM 9) (DJO 10) Pre-recorded: Introduction to Practical 1 (DJO) 2 (DJO) 3 (CW) 4 (CW) 5 (PS) Live digital: Practical with Demonstrators Tue 3pm Tue 3pm Tue 3pm Tue 3pm Tue 3pm (own time & Collaborate) 1 (DJO) 2 (DJO) 3 (CW) 4 (CW) 4 (PS) Take Practical quiz [20%] Prac 1: Prac 2: Prac 3: Prac 4: Prac 5:

Practicals Computer Computer (Deadline start of the following Week) χ2 Drift Regression Heritability Phylo Do Problem Session questions and take quiz Probs 1 Probs 2 Probs 3 Probs 4 (Deadline Friday of the following Week) (DJO) (DJO) (CW) (PS) Live digital: Problem Session Q&A Fri 2pm Fri 2pm Fri 2pm Fri 2pm

Sessions (Collaborate; potentially repeated back to Problem Problem (DJO 1) (DJO 2) (CW 3) (PS 4) back) Pre-recorded: Introduction to essays DJO Practice Marking an Essay Live digital: How to mark an essay Fri 2pm (collaborate) (DJO) Choose Essay title From 5pm Choose Paper to summarise Summarise Paper Paper Questions/Answers [3%]

Course Essay Course Fri 2pm Live digital: Online Essay Q&A (collaborate) (Marker) Hand in Essay [27%] Fri 5pm

Note that all the live sessions will be recorded (please remind the lecturer) and you can submit questions in advance!

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Checklist: You can cross the activities off this list when you have done them

Activity

Watch Pre-recorded course introduction (DJO) Tue 2pm: Live Digital Welcome to EEG (DJO) [Use Collaborate Link; Teams or Zoom backup] Week 1 Watch Pre-Recorded Lecture Module 1 (DJO) 22 Sept Take the quiz for Lecture Module 1 before the start of week 2 Read the three example essays, and use the survey to submit your marks for them before Friday of Week 2

Tue 2pm: Live Digital Q&A for Lecture Module 1 (DJO) [Use Collaborate Link; Teams or Zoom backup] Watch Pre-Recorded Introduction to Practical 1: Chi squared, and start the practical if you wish Tue 3pm: Live Digital Demonstrator session for Practical 1 (DJO & Demonstrators) Week 2 Take the quiz for Practical 1 before the start week 3 29 Sept & 2 Oct Fri 2pm: Live Digital Q&A on how to write and mark an essay (DJO) [Use Collaborate Link; Teams or Zoom backup] Watch Pre-Recorded Lecture Module 2 (DJO) Take the quiz for Lecture Module 2 before the start of week 3 Choose your essay title (From 5pm, Friday)

Tue 2pm: Live Digital Q&A for Lecture Module 2 (DJO) Watch Pre-Recorded Introduction to Practical 2: Drift, and start the practical if you wish Tue 3pm: Live Digital Demonstrator session for Practical 2 (DJO & Demonstrators) Week 3 Take the quiz for Practical 2 before the start of week 4 [5%] 6 Oct Watch Pre-Recorded Lecture Module 3 (DJO) Take the quiz for Lecture Module 3 before the start week 4 Choose which essay starter-reference you’re going to summarise, and read it Tue 2pm: Live Digital Q&A for Lecture Module 3 (DJO) Fri 2pm: Live Digital Problem Session 1 (DJO) Week 4 Watch Pre-Recorded Lecture Module 4 (DJO) 13 & 16 Oct Take the quiz for Lecture Module 4 before the start of week 5 Write your essay starter-reference and upload it

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Tue 2pm: Live Digital Q&A for Lecture Module 4 (DJO) Watch Pre-Recorded Lecture Module 5 (DJO) Week 5 Take the quiz for Lecture Module 5 before the start of week 6 20 Oct Read essay paper-summaries, ask and answer peer questions Do the problems for Session 2, and enter your answers through the quiz before Friday of week 6

Tue 2pm: Live Digital Q&A for Lecture Module 5 (CW) Fri 2pm: Live Digital Problem Session 2 (DJO) Week 6 Watch Pre-Recorded Lecture Module 6 (CW) 27 & 30 Oct Take the quiz for Lecture Module 6 before the start of week 7 Read essay paper-summaries, ask and answer peer questions [3%]

Tue 2pm: Live Digital Q&A for Lecture Module 6 (CW) Watch Pre-Recorded Introduction to Practical 3: Regression, and start the practical if you wish Tue 3pm: Live Digital Demonstrator session for Practical 3 (CW & Demonstrators) Week 7 Take the quiz for Practical 3 before the start of week 8 [5%] 3 & 6 Nov Watch Pre-Recorded Lecture Module 7 (CW) Fri 2pm: Live Digital Q&A for your essay with the essay marker Take the quiz for Lecture Module 7 before the start of week 8 Do the problems for Session 3, and enter your answers through the quiz before Friday of week 8

Tue 2pm: Live Digital Q&A for Lecture Module 7 (CW) Watch Pre-Recorded Introduction to Practical 4: Heritability, and start the practical if you wish Tue 3pm: Live Digital Demonstrator session for Practical 4 (CW & Demonstrators) Week 8 Take the quiz for Practical 4 before the start of week 9 [5%] 10 & 1 Nov Fri 2pm: Live Digital Problem Session 3 (CW) Watch Pre-Recorded Lecture Module 8 (PS) Take the quiz for Lecture Module 8 before the start of week 9

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Tue 2pm: Live Digital Q&A for Lecture Module 8 (PS) Watch Pre-Recorded Introduction to Practical 5: Phylogenetics, and start the practical if you wish Tue 3pm: Live Digital Demonstrator session for Practical 5 (PS & Demonstrators) Week 9 Take the quiz for Practical 5 before the start of week 10 [5%] 17 Nov Watch Pre-Recorded Lecture Module 9 (SM) Take the quiz for Lecture Module 9 before the start of week 10 Do the problems for Session 4, and enter your answers through the quiz before Friday of week 10

Tue 2pm: Live Digital Q&A for Lecture Module 9 (SM) Week 10 Fri 2pm: Live Digital Problem Session 4 (PS) 24 & 27 Nov Watch Pre-Recorded Lecture Module 10 (DJO) Take the Quiz for Lecture Module 10 before the start of week 11

Tue 2pm: Live Digital Q&A for Lecture Module 10 (DJO) Week 11 Fri 2pm: Optional Live Digital revision session via Collaborate (DJO) 1 & 4 Dec Fri 5pm: Essay hand-in deadline [27%]

Week 12 Tue 2pm: Optional Live Digital revision session via Collaborate (DJO) 8 Dec

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Reading Material

You are not expected to buy any text books. We do not follow any particular book closely, and all should be available in the library or downloadable for free. Most lecturers provide suggested additional reading in the form of papers from scientific literature, and these can be accessed online directly or through the University Library.

We also provide a free PDF book by Prof. Graham Coop (UC Davis) that can be downloaded from the LEARN. However, we do not follow the text closely; consider it a parallel source of information, not just a re-statement of the lectures. In addition, some of the material it covers is beyond this course – do not feel you need to read the sections that are not recommended.

 Coop, G (2020) “Population and Quantitative genetics” (available on LEARN)

If you need a more general introduction, then a suitable introductory text is:

 Ridley, M. (2003) Evolution (3rd edition). Blackwell, Oxford (QH366.2 Rid)

Ridley’s book provides an overview and basic introduction, and is associated with Web-based review questions and tutorials that may be helpful.

The following textbooks cover particular aspects of the course, and where specifically indicated in lectures / notes they should be consulted. However, in general these are for reference, and do not form required reading

 Barton, N. H. et al. (2007). Evolution. Cold Spring Harbor Laboratory Press (HUB RESERVE; Folio QH366.2 Evo.)  Gillespie (2004) “Population Genetics: A Concise Guide” Johns Hopkins University Press (QH455 Gil.)

