Structured GWAS data

Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations.

11p vinarcissa 21-03-2023 5 1   Download

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome.

18p vihagrid 30-01-2023 9 3   Download

The primary goal of genome-wide association studies (GWAS) is to discover genes or variants associated with complex diseases. Most GWAS use single nucleotide polymorphism (SNP) approaches that mainly focused on assessing the association between each individual SNP and disease; therefore they cannot take into account the combinations of SNPs.

3p vipagani 20-10-2022 4 1   Download

In recent years genetic data analysis has seen a rapid increase in the scale of data to be analyzed. Schadt et al (NRG 11:647–657, 2010) offered that with data sets approaching the petabyte scale, data related challenges such as formatting, management, and transfer are increasingly important topics which need to be addressed.

7p viwyoming2711 16-12-2020 9 2   Download

The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies.

11p vikentucky2711 24-11-2020 10 1   Download