Genome-wide Association Studies

By John R. Shaffer, Eleanor Feingold, M. Marazita. 2012

931 citations recorded by the source corpus.

How researchers see this paper

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

In Genetics

In Molecular Biology

Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “We refer the reader to reviews by Bush et al. [70] and Wei et al. [65] for more details.”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Genetic information is known to be heterogeneous among populations of individuals but most methods assume that they share the same genome 187 . scGET-seq 188 , a technology that jointly profiles the genome and chromatin accessibility, has the potential to aid the inference of causal GRNs by testing…”
Bringing the Animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services (2021, background): “In the meantime, continued discovery of single nucleotide polymorphisms (SNPs) has enabled the generation of highthroughput genotype information from different SNP chip platforms and production of massive genotype–phenotype data from association studies, trait correlation analysis, and heritability ”
Application of t-SNE to Human Genetic Data (2017, background): “Genome-wide association study (GWAS) [1, 2, 3, 4, 5, 6] is an approach to type common genetic variants in a general human population, and by comparing the allele frequency difference between a group of patients (cases) and a group of normal people (controls), discover statistical association signals”
High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform (2011, background): “Recently, next-generation sequencing at 19 coverage across 517 rice varieties has identified over 3.6 million SNPs, of which 167,000 were found in coding regions (Huang et al. 2010).”
Genetic mapping of quantitative trait loci in crops (2017, methodology): “Although random-effects models have been used to deal with this problem [96], some studies with high heterogeneity should be removed from a metaanalysis [97].”
High-Throughput SNP Genotyping to Accelerate Crop Improvement (2014, background): “13X sequence coverage for mapping populations and approximately 1X sequence coverage for diverse germplasm (Huang et al. 2009; Huang et al. 2010; Xu et al. 2010; Huang et al. 2012).”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, background): “Although diverse ancestry groups only account for about 5% of this data, that fraction amounts to over 35,000 samples of non-European and admixed ancestry (Bycroft et al., 2018) and yet only 7.”
Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review (2019, background): “GWAS generally highlight linkage among SNPs and traits and based on GWAS design, genotyping tools, statistical models for examination, and results interpretation [145].”