For reference, here are two more advanced technical books (below). They are not part of a ‘reading list’, they’re simply listed for your convenience if you want to go beyond the core material

 Falconer, D. S. & Mackay, T. F. C. (1996). Introduction to Quantitative Genetics, 4th Edition. Longman Scientific and Technical, Harlow, Essex. (QH452.7.Fal)  Charlesworth B & Charlesworth D (2010) Elements of Evolutionary Genetics. Roberts & Co, Greenwood Village (QH390 Cha.)

If you want to read something, and cannot get hold of it, please ask the lecturer for help!

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Pre-recorded Lecture Modules

Each week, between 4 and 10 short pre-recorded Lecture Modules will be made available for you to watch. These will be accompanied by downloadable slides and notes (including possible further reading), and an online ‘quiz’. You should watch the lecture modules, read around the area, and attempt the quiz before the start of the following week. Then, on Tuesday of the week after release, there will be a live digital ‘collaborate’ session in which you can ask questions of the lecturer.

The lecture module quizzes may include some very difficult questions, including some that require calculations – do not expect them to be quick! These questions may not be easily answerable from the lecture modules alone. However, these lecture module quizzes do not contribute to your grade for the course. They are there to encourage you to watch the modules, to get you reading and thinking, and to provide a focus for the Q&A. By looking at which questions turned out to be easy, and which hard, we can make sure the Q&A focusses on the most important areas.

Introduction to the course (DJO) i) Five ‘forces’ of evolution 1. Mutation 2. Genetic Drift 3. Natural Selection 4. Migration 5. Recombination ii) An overview of the course  The behaviour of genes in populations – Population genetics and Molecular Evolution  Genetics of Complex Traits – Quantitative genetics  Molecular phylogenetics  Speciation  Genetic parasites  Revision Sessions

Population genetics and molecular evolution (DJO)

Equations from my lectures (1-9) that you may need to remember are marked in boxes that are highlighted in Bold/Grey with an ‘R*’ in the bottom right hand corner of the box, like this one. R*

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Other equations from my lectures, which you might need to use in problems or exam questions, but you do not need to remember (they will be provided), are boxed but not highlighted, like this one

Module 1: Mutations, Diversity and Drift 1.1 Mutations  Individuals differ from each other  Mutations arise through errors in repairing and/or copying genetic material  Mutations take many forms (for example single-base, indel, transposable elements, Inversions, Strand slippage)  Mutation rates vary greatly between species, around 10-3 to 10-9 /site/year  Mutations vary between sexes, ages, and individuals  Mutations vary in their effect (neutral, deleterious, beneficial)  Important classes are synonymous and non-synonymous mutations  The fitness effects of mutations can be measured experimentally, but it is challenging

1.2 Genetic Diversity  The most commonly used measure of genetic diversity is Gene diversity (H), which reflects “The probability that two alleles drawn at random are different”  This can be averaged across many loci  For DNA sequences, the most commonly used measure of genetic diversity is the nucleotide or pairwise diversity π, which reflects “the average proportion of bases that differ between a pair of sequences”  For large sample sizes, π and H measure the same quantity

For n different alleles at a locus, each with frequency pi, gene diversity (H) can be calculated as 풏 ퟐ 푯 = ퟏ − ∑(풑풊 ) 풊=ퟏ R*

1.3 Heterozygosity and Hardy-Weinberg Equilibrium (HWE)  In a diploid population the proportion of heterozygous individuals is called the “Heterozygosity”  Under random mating, expected heterozygosity (Hexp) is the same as gene diversity (H)  Hardy-Weinberg Equilibrium (HWE) describes the stable frequencies of heterozygotes (2pq if there are two alleles) and homozygotes (p2 and q2) under random mating  Unless individuals are mating non-randomly with respect to their alleles, HWE is usually a good approximation for almost all populations  By assuming HWE, we can estimate allele frequencies from genotype frequencies, even if there is dominance

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1.4 Generalised HWE  Mating between relatives (who are more likely to share alleles) generates deviations from HWE  Under complete selfing, all heterozygotes are eventually lost, and the diploid genotype frequencies are p and q.  We can use the concept of “identity by descent” to show that under other forms of inbreeding, the proportion of heterozygoes decreases relative to HWE by 1-f

Genotype BB BR RR Frequency p2+fpq 2pq(1-f) q2+fpq

thus: f = (2pq – Hobs)/2pq = 1- Hobs/Hexp

Where p = freq. allele B and q = freq. R, and f = inbreeding coefficient R*

 The inbreeding coefficient within a population f can be estimated from the observed and expected heterozygosity

For partial selfing

f=S/(2-S)

Where S is the proportion of the zygotes that are formed by selfing

1.5 – 1.6 Genetic Drift  In each generation, every individual has a parent, but not every individual has offspring  For a finite population size, chance sampling of alleles generation-to- generation causes allele frequencies to change, and this is called Genetic Drift  This is often modelled in a simple framework with a fixed population size, random mating, and discrete non-overlapping generations  For two alleles, the change in allele frequencies can be modelled as a binomial sampling process, and the variance in allele frequencies across samples is pq/N for a haploid population where p and q are the two allele frequencies (p+q=1) and N is the number of haploid individuals (2N alleles in the population for diploids) in this idealised population  This means that drift happens faster in smaller populations  Genetic drift is expected to always reduce diversity, on average

For a change in diversity per generation ΔH, starting diversity H0, and idealised population of size N individuals (i.e. 2N alleles for diploids)

푯 횫푯 = − ퟎ ퟐ푵

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For a change in diversity over t generations for a diploid

ퟏ 풕 푯 = 푯 (ퟏ − ) 풕 ퟎ ퟐ푵 R*

 Standing neutral genetic diversity represents a dynamic equilibrium between loss of diversity (through drift) and the gain of diversity (through mutation).

At equilibrium between mutation (per generation) and genetic drift in a Wright- Fisher population of size N (or any population with effective size Ne), gene diversity is:

ퟒ푵풆흁 푯 ≈ ퟒ푵풆흁 + ퟏ And if 4Ne

푯 ≈ ퟒ푵풆흁  R*

1.7 Effective Population Size  An idealised randomly-mating diploid population of N individuals (2N alleles) with constant population size and non-overlapping generations in which every individual has an equal chance of reproducing is called a Wright-Fisher population  The size of a hypothetical Wright-Fisher Population that drifts at the rate of a real, observed, population is the Effective population size (Ne) of that real population  For this process, the ‘rate of drift’ can be measured in several ways (e.g. the variance in allele frequencies among potential new generations)  Effective population size can be estimated from the relationship between the gene diversity (nucleotide diversity) and the mutation rate  Any process that reduces the number of reproducing individuals or increases the variance in reproductive success causes the effective population size to be smaller than the census population size  This means that Ne is almost always much smaller (often orders of magnitude) than Nc  Random mating in a finite population gives rise to distant inbreeding, and this increase in inbreeding from generation to generation over time is another way of looking at genetic drift  This gives a version of effective population size based on the rate at with inbreeding increases, in addition to “variance in allele frequencies” and “coalescent time” definitions. Suggested reading  Coop PDF, Chapters 2 and 4  Ridley 3rd Edition, pp96-104 and Chapter 6;  Barton, pp29 and pp415-425

