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Big data challenges in bone research: genome-wide association studies and next-generation sequencing
Nerea Alonso1, Gavin Lucas2, Pirro Hysi3
1Rheumatic Diseases Unit, MRC Institute of Genetics and Molecular Medicine, Centre for Genomic and Experimental Medicine, Western General Hospital, University of Edinburgh , Edinburgh, UK.
Bonekey Reports
|February 25, 2015
Summary
Genome-wide association studies (GWAS) identify disease-related genetic loci. Protocols ensure data quality for GWAS, genotype imputation, meta-analysis, and next-generation sequencing (NGS) for robust genetic discoveries.
Area of Science:
- Genetics
- Genomics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) are crucial for identifying genetic loci linked to complex diseases.
- Advances in genotyping, large-scale projects (HapMap, 1000G), and collaborative genetics have accelerated progress.
- Strict protocols are essential for data quality and analysis integrity in collaborative GWAS.
Purpose of the Study:
- To discuss essential protocols for ensuring data quality and analysis in genome-wide association studies (GWAS).
- To highlight the role of quality control, genotype imputation, and meta-analysis in enhancing GWAS power.
- To address the integration and interpretation of next-generation sequencing (NGS) data within the GWAS framework.
Main Methods:
- Application of quality control analyses to individual samples and single-nucleotide polymorphisms (SNPs).
- Utilization of software packages like PLINK for comprehensive quality control testing.
- Employing genotype imputation to investigate associations at non-genotyped markers.
- Performing meta-analysis to combine results from individual GWAS.
Main Results:
- PLINK software facilitates essential quality control for GWAS data.
- Genotype imputation increases the power of GWAS by enabling analysis of ungenotyped markers.
- Meta-analysis effectively combines results from multiple GWAS.
- Next-generation sequencing (NGS) offers broader marker analysis but requires careful interpretation.
Conclusions:
- Adherence to strict protocols is vital for reliable GWAS data quality and analysis.
- Genotype imputation and meta-analysis are key strategies for maximizing GWAS utility.
- NGS provides a high-throughput alternative but necessitates cautious interpretation of biological relevance.
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