ArticleMethods in molecular biology (Clifton, N.J.)2012
Detecting rare variants.
Article in Methods in molecular biology (Clifton, N.J.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies.Frontiers in genetics · 2021Article
- How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity.Biogerontology · 2016Review
- Genome-wide association mapping revealed a diverse genetic basis of seed dormancy across subpopulations in rice (Oryza sativa L.).BMC genetics · 2016Article
- The advantages and limitations of trait analysis with GWAS: a review.Plant methods · 2013Review
- Some surprising twists on the road to discovering the contribution of rare variants to complex diseases.Human heredity · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The limitations of genome-wide association (GWA) studies that are based on the common disease common variants (CDCV) hypothesis have motivated geneticists to test the hypothesis that rare variants contribute to the variation of common diseases, i.e., common disease/rare variants (CDRV). The newly developed high-throughput sequencing technologies have made the studies of rare variants practicable. Statistical approaches to test associations between a phenotype and rare variants are quickly developing. The central idea of these methods is to test a set of rare variants in a defined region or regions by collapsing or aggregating rare variants, thereby improving the statistical power. In this chapter, we introduce these methods as well as their applications in practice.
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