Module 2: Natural Selection

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2.1-2.3 Fitness and Natural Selection  Natural selection corresponds to differential survival and/or reproduction; such that different genotypes contribute unequal numbers of gametes to the next generation  The fitness of a genotype is defined as its relative contribution of gametes to the next generation  Simple models can be used to predict the change in allele frequency when recessive homozygotes or dominant phenotypes are at a selective disadvantage.  Application of these models to data from natural populations allows the size of selection coefficients to be estimated.  An equilibrium occurs between mutation and purifying selection against recessive deleterious mutations. These deleterious mutations can be maintained at significant frequencies in the population (genetic load)  If the deleterious alleles are recessive, this leads to inbreeding depression  Some forms of selection (balancing selection) can maintain genetic variation within populations  When the heterozygote has higher fitness than either homozygote, genetic variation is maintained but the population suffers a ‘segregation load’

The change in frequency of allele under selection against a recessive deleterious allele with frequency q, is 푝푞2푠 훥푞 = − ( ) 1 − 푞2푠

At equilibrium between mutation (μ per generation toward the deleterious allele) and purifying selection (s) against recessive phenotype, 푞 = 2√휇⁄푠

2.4 Natural Selection versus genetic drift

 The probability of a neutral allele fixing is simply its frequency, which is 1/(2Ne) for a new mutation in a diploid population with an effective size of Ne individuals  With additive selection (the heterozygote is half way between the two homozygotes in fitness) the probability of a positively selected allele (with advantage s) being fixed is s, under simple models  For small populations and weak selection, the change in allele frequency due to genetic drift (chance sampling) can outweigh the change due to naturel selection  When selection is low (s is small) so that Nes<1 then there is a reasonable chance of the deleterious allele being fixed by selection  Thus selection can only dominate over drift when Nes>>1 2.5-2.6 DNA and protein sequence evolution  We can measure the divergence between two sequences as the number of substitutions that have accumulated, per site (K)  If divergence is high, ‘saturation’ may mean that we cannot detect all of the changes simply by counting the differences

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 Models such as the Jukes Cantor model can be used to estimate the true divergence

The probability of fixation of a new neutral allele in a diploid population size N is ퟏ

ퟐ푵

The probability of fixation of a new additive beneficial allele is approximately s

Selection will only dominate over drift if Nes>>1

Neutral theory predicts that the rate of fixation of neutral alleles in a population is the same as the mutation rate, µ

The KA/KS ratio can be used to diagnose the kind of selection that has acted on a sequence R*

Protein sequence evolution  KA and KS measure the rate of change at nonsynonymous and synonymous sites  If KA=KS it implies that protein evolution is close to neutral, if KAKS there may be positive selection  McDonald-Kreitman tests use a Chi-squared test of polymorphism and divergence data to test for adaptive protein substitution and can quantify the rate using the statistics a, alpha, and ωa.

The number of adaptive amino-acid substitutions in a gene (a) can be estimated from the number of synonymous (S) and non-synonymous (N) fixed differences between species (D) and polymorphisms (P) within species 푃푁퐷푆 푎 = 퐷푁 − 푃푆

 Suggested reading  Ridley 3rd Edition, pp104-128 and Chapter 7;  Barton Chapter 17  Coop PDF Chapters 10-13

Module 3: Balancing Selection and Population structure 3.1 Heterozygote advantage  If the heterozygote is more fit than either homozygote, then both alleles will be retained in the population (such as the locus underlying the Sickle-Cell trait)  This can be modelled in a similar way to beneficial and deleterious mutations, and shows that there is a stable equilibrium frequency for the alleles

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If there is heterozygous advantage (where the heterozygote is more fit than either homozygote):

Genotype BB BR RR Fitness 1-s1 1 1-s2 then there will be a stable equilibrium at

푠 푠 푝∗ = 2 , and 푞∗ = 1 − 푝∗ = 1 푠1+푠2 푠1+푠2

3.2-3.3 Negative frequency dependent selection  Any process that causes rare alleles to be more beneficial than common ones (e.g. selective coefficients are inversely proportional to frequency:) will have this property.  This indirectly includes cases of heterozygote advantage, and other examples include sex determination loci and self-incompatibility loci  Time-lagged host-parasite dynamics can also give rise to a rare-allele advantage, but in this case the allele frequencies can cycle over time  Negative frequency-dependent selection tends to prevent alleles fixing (or being lost), and consequently such ‘balanced’ alleles or haplotypes tend to have high diversity and can be much older than species 3.4-3.7 Structured Populations  Populations are often formed of a large number of ‘local’ or ‘sub’-populations (sometimes called ‘demes’) that exchange migrants  Genetic drift causes the allele frequencies of the subpopulations to diverge, but migration between them tends to homogenise allele frequencies  Differences in allele frequencies among sub populations lead to a global excess of homozygotes (over the single-population expectation): this is called the Wahlund effect  The deviation from overall HWE caused by population structure is called FST. This is the proportion of total diversity that is due to differences in allele frequencies among populations  FST is one of Wrights ‘F statistics’, and measures the increased inbreeding that results from population structure (FIS measures inbreeding within a subpopulation, and FIT measures the combined inbreeding)  FST also reflects the difference in expected coalescence time between alleles sampled at random, and alleles sampled at random within a subpopulation  Models, such as the infinite island model, can be used to derive expected FST in terms of the proportion of the population that are immigrants in each generation  Such models provide valuable insights, but can only be used to estimate Nm when the key assumptions are met

FST can be defined as 푯푻 − 푯̅̅̅푺̅ 푭푺푻 = 푯푻

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Where FST is inbreeding due to population subdivision, HT is gene diversity in the total population and HS is the gene diversity within each sub-population. If the calculated estimates HT and HS are used to calculate an estimate of FST, the estimator is sometimes called GST

Under an infinite island model, and assuming low levels of migration and large population sizes, we expect ퟏ 푭푺푻 ≈ ퟏ + ퟒ푵풆풎 where Ne is effective population size of each subpopulation m is migration rate (probability that an allele was a migrant) per generation among populations R*

Selection in structured populations  Local adaptation increases genetic differentiation at the selected locus  One simple island model (selection in favour of a new dominant allele on an island, versus selection against it on an infinite mainland) is analogous to our model of mutation-selection balance.

Suggested reading  Ridley Pages 123-129  Barton Pages 505-510  Coop Chapters 3, 6 and 11

 Barton Chapter 16  Coop Page 163-170

Module 4: Sex and Recombination 4.1-4.3 Linkage and Linkage disequilibrium  Sites in the genome can be physically linked (into haplotypes) and this can cause non-random associations between alleles at different loci (e.g. A occurs with B more or less often than you would expect by chance, given the frequencies of A and a, B and b)  The deviation from random association is called Linkage Disequilibrium (LD), can be quantified by D=pAB-pA.pB where pAB is the frequency of gametes carrying A and B, and pA is the frequency of the A allele  Recombination rapidly breaks down LD, so that the effect is usually only large between closely linked sites  Processes that increase the rate of genetic drift increase the amount of LD through random sampling, but for models like the Wright-Fisher model, the effect is small except for very closely linked sites  Natural selection and population structure can also (transiently) increase LD, and do not require physical linkage.

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 Recombination is ‘patchy’ (‘Hotspots’) in many organisms, and this gives rise to large haplotype blocks that allow alleles to imputed, and permits association mapping with markers. 4.4-4.5 Selection at linked sites  Alleles physically linked to a selected site spread with it, reducing diversity. This is called a “selective sweep”  Recombination allows alleles at more distant sites to escape the sweep, so that diversity is only reduced close to the selected site.  Strong selection leads to rapid sweeps, which (because there is little chance for recombination during the sweep) reduce diversity over a wider area  The continuous removal of deleterious alleles also reduces diversity at linked sites  Together these two phenomena result in a correlation between recombination rate and diversity.  The local reduction in diversity and increased LD caused by a selective sweep can be detected. This can be used to detect recent selective evets  Strong selection leads to rapid sweeps, which leave to a wider ‘valley’ in diversity

4.6 The evolution of sex and recombination  Sex is costly, and asexuality will tend to spread because of the two-fold advantage  Theories for the evolution and maintenance of sex revolve around its power to break up LD between beneficial and deleterious mutations, facilitating the spread of the beneficial ones, and permitting the removal of deleterious ones

4.7 Recombination is sometimes selected against  Co-adapted alleles at different loci can be held together by reduced recombination rates  This is thought to be the basis of the evolution of sex-determining regions and chromosomes, and ‘supergenes’ Suggested reading  Coop pp43-48 and Chapter 13  Ridley pp200-203 and 314-320;  Barton pp427-438, 663-672, 682-683

Genetics of Complex Traits (Craig Walling) Module 5: Introduction to the genetics of complex traits and heritability 5.1 An Introduction to complex traits and their genetics  The importance of quantitative traits in biology.  Fundamental properties of quantitative traits.  Complex or quantitative traits often show continuous or quasi-continuous variation.

