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=Genetic Association Course:='''with application to sequence and genotype data''' September 16-20, 2024<br /> Max Delbrück Center (MDC) for Molecular Medicine<br /> 

The eleventh annual Berlin Genetic Association Course will be held at the [https://www.mdc-berlin.de/ MDC] in Berlin from September 16-20, 2024. The goal of the course is to teach course participants both the theory and application of methods for population-based association analysis, with a concentration on the analysis of human exome and whole-genome sequence and array genotype array data.

The cost of the 5-day course is 975 EUR for researchers from an academic institution, and 1,950 EUR for individuals from private (for -profit) companies. This fee covers tuition, Monday evening course dinner, and course-related expenses (handouts, etc.) but not room, board, or meals. Housing is available for course participants at nearby hotels.

The emphasis of this course is on strategies for the genetic mapping of complex human traits. It will include theory as well as practical exercises. The exercises will be carried out using a variety of computer programs (ANNOVAR, FAST-GWA, bcftools/vcftools, LDPhred2, PLINK, R, REGENIE, and LDPhred2REGENIE) and with pencil and paper. The emphasis of this course is on the analysis of population-based whole genome and exome sequence data, and genotype array data and imputed genotype data. TOPICS include: Association association analysis of qualitative and quantitative traits; single marker and rare variant aggregate analysis; analysis of whole-genome association study data; complex trait rare variant association analysis of next-generation sequence data; data quality control for genotype and next-generation sequence data; haplotype reconstruction; controlling population admixture (genomic control, multi-dimensional scaling, principal components analysis, etc); estimation of heritability and genetic correlations, generalized linear mixed models and linear mixed models, Mendelian Randomization, polygenic risk scores; imputing and analysis of imputed genotype data; detecting gene x gene and gene x environmental interactions; power and sample size estimation for both genotype and rare variant data; permutation (estimating empirical p-values); and false discovery rate (FDR).