Journal
NATURE REVIEWS GENETICS
Volume 14, Issue 7, Pages 507-515Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nrg3457
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Funding
- NCI NIH HHS [P01 CA055075, P01 CA087969] Funding Source: Medline
- NCRR NIH HHS [UL1 RR025005] Funding Source: Medline
- NHGRI NIH HHS [U01 HG004424, U01 HG004402, U01 HG004399, U01 HG004446, R01 HG006399] Funding Source: Medline
- NHLBI NIH HHS [N01HC25195, N01 HC025195, HHSN268201100008I, HHSN268201100007C, R01 HL087641, HHSN268201100005I, HHSN268201100005G, HHSN268201100009C, R01 HL086694, HHSN268201100011C, HHSN268201100009I, HHSN268201100008C, HHSN268201100011I, HHSN268201100005C, HHSN268201100012C, HHSN268201100010C, R01 HL059367, N02HL64278, HHSN268201100007I, HHSN268201100006C] Funding Source: Medline
- NIDDK NIH HHS [R01 DK058845] Funding Source: Medline
- NIGMS NIH HHS [P01 GM099568, R01 GM075091] Funding Source: Medline
- NIMH NIH HHS [R01 MH100141] Funding Source: Medline
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The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results.
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