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 Statistical properties of interest are means, variances and covariances between traits or individuals.  Genetic variation for quantitative traits leads to resemblance between relatives.  The degree of resemblance can be used to quantify the level of genetic variation.  Artificial selection on quantitative traits leads to changes in mean.  Progeny of matings among relatives tend to show reduced fitness or level of fitness-related traits, a phenomenon known as inbreeding depression.  The continuous range of values for quantitative traits results from the joint action of many genes plus random environmental influences. References Ridley p222-228 Falconer & Mackay Chapter 6. Mackay, TFC Stone, EA & Ayroles, JF (2009). The genetics of quantitative traits: challenges and prospects. Nature Reviews Genetics 10: 565-577. 5.2 Resemblance between relatives and the heritability of complex traits  Causes of resemblance between relatives  Quantifying resemblance between relatives  A model of the phenotypic value, P.  Phenotypic values for quantitative traits can be partitioned into genetic and environmental effects.  The genetic value of an individual can in turn be partitioned into additive and non- additive (including dominance) effects.  Additive effects can be transmitted from parents to offspring, whereas non- additive effects cannot.  The additive effect for an individual is known as its breeding value.  Broad sense heritability (H2) is the proportion of the phenotypic variation in a population that is explained by genetic effects (additive or non-additive).  Narrow sense heritability (h2) is the proportion of variation in a population that is explained by additive genetic effects of loci.  An outline of how broad sense heritability can be estimated using multiple cloned progeny from different families.  An outline of how narrow sense heritability can be estimated using the variation among full-sib families. References Ridley p228-232 Falconer & Mackay Chapter 8 Visscher, P. M., Hill, W. G. and Wray, N. R. (2008). Heritability in the genomics era — concepts and misconceptions. Nat. Rev. Genet. 9: 255-266. Key formulae that you need to remember P = G + E P = A + D + E 2 H = VG/VP 2 h = VA/VP

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Module 6. Estimation of heritability and an introduction to selection on complex traits 6.1 Experimental estimation of the heritability of complex trait  Resemblance between relatives - recap  Relatives tend to resemble each other because they share genes and environments.  A measure of resemblance between relatives for a quantitative trait is covariance.  General principle of heritability inference.  Heritability is estimated from resemblance between relatives based on covariances between different sorts of relatives.  Heritability may be estimated from parent-offspring resemblance by regression of offspring on parental phenotype.  Heritability may also be estimated from resemblance (in this case measured as a correlation) between full sibs. Part of the resemblance between full sibs is caused by dominance and common environmental variation.  The correlation between half sibs may also be used to estimate heritability. Such estimates are not subject to upward bias due to dominance or common environmental sources of resemblance.  In general, the degree of gene sharing determines the genetic covariance between relatives. References Ridley p.232-236 Falconer & Mackay Chapters 9 & 10 Key formulae that you need to remember h2 = VA/VP h2 = 2bOP h2 = bOPM cov(FS) = VA/2 + VD/4 cov(HS) = VA/4 6.2 An introduction to selection on complex traits  Artificial selection is the deliberate mating of phenotypically extreme individuals for a quantitative trait.  Importance of artificial selection and its applications.  The selection differential (S) is the difference in mean phenotype between the overall population and the group of selected individuals.  The selection response (R) is the change in mean in the population due to one generation of selection.  The response prediction equation (the “breeder’s equation”) is R = h2S.  S can often be expressed more usefully as the selection intensity (i), a measure based on phenotypic standard deviation units.  Empirical properties of selection response.  Realised heritability is the heritability estimated from the selection response in a selection experiment.  Example of calculating realized heritability using morphological trait data from a Darwin’s finch.

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References Ridley p236-240 Falconer & Mackay Chapters 11 & 12 Hill, WG and Kirkpatrick, M (2010). What animal breeding has taught us about evolution. Annual Review of Ecology, Evolution, and Systematics 41: 1-19 Key formulae that you need to remember R = h2S

Module 7. The nature of complex trait variation

7.1 An introduction to studying the nature of genetic variation underlying complex traits  The genes underlying quantitative traits are known as quantitative trait loci (QTLs).  Definitions of QTL – region affecting a trait or causal allele.  Why study QTLs?  QTLs are amenable to experimental investigation using genetic markers in QTL mapping experiments.  The analysis of QTLs requires non-random associations between the alleles at QTLs and alleles at marker loci.  QTL mapping experiments using inbred lines that differ at QTL loci and at marker loci are powerful, because non-random associations between QTLs and markers are complete.  The presence of a QTL in a mapping experiment is signalled by a difference in mean phenotype between groups of individuals derived from a cross that differs for a marker locus, or marker loci.  The analysis of QTLs can be carried out one marker at a time or by analysing pairs of markers - the latter known as interval mapping.  Empirical results from mapping experiments.  QTL mapping experiments have usually confirmed that genetic variation in quantitative traits is caused by many loci with relatively small effects, but occasionally, single loci with large effects have been found.  An example of one the nature of variation for key traits in the domestication of maize is discussed, including the identification and of teosinte branched1 locus that controls a large part of the difference in morphology between maize and its presumed wild ancestor, teosinte.  The basis of QTL mapping in outbred populations using genome-wide association studies.  Examples of case-control study for human genetic disease and mapping of loci affecting human height– the missing heritability problem.

References Falconer & Mackay Ch. 21 Doebley, J. F. 2004. The genetics of maize evolution. Annual Review of Genetics 38: 37-59 The Wellcome Trust Case Control Consortium. (2007). Nature 447: 661-678. Weedon, MN et al. (2008) Genome-wide association analysis identifies 20 loci that influence adult height. Nat. Genet. 40:575-583.

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Module 8: Molecular Phylogenetics (Paul Sharp) 8.1 Introduction to phylogenetics

Variation in rates of molecular evolution: - Rates vary among genes/proteins - Rates vary among genomes (nuclear, organelle) - Rates vary among lineages; relative rate tests

Four aspects of a phylogenetic tree: - Topology; the branching order - Branch lengths; scaled to reflect genetic change - Root; the last common ancestor - Confidence; statistical evaluation of the tree

The philosophy behind making phylogenies from sequence data, and how different methods for making phylogenetic trees approach this.

MP (Maximum Parsimony) - produces topology only; unrooted tree with undefined branch lengths - susceptible to rate variations and parallel or convergent changes UPGMA (Unweighted Pair-Group Method with Arithmetic means) - assumes a constant rate - automatically midpoint roots a tree - very susceptible to rate variations

NJ (Neighbour-Joining) - approximate route to a “minimum evolution” tree - does not assume a constant rate - produces an unrooted tree

Bootstraps to estimate confidence

ML (Maximum Likelihood) methods - very complex - in principle, very good Problems encountered when making phylogenies: - estimating distances - how to root? - long branch attraction Background reading: Chapters 7 (sections 7.3-7.4) and 15 (sections 15.9-15.11) in Ridley, M. (2003) Evolution (3rd edition)

8.2 An application of molecular phylogenetics

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The origins and evolution of AIDS viruses. AIDS (Acquired Immune Deficiency Syndrome) was first described in 1981. It is estimated that more than 35 million people are infected with AIDS viruses, and that over 39 million people have died from AIDS.

