The University of Manitoba
Department of Biochemistry and Medical Genetics
BGEN 7130 Genetic Epidemiology of Human Population (Winter 2016)
Instructors: Michelle Xiao-Qing Liu and Louise Simard
Phone: (204) 480-1363
Email: liu@umanitoba.ca
Credit hours: 3 (2 hours/week)
Time: Friday 10:30am – 12:30pm
Location: TBA
Office hour and location: by appointment at Room 807J, John Buhler Research Centre
Course Description
The purpose of this course is to introduce the students to genetic epidemiology, a
study of genetic factors in human diseases/traits and their interactions with environmental
factors. The focus will be on key concepts, principles, and their applications in mapping
the genetic loci/variants for human diseases/traits. For each of the sessions, the majority
of the class time will be spent on discussion of the papers that are related to a particular
topic listed in the course outline, while the remaining time will be spent on data analysis
methods using PLINK, a commonly used genome data analysis program, and other tools.
Prerequisites
This is a graduate course for students in graduate programs in genetics or related
disciplines as well as medical genetics residents and those in related specialties. The
students should have taken BGEN7090 ‘Principles and Practice of Human Genetics.’
Otherwise, the consent of instructor is needed.
Grading
Assignments and class participation
One term paper
Oral presentation
Final exam
40%
20%
10%
30%
Marks are
A+
90-100
A
80-89
B+
75-79
B
70-74
C+
65-69
C
60-64
A minimum grade of C+ is required to pass this course.
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Participation
Students must come to the class having read the assigned papers. Level and
quality of participation in class discussions will be graded.
Assignments
At the end of each session, a research paper or papers will be assigned to you.
You are required to prepare a 10 minutes presentation for each paper for the next session.
Your presentation should follow the guidelines in ‘Guide to Reading the Literature’
and/or ‘Methodologic Guidelines for Review Papers’ (which will be given to you on
January 8). You can choose to present using powerpoint slides or a white board. In
addition, there will be assignments for the data analysis component of this course.
Final exam
The final exam will cover material from the entire course to test your
understanding of the basic concepts, principles, and applications of this discipline. You
will be given short and long questions for which you are required to provide written
explanations. The exam will be one hour long. After you submit your exam, there will be
a discussion about the questions and their correct answers.
Term paper (due at 4pm on Thursday April 3)
The term paper will be on a topic of your own choosing after vetting by me. It
should be 10-12 pages (double spaced, Times Roman 12 or Arial 11 font) excluding the
references. References must be written in a standard journal format, e.g. for the American
Journal of Human Genetics. You can use a reference management software, such as
EndNote or Reference Manager, for this purpose.
Oral presentation
The presentations should be 20 minutes followed by discussion and questions.
You will present your topic in your term paper, lay the groundwork for the topic, and lead
the subsequent discussion.
Computer preparation
For the data analysis component of this course, you need to apply for an account
with WestGrid (part of Compute Canada) at www.westgrid.ca. For this application, you
will be asked to provide a sponsor’s Compute Canada Role Identifier (CCRI). My CCRI
is mxa-504-01. Next, you need to have a terminal program which allows you to
communicate between your computer and a remote server. You can download
MobaXterm from http://mobaxterm.mobatek.net for this. You could choose Hermes as
your primary system. You should be able to find many useful information about
WestGrid from their website. However, if you could not find the answer to your
questions, you can contact support@westgrid.ca for help.
Course textbooks
For basic concepts, you can refer to Chapters 13 to 16 of ‘Human Molecular
Genetics (4th edition)’ by Strachan and Read. For statistical methods, you can use ‘A
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Statistical Approach to Genetic Epidemiology (2nd edition)’ by Ziegler and König. One
copy of this book is reserved for this course at the NJM Health Sciences Library.
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Course outline
A course outline is provided below along with the sets of readings for each
session. The papers were selected to illustrate key points and the evolution of the
discipline. They are often grouped into themes to allow some in depth exploration of
different topics. Many of the articles are available online through the University of
Manitoba libraries electronic journals system. If you have difficulties finding these
articles, you can order them through Document Delivery or borrow mine for photocopy.
S1. January 8
1) Overview of genetic epidemiology
2) The HapMap data for the data analysis component of this course
S2. January 15
1) Familial aggregation
2) PLINK: missingness, minor allele frequency, sex check, Mendelian errors
Reading list:
1. Bartsch DK, Kress R, Sina-Frey M, Grutzmann R, Gerdes B, Pilarsky C, Heise JW,
Schulte KM, Colombo-Benkmann M, Schleicher C, et al. (2004) Prevalence of
familial pancreatic cancer in Germany. Int J Cancer 110: 902-906.
2. McGuffin P and Huckle P (1990) Simulation of Mendelism revisited: the recessive
gene for attending medical school. Am J Hum Genet 46: 994-999.
S3. January 22
1) Family studies
2) PLINK: Hardy-Weinberg equilibrium
Reading list:
1. Hallmayer, J., Cleveland, S., Torres, A., Phillips, J., Cohen, B., Torigoe, T., Miller, J.,
Fedele, A., Collins, J., Smith, K., et al. (2011) Genetic heritability and shared
environmental factors among twin pairs with autism. Arch Gen Psychiatry 68,
1095-1102.
2. van Dongen, J., Slagboom, P.E., Draisma, H.H., Martin, N.G., and Boomsma, D.I.
(2012) The continuing value of twin studies in the omics era. Nat Rev Genet 13,
640-653.
