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Trends Genet. 2018 Oct;34(10):746-754. doi: 10.1016/j.tig.2018.07.004. Epub 2018 Aug 20.

Complex-Trait Prediction in the Era of Big Data.

Author information

1
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA; Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA. Electronic address: gustavoc@msu.edu.
2
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA; Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
3
Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA; Cognitive Genomics Laboratory, BGI, Shenzhen 518083, China.
4
Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA.

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.

KEYWORDS:

Big Data; GWAS; SNP; complex traits; disease risk; prediction

PMID:
30139641
PMCID:
PMC6150788
[Available on 2019-10-01]
DOI:
10.1016/j.tig.2018.07.004
[Indexed for MEDLINE]

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