| Literature DB >> 30139641 |
Gustavo de Los Campos1, Ana Ines Vazquez2, Stephen Hsu3, Louis Lello4.
Abstract
Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification of complex patterns in high-dimensional settings. However, training these highly parameterized methods requires very large data sets. Until recently, such data sets were not available. But the situation is changing rapidly as very large biomedical data sets comprising individual genotype-phenotype data for hundreds of thousands of individuals become available in public and private domains. We argue that the convergence of advances in methodology and the advent of Big Genomic Data will enable unprecedented improvements in complex-trait prediction; we review theory and evidence supporting our claim and discuss challenges and opportunities that Big Data will bring to complex-trait prediction. Published by Elsevier Ltd.Entities:
Keywords: Big Data; GWAS; SNP; complex traits; disease risk; prediction
Mesh:
Year: 2018 PMID: 30139641 PMCID: PMC6150788 DOI: 10.1016/j.tig.2018.07.004
Source DB: PubMed Journal: Trends Genet ISSN: 0168-9525 Impact factor: 11.639