Results 311 to 320 of about 674,070 (352)

Genome-Wide Association Studies

2010
Genome-wide association (GWA) studies are best understood as an extension of candidate gene association studies, scaled up to cover hundreds of thousands of markers across the genome in samples usually of several thousand cases and controls. The GWA approach allows the detection of much smaller effect sizes than with previous linkage-based genome-wide ...
Paola Sebastiani, Nadia Solovieff
  +5 more sources

Genome-Wide Association Studies (GWAS)

2023
Most of the breeding targets are quantitative traits. In exploring the quantitative trait locus (QTL) system of a trait, linkage mapping was established using sparse polymerase chain reaction (PCR) markers. With the genome-wide sequencing technology advanced, genome-wide association study (GWAS) was developed for natural (germplasm) populations using ...
Jianbo, He, Junyi, Gai
openaire   +2 more sources

Genome-Wide Association Studies

2012
A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled ...
Tun-Hsiang, Yang, Mark, Kon, Charles, DeLisi   +2 more
openaire   +3 more sources

Family-Based Genome-Wide Association Studies

Pharmacogenomics, 2009
In the last 2 years, the effort to identify genes affecting common diseases and complex traits has been accelerated through the use of genome-wide association studies (GWAS). The availability of existing large collections of linkage data paved the way for the use of family-based GWAS.
Benyamin, Beben, Visscher, Peter M., McRae, Allan F.   +2 more
openaire   +4 more sources

Genome-Wide Association Studies

Cold Spring Harbor Protocols, 2009
INTRODUCTIONThe goal of association studies is to discover genetic variation that differs in frequency between cases and controls or between individuals with different phenotypic values. Until a few years ago, the only method available for such studies was low-throughput analysis in which a single gene was selected and either genotyped for known ...
openaire   +2 more sources

Genome-Wide Association Studies

2018
Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach.
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Genome-Wide Association Studies of Cancer

Future Oncology, 2007
Genome-wide association studies provide a new and powerful approach to investigate the effect of inherited genetic variation on the risk of human disease. These studies rely on high throughput DNA microarray technology to genotype hundreds of thousands of genetic variants across the human genome.
Eric, Jorgenson, John S, Witte
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[Genome-wide association studies].

Ugeskrift for laeger, 2008
Within the last year, genome-wide association (GWA) studies have identified a large number of robust associations between genetic variants and common diseases. Two key premises underlie this burst of discovery. First, the HapMap project has provided a catalogue of human genetic variation.
Bjarke, Feenstra, Heather Allison, Boyd, Mads, Melbye   +2 more
openaire   +1 more source

[Genome-wide association studies].

Deutsche medizinische Wochenschrift (1946), 2011
Genome-wide association studies (GWAS) are aimed to identify genetic markers of complex human diseases and individual traits. In this context more than 150 gene loci have been found to be associated with about 60 different diseases and personal characteristics.
D, Grimm, H E, Blum, R, Thimme
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R for Genome-Wide Association Studies

2013
In recent years R has become de facto statistical programming language of choice for statisticians and it is also arguably the most widely used generic environment for analysis of high-throughput genomic data. In this chapter we discuss some approaches to improve performance of R when working with large SNP datasets.
Gondro, Cedric, Porto-Neto, Laercio R., Lee, Seung Hwan   +2 more
openaire   +4 more sources