Estimating missing heritability for disease from genome-wide association studies.

By Sang Hong Lee, Naomi R. Wray, Michael E. Goddard, Peter M. Visscher. American Journal of Human Genetics, 2011

1,100 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a91c4687667f88d2471622b470b91204441ef7ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Genetics

Citations in the corpus

Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, background): “to account for the dichotomization of an underlying continuous liability distribution and over-sampling of cases relative to the population prevalence (Lee et al., 2011).”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, methodology): “The genetic effects bij are now taken to act on liability, and for small effects a linear transformation to the corresponding effect on the observed trait may be obtained as [30]”
Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, methodology): “For PVE estimation, one can directly apply BSLMM, treating the 1/0 case-control status as a quantitative outcome, and then apply a correction factor derived by [24] to transform this estimated PVE on the ‘‘observed scale’’ to an estimated PVE on a latent liability scale.”
Dissecting the genetics of complex traits using summary association statistics (2016, methodology): “The prediction R 2 for each method are given by 80,113 These equations can naturally be extended to linked markers (using the effective number of unlinked markers 104 ) and case-control traits (using observed-scale SNP-heritability 114 ).”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014, positive): “In recent genome-wide complex trait analyses ( “ GCTA ” analyses; Yang et al. 2011; Lee et al. 2011), Wray, Visscher and their colleagues have shown that GWAS data may account for a surprisingly high proportion of genetic variance in a trait, even when the individual predictive value of a given…”
MAGMA: Generalized Gene-Set Analysis of GWAS Data (2015, neutral): “However, despite growing sample sizes, the genetic variants discovered by GWAS generally account for only a fraction of the total heritability of a phenotype [2,3].”