When the first example of a causative virus (HIV-1) was characterised in 1985 there were no known close relatives. Numerous HIV-1 creation myths arose, implicating germ-warfare research, or contaminated vaccines.

Since then the sequences of numerous strains of HIV-1 have been determined, and closely related viruses have been found infecting more than 30 different species of African primates. In a classic application of molecular phylogenetics, comparison of these viral genomes has allowed the retrospective tracing of the origins and spread of the AIDS pandemic. A number of the results are quite surprising ....

Reference: Sharp, P.M. & Hahn, B.H. (2011) Origins of HIV and the AIDS pandemic. Pp. 1-22 in HIV: From biology to prevention and treatment. Editors: Bushman, F.D., Nabel, G.J. & Swanstrom, R.. Cold Spring Harbor Perspectives in Medicine 2011;1:a006841. doi:10.1101/cshperspect.a006841

Module 9: Speciation (Simon Martin)

9.1 Mechanisms of speciation

 Species definitions centre on reproductive isolation, but they do not reveal the mechanisms of speciation  Divergence in allopatry is the null model for speciation  Post-zygotic ‘Incompatibilities’ can evolve unhindered, and may accumulate quadratically  Incompatibility loci can be found through genetic mapping  Theoretical models of hybrid fitness can explain Haldane’s rule  Reinforcement of pre-mating isolation may evolve upon secondary contact  But the null model is not consistent with some empirical observations: sympatric species and natural hybridisation

9.2 Genomics of speciation

 Species barriers may be permeable: the ‘speciation continuum’ and the ‘genic view of speciation’  Natural hybridisation provides a window into the mechanisms of speciation  Speciation despite gene flow: Local adaptation (ecological speciation) and inversions  Speciation because of gene flow: A creative role for hybridisation  An ongoing challenge: distinguishing causes from consequences of speciation

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Background Reading Barton et al. Chapter 22 See also: Seehausen et al. (2014) Nature Reviews Genetics 15, 176–192 And the special issue of Trends in Ecology and Evolution (2001) Vol 16, 325-413.

Module 10: Genetic parasites (Darren Obbard)

10.1– Genetic parasites Alleles that (1) Kill their competitor allele, (2) Over-replicate themselves, (3) Convert the competitor allele, or (4) are favoured in germline divisions, will all tend to spread This can be at the expense of overall organismal fitness, so long as net effect of the transmission-bias outweighs the fitness cost

10.2 – t-haplotypes in mice 10.3 – Transposable elements 10.4 – Male killing endosymbiotic bacteria in insects

Background Reading Barton pp587-593,

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Computer Practicals and Quizzes

Computer practicals will be done using freeware or university-provided software, using your own computer. In addition, several pieces of software are available via https://apps.ed.ac.uk/. In advance of the live-supported online session, it is essential that you have either downloaded and installed the necessary free software, or that you have confirmed you can access it remotely from the University via https://apps.ed.ac.uk/.

You can do the practical at any time during the week, but following the lecture on Tuesday there will be live online support from the practical leader and demonstrators, who will be available for around 2 hours (from 3pm). Remember that you can ask questions at any time, via the discussion forum.

Detailed instructions for the five different computer practicals will be made available online, under the appropriate week for ‘course materials’. The first two practicals (DJO) will provide an introduction to Chi-Squared tests for count data (such as testing for HWE), and give you an opportunity to simulate genetic drift and selection to see how they interact to determine fitness in small populations. The next two practicals (CW) will provide an introduction to regression and ANOVA, which are essential statistical methods in quantitative genetics, and then give you an opportunity to estimate the heritability of some human traits. The final practical (PS) will introduce you to phylogenetic methods

Please download and read the instructions carefully before the live digital session with the demonstrators.

Each week there will be an online quiz associated with the practical. Each practical quiz counts 5% toward your mark for this course, except the first (which is a practice). The quiz must be completed before the start of the following week. The online quiz is ‘open book’ (you can use your notes, the lectures, the handout etc) but you cannot discuss the answers with each other. As you work through each practical, ensure that you make notes in answer to the questions (both written and numerical), so that you can use these when doing the quiz.

Problem Tutorial Sessions

The four problem tutorial sessions are designed to assist with understanding lecture material and provide practice in answering quantitative questions in part A of the exam. One third of the examination (20% of the overall mark for the course) takes the form of quantitative questions similar to those dealt with in problem sessions 1-3.

Details of the problem sessions are provided in the Problem Session Handbook. They include some ‘practice’ questions with answers, and questions for which the answers will be discussed in the problem sessions. After you have worked through the problems, please use your answers to fill in the problem session quizzes on learn. These do not contribute to your course mark, but will help us to see what people found difficult, and thus where to focus the problem session.

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In-course Essay The essay provides you with the opportunity to study a topic in depth using information from the primary literature. The essay will be assessed, and counts 27% towards your final mark. It is associated with an online discussion forum that counts 3% of your final mark.

Essay titles will be available from the start of week 1, and from 5pm Friday of week 2 you will be able to choose which one you are going to answer. The titles are provided along with starter references and a brief description of what is expected. Unfortunately, because of the numbers involved, we cannot guarantee that everyone will get their first choice: sign-up will be on a first-come, first-served basis with a maximum number for each essay.

The essay activities are divided into three parts: Part 1: What makes a good essay?

Long-form formal or technical writing is an essential transferable skill. Although ‘essays’ are rarely called that outside of a pedagogical setting, the skills required are similar to those used in any type of report or paper. In academic science, a ‘review article’ is really just a type of essay. An essay needs to introduce and explain a topic (at a level suitable for the reader), it must give a flavour of the background or context while focussing on a particular message (such as answering a question), and it must provide a narrative—clearly explaining a logical train of thought or taking the reader through an argument.

You will almost certainly have written essays before, although perhaps not at this level of technical content. But do you know what makes a good essay, or how they are marked? In week 1 you can watch a short pre-recorded introduction to essay writing (from DJO’s, potentially idiosyncratic, perspective) and download three example essays that have been written for EEG. Over the following two weeks (before Friday of week 2) you should attempt to ‘mark’ these three essays. What mark (out of 100) would you award each one, and what grades (1st, 2.1, 2.2, 3rd) does this correspond to? You are given the Edinburgh ‘Common Mark Scheme’ to guide your decisions, and you should enter your answers via the LEARN survey. Please discuss your thoughts with each other, and via the LEARN discussion forum. Then, on Friday of Week 2 we will have a live digital Q&A session via collaborate, where you can ask general questions about essay writing and marking, and find out what marks the essays were originally given (and why). We hope that this will prepare you for writing your own essay! Part 2: Engagement with the starter-references [3%]

After choosing your essay title from the list provided (week 2) you should skim-read a few of the starter references to get a feel for the topic, and then choose one paper to summarize for the rest of the people writing the same essay. You should aim to write your summary in Week 3 or 4, and upload it before the end of week 4. Your summary should be around half a page (no more than 350 words), and should answer (i) What is the primary ‘take-home’ message of the paper? and (ii) How this is relevant to the essay question you have been set? Remember, some of the recommended starter-references may be ‘deep’ background, or tangential to the question asked—but they have all been recommended for a reason!

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You should upload your summary to the discussion forum associated with your essay question (i.e. within the relevant ‘group’ area on LEARN). Then you need to read the summaries uploaded by the others who are writing the same essay, and use the forum to ask them questions about the paper they read. This should be done during weeks 5 and 6. You should ask at least three different people a question, and you should respond to the questions that you are asked. Engaging with this is worth 3% of the course mark (0.5 for each question you ask, and 1.5 for answering the questions you receive).

