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Quality control issues and the identification of rare functional variants with next-generation sequencing data
- Source :
- Genetic Epidemiology. 35:S22-S28
- Publication Year :
- 2011
- Publisher :
- Wiley, 2011.
-
Abstract
- Next-generation sequencing of large numbers of individuals presents challenges in data preparation, quality control, and statistical analysis because of the rarity of the variants. The Genetic Analysis Workshop 17 (GAW17) data provide an opportunity to survey existing methods and compare these methods with novel ones. Specifically, the GAW17 Group 2 contributors investigate existing and newly proposed methods and study design strategies to identify rare variants, predict functional variants, and/or examine quality control. We introduce the eight Group 2 papers, summarize their approaches, and discuss their strengths and weaknesses. For these investigations, some groups used only the genotype data, whereas others also used the simulated phenotype data. Although the eight Group 2 contributions covered a wide variety of topics under the general idea of identifying rare variants, they can be grouped into three broad categories according to their common research interests: functionality of variants and quality control issues, family-based analyses, and association analyses of unrelated individuals. The aims of the first subgroup were quite different. These were population structure analyses that used rare variants to predict functionality and examine the accuracy of genotype calls. The aims of the family-based analyses were to select which families should be sequenced and to identify high-risk pedigrees; the aim of the association analyses was to identify variants or genes with regression-based methods. However, power to detect associations was low in all three association studies. Thus this work shows opportunities for incorporating rare variants into the genetic and statistical analyses of common diseases.
- Subjects :
- Quality Control
Genetics
Molecular Epidemiology
Epidemiology
Genetic Variation
Regression analysis
Pedigree chart
Computational biology
Biology
Article
DNA sequencing
Human Genome Project
Genotype
Humans
Regression Analysis
Exome
Genetic Predisposition to Disease
Identification (biology)
1000 Genomes Project
Sequence Analysis
Algorithms
Genetics (clinical)
Genetic association
Subjects
Details
- ISSN :
- 07410395
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Genetic Epidemiology
- Accession number :
- edsair.doi.dedup.....356b7b72ba880f35bcb1e1a2fdebcc83