S4. January 29
1) Population stratification
2) PLINK: population stratification
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Reading list:
1. Hamer D and Sirota L (2000) Beware the chopsticks gene. Mol Psychiatry 5: 11-13.
2. Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC,
Altshuler D, Ardlie KG, and Hirschhorn JN (2005) Demonstrating stratification in
a European American population. Nat Genet 37: 868-872.
3. Skafidas, E., Testa, R., Zantomio, D., Chana, G., Everall, I.P., and Pantelis, C. (2012)
Predicting the diagnosis of autism spectrum disorder using gene pathway analysis.
Molecular psychiatry.
4. Belgard, T.G., Jankovic, I., Lowe, J.K., and Geschwind, D.H. (2013) Population
structure confounds autism genetic classifier. Molecular psychiatry.
S5. February 5
1) Genetic architecture/landscape for complex diseases
2) PLINK: linkage disequilibrium and strand problems
Reading list:
1. Morrison, A.C., Voorman, A., Johnson, A.D., Liu, X., Yu, J., Li, A., Muzny, D., Yu,
F., Rice, K., Zhu, C., et al. (2013) Whole-genome sequence-based analysis of
high-density lipoprotein cholesterol. Nat. Genet. 45, 899-901.
2. Huguet, G., Ey, E., and Bourgeron, T. (2013) The genetic landscapes of autism
spectrum disorders. Annu Rev Genomics Hum Genet 14, 191-213.
3. Blair, D.R., Lyttle, C.S., Mortensen, J.M., Bearden, C.F., Jensen, A.B., Khiabanian, H.,
Melamed, R., Rabadan, R., Bernstam, E.V., Brunak, S., et al. (2013) A
nondegenerate code of deleterious variants in Mendelian loci contributes to
complex disease risk. Cell 155, 70-80.
S6. February 12
1) Linkage and homozygosity mapping
2) PLINK: runs of homozygosity
Reading list:
1. Clerget-Darpoux F and Elston RC (2007) Are linkage analysis and the collection of
family data dead? Prospects for family studies in the age of genome-wide
association. Hum Hered 64: 91-96.
2. Miano MG, Jacobson SG, Carothers A, Hanson I, Teague P, Lovell J, Cideciyan AV,
Haider N, Stone EM, Sheffield VC, et al. (2000) Pitfalls in homozygosity
mapping. Am J Hum Genet 67: 1348-1351.
3. Woods CG, Cox J, Springell K, Hampshire DJ, Mohamed MD, McKibbin M, Stern R,
Raymond FL, Sandford R, Malik Sharif S, et al. (2006) Quantification of
homozygosity in consanguineous individuals with autosomal recessive disease.
Am J Hum Genet 78: 889-896.
S7. February 26
1) Modifiers for monogenic diseases/traits
2) PLINK: cryptic relationship
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Reading list:
1. Scriver CR and Waters PJ (1999) Monogenic traits are not simple: lessons from
phenylketonuria. Trends Genet 15: 267-272.
2. Cutting GR (2010) Modifier genes in Mendelian disorders: the example of cystic
fibrosis. Ann N Y Acad Sci 1214: 57-69.
S8. March 4
1) Population-based association and linkage studies
2) PLINK: association study methods for binary traits
Reading list:
1. The Wellcome Trust Case Control Consortium (2007) Genome-wide association study
of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:
661-678.
2. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar
P, de Bakker PI, Daly MJ, et al. (2007) PLINK: a tool set for whole-genome
association and population-based linkage analyses. Am J Hum Genet 81: 559-575.
S9. March 11
1) Missing heritability
2) PLINK: association study methods for quantitative traits
Reading list:
1. Maher B (2008) Personal genomes: The case of the missing heritability. Nature 456:
18-21.
2. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA,
Heath AC, Martin NG, Montgomery GW, et al. (2010) Common SNPs explain a
large proportion of the heritability for human height. Nat Genet 42: 565-569.
3. Visscher PM, Yang J, and Goddard ME (2010) A commentary on 'common SNPs
explain a large proportion of the heritability for human height' by Yang et al.
(2010). Twin Res Hum Genet 13: 517-524.
S10. March 18
1) New development: Sequencing and linkage studies
2) Gene and genetic variant annotation
Reading list:
1. Ott J, Wang J, and Leal SM (2015) Genetic linkage analysis in the age of wholegenome sequencing. Nat Rev Genet 16: 275-284.
2. Pabinger, S., Dander, A., Fischer, M., Snajder, R., Sperk, M., Efremova, M.,
Krabichler, B., Speicher, M.R., Zschocke, J., and Trajanoski, Z. (2014) A survey
of tools for variant analysis of next-generation genome sequencing data. Brief
Bioinform 15, 256-278.
3. Bloch-Zupan A, Jamet X, Etard C, Laugel V, Muller J, Geoffroy V, Strauss JP,
Pelletier V, Marion V, Poch O, et al. (2011) Homozygosity Mapping and
Candidate Prioritization Identify Mutations, Missed by Whole-Exome
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Sequencing, in SMOC2, Causing Major Dental Developmental Defects. Am J
Hum Genet 89: 773-781.
S11. April 1
1) New development: Gene expression and epigenetics
2) The Human Phenotype Ontology
Reading list:
1. Rung J and Brazma A (2013) Reuse of public genome-wide gene expression data. Nat
Rev Genet 14: 89-99.
2. Bell JT and Spector TD (2011) A twin approach to unraveling epigenetics. Trends
Genet 27: 116-125.
S12. April 8
Review of the course
S13. April 15
Presentation by the students
(term paper is due on Monday April 11 at 4pm)
S14. April 22
Final exam
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