At any time after the end of Week 4 you could be ready to write a provisional essay plan for yourself (e.g. one A4 side of bullet points, broken up into suitable subheadings). You should certainly do this before week 7.

An effective essay plan should be structured around the key ideas that you plan to focus on in the essay, and ordered so that it helps structure a comprehensible narrative for the reader. On Friday of week 7 there will be a live digital Q&A session with the person who set the essay (probably via collaborate). Thinking about your essay plan, the papers you have read, and the questions/answers on LEARN, this is your opportunity to ask for guidance, clarification, and explanation. You should have specific questions (“Would it be a good idea to write a whole paragraph on x, or should I keep this short and focus more on y?”, not vague generalisations (“What should I write about?”). You can also ask specific technical questions, or about questions that arose in the discussion forum. But do be prepared for answers such as “Re-read page 2 of the paper”, or “Read chapter z of this book”, if the person setting the essay thinks that answering the question yourself is an important part of the process.

Part 3: Writing the Essay [27%]

The essay should be no more than 2,000 words (excluding bibliography, but including in-text references, tables, and legends to figures). The essay will be assessed, and counts 27% towards your final mark. An effective essay will introduce the topic, explore the question in a clear, logical, and critical fashion, and come to conclusions based on the evidence presented. Good essays rarely follow the structure of the reading material (e.g. paper by paper), and often tend to follow the structure of the ideas (concept by concept). In general, unless you are asked specifically to review the papers, try to focus on the biology rather than literature surrounding it. The sources of evidence gathered from the primary literature should be properly cited in the text, and referenced in full at the end of the essay. Further advice for essay writing is provided in Appendix I.

The essays should be submitted electronically via Turnitin by the deadlines specified on LEARN.

You will be given preliminary written feedback along with your mark. However, if you would like more comprehensive verbal feedback, then we offer a 1:1 tutorial meeting (15-30 minutes) to discuss your essay with the marker in person. Markers’ email addresses appear below: just email them once you have your mark.

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Essay Titles

Provisional essay titles are given below, they may change slightly.

1. What are the evolutionary consequences of chromosomal inversions?

Simon Martin: [email protected]

Chromosomal inversions are thought to suppress recombination, and thereby have important consequences for evolutionary processes such as adaptation and speciation. Your essay should describe the mechanisms by which chromosomal inversions suppress recombination, and the theoretical models for how this can affect evolution. It should also review recent empirical studies of inversions, and describe how these relate to theoretical predictions. In particular you should focus on population genetics processes that could lead to the spread and maintenance of inversions, as well as the evidence that is required to prove a role for inversions in evolution.

Starter references:  Noor, M. A. F. et al. (2001) ‘Chromosomal inversions and the reproductive isolation of species’, PNAS, 98: 12084–12088. https://doi.org/10.1073/pnas.221274498  Rieseberg, L. H. (2001) ‘Chromosomal rearrangements and speciation’, Trends in Ecology and Evolution, pp. 351–358. https://doi.org/10.1016/S0169- 5347(01)02187-5  Kirkpatrick, M. and Barton, N. (2006) ‘Chromosome inversions, local adaptation and speciation.’, Genetics, 173: 419–34. https://doi.org/10.1534/genetics.105.047985  Berdan, A. et al. (2019) ‘Muller’s Ratchet and the Long-Term Fate of Chromosomal Inversions’, BioRxiv https://doi.org/10.1101/606012  Küpper C. et al. (2016) ‘A Supergene Determines Highly Divergent Male Reproductive Morphs in the Ruff’. Nature Genetics 48: 79–83. https://doi.org/10.1038/ng.3443  Davey, JW. Et al. (2017) ‘No Evidence for Maintenance of a Sympatric Heliconius Species Barrier by Chromosomal Inversions’. Evolution Letters https://doi.org/10.1002/evl3.12  Coughlan, J. M. and Willis, J. H. (2019) ‘Dissecting the role of a large chromosomal inversion in life history divergence throughout the Mimulus guttatus species complex’, Molecular Ecology, 28: 1343–1357. https://doi.org/10.1111/mec.14804  Yan, Z. et al. (2020) ‘Evolution of a Supergene That Regulates a Trans- Species Social Polymorphism’. Nature Ecology & Evolution 4: 240–49. https://doi.org/10.1038/s41559-019-1081-1  Todesco, M. et al. (2020) ‘Massive Haplotypes Underlie Ecotypic Differentiation in Sunflowers’. Nature 584: 602–7. https://doi.org/10.1038/s41586-020-2467-6

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2. Why might males and females evolve different lifespans?

Jacob Moorad: [email protected]

Multiple evolutionary models have been proposed to explain why in many species one sex (usually the females) tends to live longer than the other. Your essay should explain the fundamentals of the evolutionary theory of ageing, paying particularly close attention to the role of natural selection. In what ways has this theory been developed to account for the observed differences in male and female lifespan? Be sure to describe how each proposed model views the ways in which environment and genes shape the evolution of ageing/lifespan. Critically discuss the evidence for each model of lifespan sexual dimorphism in laboratory and natural populations.

 Austad, S. N., and K. E. Fischer. 2016. Sex differences in lifespan. Cell Metab 23:1022-1033.  Bonduriansky, R., A. Maklakov, F. Zajitschek, and R. Brooks. 2008. Sexual selection, sexual conflict and the evolution of ageing and life span. Functional Ecology 22:443-453.  Clutton-Brock, T. H., and K. Isvaran. 2007. Sex differences in ageing in natural populations of vertebrates. Proceedings of the Royal Society B- Biological Sciences 274:3097-3104.  Maklakov, A. A., and V. Lummaa. 2013. Evolution of sex differences in lifespan and aging: causes and constraints. Bioessays 35:717-724.  Marais, G. A. B., J. M. Gaillard, C. Vieira, I. Plotton, D. Sanlaville, F. Gueyffier, and J. F. Lemaitre. 2018. Sex gap in aging and longevity: can sex chromosomes play a role? Biol Sex Differ 9:33.  Williams, G. C. 1957. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11:398-411.

3. How do beneficial mutations go to fixation in populations?

Matthew Hartfield: [email protected]

Populations adapt through the fixation of beneficial mutations. A traditional view was that these mutations arose de novo and spread to fixation, in a process known as a ‘selective sweep’. Recent research has emphasised that adaptive mutation could have instead spread via other processes, which are sometimes called ‘soft sweeps’. These mechanisms include adaptation from recurrent mutation or existing standing variation. Explain the different adaptation mechanisms, and when each is likely to arise. Consider how feasible it is to differentiate between them, in light of various critiques in the literature.

Starting references:  Anderson, TJC et al. (2017) Population Parameters Underlying an Ongoing Soft Sweep in Southeast Asian Malaria Parasites. Mol. Biol. Evol. 34: 131— 144.  Colosimo, PF et al. (2005) Widespread Parallel Evolution in Sticklebacks by Repeated Fixation of Ectodysplasin Alleles. Science 307: 1928—1933.  Garud, NR et al. (2015) Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps. PLoS Genet. 11: e1005004.

