Genome-wide association studies for complex traits: consensus, uncertainty and challenges

By Mark I. McCarthy, Gonçalo R. Abecasis, Lon R. Cardon, David B. Goldstein, J. Little, John P. A. Ioannidis, Joel Hirschhorn. Nature reviews genetics, 2008

3,055 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0f07c69cde4ee6f453fa22f449347b470395d53f

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Citations in the corpus

Identifying Causal Variants at Loci with Multiple Signals of Association (2014, background): “…al. 2013; Ripke et al. 2013), only a handful of causal genetic variants (i.e., variants that biologically alter disease risk) have been found (Altshuler et al. 2008; Manolio et al. 2008; McCarthy et al. 2008), thus prohibiting the mechanistic understanding of the genetic basis of common diseases.”
Case definitions, diagnostic algorithms, and priorities in encephalitis: consensus statement of the international encephalitis consortium. (2013, background): “While genomewide association studies circumvent many of these issues, adequate statistical power requires recruitment of hundreds or thousands of affected cases and exposed controls [61].”
HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants (2011, background): “Genome-wide association studies (GWAS) are providing a ﬂood of data associating genetic variants with common phenotypes (1).”
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, background): “Aside from its intrinsic interest, correctly accounting for population stratification is critical for the interpretation of association studies [108, 109], particularly for rare variants [110, 111].”
Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism (2010, methodology): “5) The global null is then tested by adjusting the significance level to account for the multiple comparisons, effectively implementing a Max test; see [33, 39] for example.”
DISEASES: Text mining and data integration of disease–gene associations (2014, background): “GWAS data are, however, non-trivial to work with for the nonexpert, because they identify marker SNPs that are often not the actual causal SNPs [30,31].”
The QTN Program and the Alleles That Matter for Evolution: All That’s Gold Does Not Glitter (2011, neutral): “…not merely the statistically significant ones, into estimation of genetic architectures and prediction of trait values (Schork 2001; Wray et al. 2007; Visscher 2009; Wei et al. 2009; de los Campos et al. 2010; Province and Borecki 2008) describe an analogous method as “gathering the gold dust”.”
Approximation to the Distribution of Fitness Effects across Functional Categories in Human Segregating Polymorphisms (2014, neutral): “For instance, genome-wide association studies (GWAS) are searching for mutations 45 associated with disease in large samples of humans [1].”
Genotype Imputation (2009, background): “These quality filters typically flag markers that have low call rates, significant evidence for deviations from Hardy-Weinberg equilibrium, a large rate of discrepancies between duplicate genotypes, or evidence for non-Mendelian inheritance (67).”