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Genome-wide association studies: quality control and population-based measures
1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Germany. ziegler@imbs.uni-luebeck.de
Genetic Epidemiology
|November 20, 2009
Summary
Genome-wide association studies identify disease genes using millions of genetic markers. This work addresses computational and statistical challenges in genetic analysis, improving accuracy and efficiency for disease susceptibility research.
Area of Science:
- Genetics and Bioinformatics
- Statistical Genomics
Background:
- Genome-wide association studies (GWAS) are standard for identifying disease susceptibility genes using numerous single-nucleotide polymorphism (SNP) markers.
- Technological advancements in GWAS present significant computational and statistical challenges.
Framework:
- Focus on addressing computational challenges in memory management and statistical procedure speed.
- Exploration of efficient SNP storage methods for large datasets.
- Evaluation of genotype calling algorithms for accuracy and speed.
Implementation:
- Introduction of novel statistical quality control procedures.
- Assessment of quality control's impact on genotype imputation accuracy.
- Discussion of diagnostic accuracy measures, including area under the curve (AUC), post-GWAS.
Implications:
- Improved methods for handling large-scale genetic data.
- Enhanced accuracy in identifying genetic variants associated with diseases.
- Methods for determining population-attributable risk from genetic variants.
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