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 Hanikenne, M et al. (2013) Hard Selective Sweep and Ectopic Gene Conversion in a Gene Cluster Affording Environmental Adaptation. PLoS Genet. 9: e1003707.  Harris, RB et al. (2018) On the unfounded enthusiasm for soft selective sweeps II: Examining recent evidence from humans, flies, and viruses. PLoS Genet. 14: e1007859. (See also reply: Schrider, DR and Kern, AD (2018). On the well–founded enthusiasm for soft sweeps in humans: a reply to Harris, Sackman, and Jensen. https://zenodo.org/record/1473856)  Hermisson, J and Pennings, PS (2017) Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation. Methods Ecol. Evol. 8:700–716.  Jensen, JD (2014) On the unfounded enthusiasm for soft selective sweeps. Nat. Commun. 5: 5281.  Karasov, T et al. (2010) Evidence that Adaptation in Drosophila Is Not Limited by Mutation at Single Sites. PLoS Genet. 6: e1000924.  Schrider, DR and Kern, AD (2017). Soft Sweeps Are the Dominant Mode of Adaptation in the Human Genome. Mol. Biol. Evol. 34(8): 1863–1877.  Studer, A et al. (2011) Identification of a functional transposon insertion in the maize domestication gene tb1. Nat. Genet. 43: 1160–1163.

4. What is the evolutionary importance of meiotic recombination?

Susan Johnston: [email protected]

Meiotic recombination is the exchange of large chunks of DNA between homologous chromosomes during gametogenesis. The rate of recombination has been shown to vary within and between chromosomes, individuals, sexes, populations and species. What are the relative costs and benefits of recombination? What factors drive this variation in recombination rate? Does recombination rate have the potential to evolve, and is there evidence that it is evolving?

Starter References:  Barton N. H., (1998) Why sex and recombination? Science. 281: 1986–1990.  Baudat, F., et al (2010). PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science, 327(5967), 836-840.  Burt, A., & Bell, G. (1987). Mammalian chiasma frequencies as a test of two theories of recombination. Nature, 326(6115), 803-805.  Coop, G., & Przeworski, M. (2007). An evolutionary view of human recombination. Nature reviews. Genetics, 8(1), 23.  Kong A. et al. (2004) Recombination rate and reproductive success in humans. Nat. Genet. 36: 1203–1206.  Kong A. et al (2008) Sequence Variants in the RNF212 Gene Associate with Genome-Wide Recombination Rate. Science, 319, 1398-1401  Kong, A. et al (2014). Common and low-frequency variants associated with genome-wide recombination rate. Nature genetics, 46(1), 11-16.  Lenormand & Dutheil (2005) Recombination difference between sexes: a role for haploid selection. PLoS Biology, 3:e63  Otto S. P., Barton N. H., (2001) Selection for recombination in small populations. Evolution (N. Y). 55: 1921–1931.

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 Otto, SP & Lenormand, T. (2002) Resolving the paradox of sex and recombination. Nat. Rev. Genet. 3, 252-261.  Stapley J, PGD Feulner, SE Johnston, AW Santure, CM Smadja (2017) Variation in recombination frequency and distribution across Eukaryotes: patterns and processes. Phil Trans Roy Soc B, 372: 20160455.

5. What is the relative importance of large-effect versus small-effect alleles in adaptive evolution? Craig Walling: [email protected]

The nature of adaptation under natural (and artificial) selection is one of the key unresolved issues in evolutionary biology. There have been recent theoretical advances that have shed light on the predicted distribution of fitness effects of alleles fixed under adaptation. Genetic mapping with dense marker maps has led to the identification of some loci that explain quantitative differences between populations and species. Particularly informative experiments (referenced below) have been carried out to map the genes responsible for adaptations in maize. Do alleles with large effects make a major contribution to evolutionary adaptation, or can most evolutionary adaptation be explained by many alleles of small effects? Consider and compare the predictions and results from theoretical and empirical (experimental) papers.

Starter references:  Doebley, J. (2004). The genetics of maize evolution. Annual Review of Genetics 38: 37-59.  Doebley, J. F., Gaut, B. S. and Smith, B. D. (2006). The molecular genetics of crop domestication. Cell 127: 1309-1321.  Gray, MM, Parmenter, M, Hogan, C et al. (2015). Genetics of Rapid and Extreme Size Evolution in Island Mice. Genetics 201:213-228  Laurie, C. C., Chasalow, S. D., LeDeaux, J. R., McCarroll, R., Bush. D., Hauge, B., Lai, C. Q., Clark, D., Rocheford, T. R. and Dudley, J. W. (2004). The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel Genetics 168: 2141-2155.  Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A. et al. (2009). Finding the missing heritability of complex diseases. Nature 461: 747-753  Yang, J et al. 2010. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42: 565-569  NJ Nadeau, CD Jiggins 2010. A golden age for evolutionary genetics? Genomic studies of adaptation in natural populations. Trends in Genetics 26: 484-492 Strasburg, J. L., Sherman, N. A., Wright, K. M., Moyle, L. C., Willis, J. H. & Rieseberg, L. H. 2012 What can patterns of differentiation across plant genomes tell us about adaptation and speciation? Phil. Trans. R. Soc. B 367, 364–373. (doi:10.1098/rstb.2011.0199)  Orr, H. A. (2001). The genetics of species differences. Trends in Ecology and Evolution 16: 343-350.  Orr, H. A. (2003). The genetic theory of adaptation: A brief history. Nature Reviews Genetics 6: 119-

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6. Are immune systems engaged in an evolutionary ‘arms-race’ with viruses?

Darren Obbard: [email protected]

Viruses are the ultimate parasites; they need to hijack a cell to achieve even the most basic cellular processes. It seems likely that this hijacking is costly for the host, and all organisms have immune responses to combat viral infection. In turn, host immune responses has fitness costs for the virus. This sets up the possibility that host immune genes and viruses are engaged in an evolutionary arms race, with reciprocal adaptations by each side. What is the evidence that such an arms-race is really occurring? Is it supported by data from hosts and from viruses? Should we even expect it to be true, or is the verbal model just too simplistic? What else might explain what we see? Think carefully about the terms ‘immune system’, ‘evolutionary’, and ‘arms-race’, and what different authors might mean by each.

Starter references:  Brockhurst MA, Chapman T, King KC, Mank JE, Paterson S, Hurst GD. Running with the Red Queen: the role of biotic conflicts in evolution. Proc Biol Sci. 2014;281(1797):20141382.  Hampton HG, Watson BNJ, Fineran PC. The arms race between bacteria and their phage foes. Nature. 2020;577(7790):327-336.  Obbard DJ, Welch JJ, Kim KW, Jiggins FM. Quantifying adaptive evolution in the Drosophila immune system. PLoS Genet. 2009;5(10):e1000698.  Enard D, Cai L, Gwennap C, Petrov DA. Viruses are a dominant driver of protein adaptation in mammals. Elife. 2016;5:e12469. Published 2016 May 17.  Enard D & Petrov DA. Ancient RNA virus epidemics through the lens of recent adaptation in human genomes bioRxiv 2020.03.18.997346;  Currenti J, Chopra A, John M, Leary S, McKinnon E, Alves E, et al. (2019) Deep sequence analysis of HIV adaptation following vertical transmission reveals the impact of immune pressure on the evolution of HIV. PLoS Pathog 15(12): e1008177.  Petrova VN, Russell CA. The evolution of seasonal influenza viruses [published correction appears in Nat Rev Microbiol. 2017 Nov 07;:]. Nat Rev Microbiol. 2018;16(1):47-60. doi:10.1038/nrmicro.2017.118  Obbard DJ, Dudas G. The genetics of host-virus coevolution in invertebrates. Curr Opin Virol. 2014;8:73-78.  Murray GG, Kosakovsky Pond SL, Obbard DJ. Suppressors of RNAi from plant viruses are subject to episodic positive selection. Proc Biol Sci. 2013; 280(1765): 20130965. Published 2013 Jun 26.  Simmonds P, Aiewsakun P, Katzourakis A. Prisoners of war - host adaptation and its constraints on virus evolution. Nat Rev Microbiol. 2019;17(5):321-328.  Longdon B, Brockhurst MA, Russell CA, Welch JJ, Jiggins FM. The evolution and genetics of virus host shifts. PLoS Pathog. 2014;10(11):e1004395. Published 2014 Nov 6.

