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This course is entitled to teach the main concepts of genomic data analysis using real data from two of the most important international projects describing human genetic variation: The HapMap and the 1000 Genomes Projects. In this course you will get familiar with general results emerged in these two projects, focusing in the genomic information displayed in the Ensemble version of 1000 Genomes Project Browser; but not in the transcriptomic and proteomic applications. You will also learn how to manage and deal with huge genetic datasets and which strategies and analysis are used to answer genetic, demographic and evolutionary questions. The course will alternate theory with practical computer exercises but it will focus on hands-on training.
Although examples will be based on single-nucleotide polymorphism (SNP) data in human individuals, most topics covered in this course can be extended to other types of markers and organisms. Basic use of the R statistical package and command-line based environments will be introduced in the course and previous knowledge is not required.
Dr. Marc Via
(Universitat de Barcelona, Spain).
Dr. Robert Carreras-Torres
(International Agency for Research on Cancer, France).
Dr. Soledad De Esteban-Trivigno
(Transmitting Science, Spain).
Graduate or postgraduate degree in Biological Sciences, Medicine or Anthropology, basic knowledge in genetics and personal computers. All participants must bring their own personal laptop (Windows, Macintosh, Linux).
Basic genetic theory: Genetic polymorphisms; allele frequencies; Hardy-Weinberg equilibrium; linkage disequilibrium (LD); LD measurements; haplotypes; tag SNPs.
Visualization of LD and Tag SNPs (HaploView).
Introduction to the International HapMap and the 1000 Genomes Projects: Phases, data generated and main results.
Navigating the HapMap and 1000G Browsers: Search regions of interest; visualize LD patterns; add custom data onto browser plots; download sets of SNPs and indels; create images and reports;…
Bulk data download and file formats (PED, MAP and DAT). Summary statistics in genetics using PLINK and visualization using R.
Data Management: Conceptual aspects of Quality Control; merging datasets; removing sets of SNPs / individuals; pruning based on LD;…
Detecting structure in your genetic data: Genetic distances; identity-by-descent (IBD); detection of cryptic relatives; outlier detection; multidimensional scaling (MDS); admixture analysis (quantification of ancestry).
Phasing genetic data with MACH software and generating haplotype reference datasets.
Genotype imputation: Practical considerations and quality control. Strategies for admixed populations.
Association analyses: Complex phenotypes; association tests; corrections for multiple comparisons (Bonferroni and permutation tests) and for population stratification (genomic-control, clustering, or admixture adjustments).
Other databases and resources.
- The International HapMap 3 Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467: 52-58.
- Via M, Gignoux C, González Burchard E (2010) The 1000 Genomes Project: new opportunities for research and social challenges. Genome Medicine, 2: 1-3.
- The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature, 491: 56-65.
- Fejerman L, Chen GK, Eng C, Huntsman C, Hu D, Williams A, Pasaniuc B, John EM, Via M, Gignoux C, Ingles S, Monroe KR, Kolonel KL, Torres-Mejía G, Pérez-Stable EJ, González Burchard E, Henderson BE, Haiman CA and Ziv E (2012) Admixture mapping identifies a locus on 6q25 associated with breast cancer risk in US Latinas. Human Molecular Genetics, 21 (8): 1907-1917.
You will find below some testimonials from former participants to previous editions of this course:
“Not being myself involved in human research, I was a bit concerned as to whether the course would provide useful information for application to my favourite, non-model organisms (benthic invertebrates). I was happy to discover that the course provided a very good methodological overview and that we learned to use basic programs applicable to all kind of organisms. The teachers were great and beared nicely with our non-specialist naive questions. I recommend the course to everyone interested in applications of SNP data, no need to be a computer wizard!”
Dr. Xavier Turon, Centro de Estudios Avanzados de Blanes - CSIC, Spain (2nd Edition).
For further information contact: firstname.lastname@example.org.
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