Common vs. Rare Allele Hypotheses for Complex Diseases
By Nicholas J. Schork, Sarah S. Murray, Kelly A. Frazer, Eric J. Topol. Current Opinion in Genetics and Development, 2009
694 citations recorded by the source corpus.
There has been growing debate over the nature of the genetic contribution to individual susceptibility to common complex diseases such as diabetes, osteoporosis, and cancer. The ‘Common Disease, Common Variant (CDCV)’ hypothesis argues that genetic variations with appreciable frequency in the population at large, but relatively low ‘penetrance’ (or the probability that a carrier of the relevant variants will express the disease), are the major contributors to genetic susceptibility to common diseases. The ‘Common Disease, Rare Variant (CDRV)’ hypothesis, on the other hand, argues that multiple rare DNA sequence variations, each with relatively high penetrance, are the major contributors to genetic susceptibility to common diseases. Both hypotheses have their place in current research efforts.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/01cc1309344e1a4796344bc4cde30dc21c4a73ef
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Citations in the corpus
- Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. (2012, background): “23, 24, 25 A comparison of the burden statistic QB in (4) and the SKAT statistic QS in (6) shows that the burden test aggregates the variants first before performing regression, while SKAT aggregates individual variant test statistics.”
- The Next-Generation Sequencing Revolution and Its Impact on Genomics (2013, neutral): “GWAS data strongly suggest that the vast majority of the heritability of complex traits will not be due to a few common variants with low to moderate effects (Schork et al., 2009).”
- Consanguinity, human evolution, and complex diseases (2010, neutral): “, whether the common disease/common variant or the common disease/rare variant hypothesis is more applicable (58), with the role of copy number Adult-onset diseases”
- Rare-variant association testing for sequencing data with the sequence kernel association test. (2011, background): “Rare genetic variants, here defined as alleles with a frequency less than 1%–5%, can play key roles in influencing complex disease and traits.(11) However, standard methods used to test for association with single common genetic variants are underpowered for rare variants unless sample sizes or effe”
- Exome sequencing as a tool for Mendelian disease gene discovery (2011, neutral): “…end of the spectrum, genome-wide association studies (GWASs) have identified large numbers of loci that contribute to the genetic basis of complex traits but, in almost all cases, these loci collectively account for only a small fraction of the observed heritability of the trait under study 4,5 .”
- The impact of next-generation sequencing on genomics (2011, background): “Following completion of the HGP, a new approach, genome-wide association study (GWAS), was widely applied to genomics.”
- Genome-wide association studies in diverse populations (2010, background): “Rare variants, which have not been examined to the same extent, provide one possible genetic source for unexplained phenotypic variatio”
- Genome-wide association study of leaf architecture in the maize nested association mapping population (2011, background): “The relative importance of common versus rare variants is critical to understanding the basis of natural variation and its application in medicine, agriculture and other areas of biolog”
- Uncovering the roles of rare variants in common disease through whole-genome sequencing (2010, background): “Taken together, these observations support the long-established idea that rare variants could be the primary drivers of common disease”
- A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease (2015, positive): “Thus, this comprehensive analysis strongly supports the common disease–common variant hypothesis 43 , given that it was powered to detect variants with MAF <0.05 having OR >1.5.”