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Appendix – Practical advice for essay writing Content and structure

The most successful essay will be one that is well planned. You should read widely around the subject before deciding on the specific presentation of the answer. The best approach is probably a logical argument that you can develop through the essay, starting with a problem or controversy and reaching a conclusion by reconciling different theories or opinions. Always present the relevant sides of any conflicting arguments before drawing any conclusions; justify your conclusions by weighing up the different arguments. Decide on the theme and work out, in advance, how the argument will progress paragraph-by-paragraph. You may then want to go back to some of the key references and re-read the evidence. Do not just present a string of paragraphs, one for each fact, or one on each of the papers that you have read. You must integrate the information. Most importantly, remember to focus on the given question or title.

All essays must have a structure, and many poor essays suffer from poor structure. If you can identify the ‘question’ or ‘argument’ to be addressed in your essay, then a logical structure should hopefully be obvious. Do not change the title of an essay that is given to you, unless you are explicitly asked to do so. An essay may need to be subdivided into subsections under separate headings, as this can make it much easier for the reader to follow the structure. You will at least want to have an introduction at the beginning and a conclusion at the end, and probably several sections in between. Make good use of diagrams, graphs, and tables to save on words and to get your message across in the clearest possible way. If you consider that large tables of raw data or long quotations are essential to the essay, then these should be placed in an Appendix at the end.

It is often useful to provide the reader with biological context. The reader should be able to get a flavour of why the topic is interesting or important, and what the wider implications are. However, the focus of the essay must be to answer the question. Do not allow peripheral ‘context’ to detract from the central topic of the essay, and do not include information that is irrelevant to the question. A good ‘rule of thumb’ is that the reader should never be left to wonder why they are reading any particular sentence or paragraph – it should always be clear from context.

In an essay it is not sufficient to provide a commentary on what has been claimed, reported, or argued. High quality essays require that you explain the logical or evidential basis of claims or reports. To do this you need to provide a suitable outline of the arguments or data that can be followed by the reader, and which demonstrates your own understanding. You are expected to be critical in your evaluation of the literature on the topic. However, ‘critical’ does not mean unthinkingly negative. When you are critiquing published literature, you must always explain the basis of your criticism. It is not sufficient to say a study should have been done differently; you also need to explain why doing the study in a different way might be expected to change its conclusions.

You are also strongly encouraged to express your own views and reasoning. But, again, explain your thinking in detail. It is not sufficient to state your own conclusion without fully explaining the reasoning that led you there. You must also make a clear distinction between your own ideas and those obtained from other

Page | 36 sources. All sources of information (data, theories, ideas, figures, tables, etc.) must be acknowledged. This means citing the name of the author(s) and date at the appropriate place in the text and giving a list of references at the end of the essay using the correct format. Refer to published papers for this format. Avoid use of direct quotations but, if used, they should be within quotation marks and properly acknowledged. Use your own words, and do not plagiarise (see the “Essential Guide”). Cite only references that you have read yourself. It is dangerous to cite a reference if you have only read the abstract as this may not give the full context of the research. References and bibliography

One of the objectives of this exercise is to give you experience in reading scientific journals and therefore your references should be articles published in the primary research journals. References to scientific books are acceptable, but they should be in the great minority. Desirable references for a statement are likely to include papers that describe any of the following: the first report of the phenomenon, the most recent report, the most comprehensive/compelling report, a recent high-quality review to function as an entry point to the literature. Undesirable references include papers that merely mention things in passing or in their own introduction/discussion, and sources such as National Geographic, Encyclopaedia Britannica, someone you once met in a pub, New Scientist, and most sites on the World Wide Web. None of these sources of information is reviewed by experts before being published, and therefore tend to be less reliable. Anyone can post information on the WWW and there is no useful assurance that it is authoritative. Articles published in research journals are usually refereed by at least two qualified experts before publication. If you are uncertain whether your reference material is appropriate, then ask! All references cited in the text must be listed at the end of the essay. Do not list references that you have not read or have not cited in the text. Try to cite references that explicitly address the point you are making, not references that only mention it in passing. Use a standard citation format, such as one that you have seen used in a paper; we recommend ‘Harvard’, i.e. in-text citations that appear as “(Author, Year)”. To save on typing, you may want to explore the use of a reference manager, such as Endnote online or ‘Zotero’ [https://www.zotero.org/]. Writing Style

Scientific writing is a skill that is learnt. The five key elements of scientific writing are: logic, precision, clarity, directness, and brevity. Make sure that your writing is well organised and has a logical structure. Write in complete paragraphs and make sure that each paragraph follows logically from the one before. Well-structured paragraphs normally have more than one sentence. Use a clear, concise, and precise style of writing. Avoid unnecessary jargon and avoid a journalistic style or idiomatic or ‘flowery’ language or metaphors, as these can overstate or exaggerate the case. Your reader wants to get maximum information for minimal effort, and they are not reading for idle entertainment. It is quite likely that English is not your first language, and for most technical writing a very high proportion of your audience will be working in a second language. Do not expect them to recognise colloquialisms or idioms; make it easy for someone who is reading your essay with a dictionary to hand.

Pay attention to detail. When reporting numerical values make sure that you use the correct notation and SI units, e.g., 20C not 68F. Make sure that your grammar is

Page | 37 correct. Most markers wince at over-enthusiastic use of semi-colons. Avoid run-on sentences—few sentences require more than 25 words. Check that all words are spelled correctly. The use of spell checkers can make this task easy, but the spell checker cannot identify whether you have used the correct word. There, their, and they’re, whether and weather, and which and witch are all spelled correctly but have quite different meanings. Beware of affect and effect, principle and principal, and avoid and viscous circles. Proof read your essay far enough in advance to allow time for corrections. Swapping essays with a friend for proof reading can be helpful and instructive, but they should NOT be someone answering the same essay question as you. Marking

The finished essay should be not more than prescribed limit and must be submitted to the BTO by the deadline. Late submission of the essay will be penalised by a loss of 5% of the mark awarded per calendar day. If there are extenuating circumstances, remember that you can apply for an extension in advance of the deadline. For further information on late penalties, extensions and special circumstances please refer to the essential guide.

Remember that the 3rd year is an intellectual step up from the 2nd year: each grade requires a greater level of skill and knowledge than it did previously. A good essay is 50-60, a very good essay 60-70, and an excellent essay is 70+. Marks in excess of 80 are unusual.

Marks will be awarded according to the common mark scheme. The following are all likely to play a part:

 evidence that you have a grasp of the whole subject and have assimilated critically the ideas and information from several different sources  evidence that you have understood the papers you have read  evidence that you have found and read a reasonable number of papers on the topic  a well-structured essay with a logical progression and clear message  correct citation and referencing of the literature  good style of writing: concise, precise and in good English.  evidence of original and critical thinking

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Further Information For further information, please refer to the “Essential Guide”.

Staff of the BTO BTO Course Email addresses Assessment Attendance Passing your courses Examination procedures Calculators Dictionaries Feedback Submission of assessed course work Penalties for late submission Extensions policy Special circumstances and extensions beyond 7 calendar days Common marking scheme Release of provisional & ratified examination marks Examination regulations Procedures for Progression Failure to pass Junior Honours Progression into Senior Honours Degree classification Plagiarism and copying Student support Your Personal Tutor BTO Student Support Team Senior Tutor Other sources of support The University of Edinburgh complaint procedure Careers Service Class Reps Data protection Democracy Disabled Students Email and Contacting You English language education EUSA student services Facilities available at King’s Buildings Institute for Academic Development Lab Coats Learn Maps and Transport Safety in the laboratory Safety Notes–Teaching Laboratories (King’s Buildings) Social Media guidance for students Student Administration Students on a Tier 4 Visa Study Skills Use of Animals in Teaching Use of barcode labels Useful contacts Telephone directory

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