Journal
JOURNAL OF BIOPHARMACEUTICAL STATISTICS
Volume 20, Issue 2, Pages 415-440Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10543400903572829
Keywords
Complex traits; Genome-wide association studies; High-dimensional data; Risk prediction; Single-nucleotide polymorphism
Categories
Funding
- NIH [R01 GM59507, T15 LM07056, U01 DK62429]
- NSF [DMS0714817]
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [U01DK062429] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM059507] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [T15LM007056] Funding Source: NIH RePORTER
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Recent genome-wide association studies have identified many genetic variants affecting complex human diseases. It is of great interest to build disease risk prediction models based on these data. In this article, we first discuss statistical challenges in using genome-wide association data for risk predictions, and then review the findings from the literature on this topic. We also demonstrate the performance of different methods through both simulation studies and application to real-world data.
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