Using Mixed Models in GWA studies
|Submitted:||Friday 6th of July 2012 11:06:36 AM|
|Content type:||Learning resource|
|Educational levels:||qc2, qc3|
- Genetics + genomics
- Clinical/medical genetics
- Statistical genetics
- Statistical genetics > Genetic epidemiology > Gene discovery > Genome-wide
- Molecular genetics > Techniques > Array based/high throughput technologies
- Molecular genetics > Techniques > Molecular techniques > Sequencing
- Molecular genetics > Techniques > Molecular techniques > Omics
AbstractCurrent biomedical research is experiencing a large boost in the amount of data generated. Individual genomes are being characterized at increased level of details using single nucleotide polymorphism (SNP) arrays, and, more recently, exome and whole-genome re-sequencing (WGRS). At the same time, technologies for high-throughput characterization of tens of thousands of molecular “omics” phenotypes in thousands of people are becoming increasingly affordable. The genetic analysis of highly dimensional, inter-correlated "omics" data is challenging both methodologically and computationally. In this talk, I will review problems arising in genetic analysis of "omics" data and will describe possible solutions of some of these problems.
This resource is part of the following learning package:
Y. Aulchenko. Using Mixed Models in GWA studies. EUROGENE portal. July 2012. online: http://eurogene.open.ac.uk/content/using-mixed-models-gwa-studies
Keywordsassociation, correlation, covariance, fixed, genome, genome wide association, genotype, heritability, identity by descent, inherited, long interspersed nuclear element, merlin, mixed model, multivariate, pedigree, phenotype, polygenic, population, population isolate, regression, sib, single nucleotide polymorphism, test statistics, trait, variance
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