ArticleGenetic epidemiology2011
Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrative Analysis of Germline Rare Variants in Clear and Non-clear Cell Renal Cell Carcinoma.European urology open science · 2024Article
- Taking population stratification into account by local permutations in rare-variant association studies on small samples.Genetic epidemiology · 2021Article
- The impact of a fine-scale population stratification on rare variant association test results.PloS one · 2018Article
- A clustering approach to identify rare variants associated with hypertension.BMC proceedings · 2016Article
- Permutation testing in the presence of polygenic variation.Genetic epidemiology · 2015Article
- Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18.Genetic epidemiology · 2014Article
- A geometric framework for evaluating rare variant tests of association.Genetic epidemiology · 2013Article
- Marbled inflation from population structure in gene-based association studies with rare variants.Genetic epidemiology · 2013Article
- Fine-scale patterns of population stratification confound rare variant association tests.PloS one · 2013Article
- Adjustment for population stratification via principal components in association analysis of rare variants.Genetic epidemiology · 2013Article
- Assessing the impact of differential genotyping errors on rare variant tests of association.PloS one · 2013Article
- Association between a multi-locus genetic risk score and inflammatory bowel disease.Bioinformatics and biology insights · 2013Article
- The value of extended pedigrees for next-generation analysis of complex disease in the rhesus macaque.ILAR journal · 2013Review
- Identification of genetic association of multiple rare variants using collapsing methods.Genetic epidemiology · 2011Article
- Multiple testing in high-throughput sequence data: experiences from Group 8 of Genetic Analysis Workshop 17.Genetic epidemiology · 2011Article
- Incorporating biological information into association studies of sequencing data.Genetic epidemiology · 2011Article
- Statistical analysis of rare sequence variants: an overview of collapsing methods.Genetic epidemiology · 2011Review
- Lessons learned from Genetic Analysis Workshop 17: transitioning from genome-wide association studies to whole-genome statistical genetic analysis.Genetic epidemiology · 2011Article
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Authors and funding
6 authors.
Funding
Abstract
As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype-phenotype association in next-generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next-generation sequencing data: type I error and false-positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long-range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes (e.g., nearly 10,000 of 25,000 markers were private variants, and the sample size was small [n = 697]) and nonrandom causes (more correlation was observed than was expected by random chance). Principal components analysis was used to control for population structure and helped to minimize type I errors, but this was at the expense of identifying fewer causal variants. A novel multiple regression approach showed promise to handle correlation between markers. Further work is needed, first, to identify best practices for the control of type I errors in the analysis of sequencing data and then to explore and compare the many promising new aggregating approaches for identifying markers associated with disease phenotypes.
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