Introduction to the statistical analysis of genome-wide association studies
Attendance dates:July 2021: 05, 06, 07, 08, 09
Time commitment: 5 days
- Inga Prokopenko
- Course leader
This course will enable human geneticists to analyse their large-scale genetic data using standard analytical approaches and freely available software tools. It will cover statistical backgrounds for association studies; a primer on scripting in the most frequently used computational environments, design and analysis of such studies, interpretation of the results.
Who is this course for?
This course is for geneticists facing the need to analyse from small to large-scale human genotyping data in relation to their effect on common human traits and diseases.
Scientists and students in training aiming to undertake SNP-based association analyses, genome-wide association studies and their meta-analyses.
Researchers willing to understand better the statistical approaches and analytical procedures for the genetic association studies.
On successful completion of this course, you will be able to:
- Undertake quality control, imputation and analysis of genome-wide data using standard statistical approaches and software tools
- Complete meta-analysis of genome-wide association studies and analysis of rare variants
- Evaluate critically the results of association analysis and visualise them
- Understand the study design and findings for publications in peer-reviewed journals.
Three guest lectures will be given by renowned scientists in the field of human genetics.
Indicative content includes, some or all of the following topics:
- Introduction to statistics for geneticists
- Introduction to Linux and R
- Genome-wide association studies (GWAS)
- Quality Control (QC) for GWAS
- Association analysis
- Population structure
- Imputation of GWAS
- Meta-analysis of GWAS
- Analysis of rare variants
- Genetic risk scores
- Mendelian randomisation.
The final programme and timings will be given to applicants prior to the course starting.
Take a look at our example timetable (PDF) to get an idea of timings and the structure of the week.
Introduction to statistics for geneticists - Prof Inga Prokopenko and Dr Marika Kaakinen
Basics of probability theory, binomial and normal distribution, polygenic inheritance and complex traits, allele frequencies in population, Hardy-Weinberg equilibrium.
Introduction to Linux and R – Dr. Ayse Demirkan
Interface, command line and basic commands, functions, text editors, saving commands in scripts and running scripts, installing software tools for statistical analysis of genetic data, versions, data storage. Linux as environment for PLINK software tool. Basics of R usage to run graphical tools for genome-wide data and analysis results.
Introduction to genome-wise association studies (GWAS) – Dr. Marika Kaakinen
Principles of linkage disequilibrium (LD) and SNP tagging for genome-wide genotyping array design, analysis and imputation; haplotypes, study design, sample size and statistical power, use UCSC browser and NHGRI GWAS catalog.
Quality Control (QC) for GWAS – Prof Reedik Mägi
Sample and variant QC :on individuals (samples) for missingness, gender checks, duplicates and cryptic relatedness, population outliers, heterozygosity and inbreeding; and on SNPs for missingness, minor allele frequency and Hardy-Weinberg equilibrium.
Invited speaker session
This slot will be filled by one of our renowned scientist guest lecturers in the field of human genetics.
Statistical models for genetic association studies – Prof Krista Fischer
linear and logistic regression, additive genetic model, test significance, type I error and multiple testing.
Association analysis - Prof Inga Prokopenko
Analyses of data using PLINK software, including genetic models used for statistical analysis, covariates and adjustments, basic types of single-variant analyses, graphical representation of the output results
Population structure – Prof Andrew P. Morris
Identification of population outliers in GWAS and methods for detecting and accounting for structure within populations. Use of PLINK for principal components analysis and association analysis adjusting for structure.
Imputation of GWAS - Prof Inga Prokopenko
GWAS reference panels, including HapMap and 1000 Genomes Projects, reference haplotypes, imputation with IMPUTE software, phasing and imputation steps, chromosome chunks, combining chinks for imputed data analysis, quality of imputation, imputed genotypes probability.
Invited speaker session
This slot will be filled by one of our renowned scientist guest lecturers in the field of human genetics.
Meta-analysis of GWAS - Prof Andrew P. Morris
Combining association summary statistics across GWAS using fixed-and random-effects meta-analysis. GWAMA software to perform meta-analysis.
Genetic risk scores, Mendelian Randomisation - Prof Krista Fischer
Weighted and unweighted genetic risk scores. Dissecting causal relationships between exposures and complex traits using Mendelian Randomization. Instrumental variable approach.
Invited speaker and Q&A session
This slot will be filled by one of our renowned scientist guest lecturers in the field of human genetics and the Q&A will be held by all the course leaders.
