Chapter 11: Genome-Wide Association Studies
By William S. Bush, Jason H. Moore. PLoS Comput. Biol., 2012
1,101 citations recorded by the source corpus.
Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/05043cc44c382c4c1e5a0cddc313d4c7120cd646
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- James D. Malley
- Angeline S. Andrew
- Margaret R. Karagas
- Jing Li
- Jason H. Moore
- Marylyn DeRiggi Ritchie
In Molecular Biology
- James D. Malley
- Angeline S. Andrew
- Margaret R. Karagas
- Jing Li
- Jason H. Moore
- Mitchell J. Machiela
- Taeho C. Jo
- Stephen J. Chanock
Citations in the corpus
- LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations (2020, background): “Linkage disequilibrium (LD) is a population-based parameter that describes the degree to which an allele of one genetic variant is inherited or correlated with an allele of a nearby genetic variant within a given population (Bush and Moore, 2012).”
- Detecting gene-gene interactions using a permutation-based random forest method (2016, background): “genes and environment factors when mapping the relationship between genotypes and phenotypes [4, 6, 7].”
- Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data (2019, background): “This is now widely recognized in genetics (König, 2011; Bush and Moore, 2012) and other fields but has been slow to penetrate deep learning studies employing neuroimaging data.”