Analysis of rare variants – Prof Andrew P. Morris
Rationale for rare variant analysis. Methods for assaying rare variation. Methods for the analysis of rare variants. GRANVIL software for testing association with rare variants.
There will be no assessment on this course, however, you will receive a course certificate provided you attend over 80 per cent of the taught course.
The Wellcome Trust Case Control Consortium., Management Committee., Burton, P. et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., & Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews. Genetics, 9(5), 356–369.
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American journal of human genetics, 101(1), 5–22.
Balding D. J. (2006). A tutorial on statistical methods for population association studies. Nature reviews. Genetics, 7(10), 781–791.
Hirschhorn, J. N., & Daly, M. J. (2005). Genome-wide association studies for common diseases and complex traits. Nature reviews. Genetics, 6(2), 95–108.
Winkler, T. W., Day, F. R., Croteau-Chonka, D. C., Wood, A. R., Locke, A. E., Mägi, R., Ferreira, T., Fall, T., Graff, M., Justice, A. E., Luan, J., Gustafsson, S., Randall, J. C., Vedantam, S., Workalemahu, T., Kilpeläinen, T. O., Scherag, A., Esko, T., Kutalik, Z., Heid, I. M., … Genetic Investigation of Anthropometric Traits (GIANT) Consortium (2014). Quality control and conduct of genome-wide association meta-analyses. Nature protocols, 9(5), 1192–1212.
Holland, D., Fan, C., Frei, O., Shadrin, A., Smeland, O., Sundar, V., Andreassen, O. and Dale, A., (2020). Estimating Inflation In GWAS Summary Statistics Due To Variance Distortion From Cryptic Relatedness. BioRxiv 164939.
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., Cho, J. H., Guttmacher, A. E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C. N., Slatkin, M., Valle, D., Whittemore, A. S., Boehnke, M., … Visscher, P. M. (2009). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753.
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., & Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature genetics, 50(9), 1219–1224.
McCarthy, S., Das, S., Kretzschmar, W., Delaneau, O., Wood, A. R., Teumer, A., Kang, H. M., Fuchsberger, C., Danecek, P., Sharp, K., Luo, Y., Sidore, C., Kwong, A., Timpson, N., Koskinen, S., Vrieze, S., Scott, L. J., Zhang, H., Mahajan, A., Veldink, J., … Haplotype Reference Consortium (2016). A reference panel of 64,976 haplotypes for genotype imputation. Nature genetics, 48(10), 1279–1283.
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 169(7), 1177–1186.
A limited number of European Society of Human Genetics (ESHG) scholarships are available for young scientists in training from European countries.
Preference will be given to ESHG members and researchers from ESHG-specified countries which are disadvantaged economically (see a full list of qualifying countries) although scholarships are not limited these groups. Scholarships will cover the full attendance fee only, beneficiaries would still need to pay for their own travel and subsistence needs.
There is no extra application for these scholarships, we will let successful applicants on the course know if they are receiving the funding.
There are no prerequisites required to do this course.
Recognition of prior learning
Applicants should understand basic genetic principles such as modes of inheritance, DNA and gene structure, SNPs and other genetic variants, principles of crossing over and recombination, concepts of heritability and penetrance.
Additionally, knowledge of basic statistical tests and some command line scripting skills would be an advantage.
Fees and funding
Price per person:
£700Fee for academics
£900Fee for non-academics
Payment information will be provided to applicants after their registration has been verified.
A limited number of the European Society of Human Genetics (ESHG) -funded scholarships are available for young scientists in training from European countries. Preference will be given to ESHG members and researchers from countries which are disadvantaged economically, although scholarships are not limited to these groups. Scholarships will cover the full attendance fee only.
Deadline for applications for Fellowships: 11 June 2021.
How to apply
There are only 50 places available on this course, so we will inform you if your application has been successful after the deadline has passed.
Please download and fill in the application form, making sure that you have completed all sections and answered all questions, uncompleted forms may result in delays and ultimately missing out on available places.
Please send back the following to us at the email included on the form:
- Application form
- A curriculum vitae (résumé) in English, including work and research experience
- A letter of motivation in English (max. 500 words)
- A signed letter of recommendation from a supervisor.
* The application documents should demonstrate with examples, in which of the following subjects, among human genetics, statistics, and programming in Unix/Linux/R, you have knowledge or experience.
* When applying for Fellowship, the application documents should contain a respective justification.
Application deadline for this course is Monday 11 June 2021. Deadline for late applications Monday 28 June 2021. Late applications will be considered with £200 increase in fees.
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