Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. / Sun, Jiangming; Wang, Yunpeng; Folkersen, Lasse; Borné, Yan; Amlien, Inge; Buil, Alfonso; Orho-Melander, Marju; Børglum, Anders D.; Hougaard, David M.; Lotta, Luca Andrea; Jones, Marcus; Baras, Aris; Melander, Olle; Engström, Gunnar; Werge, Thomas; Lage, Kasper; Regeneron Genetics Center.

I: Nature Communications, Bind 12, 5276, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Sun, J, Wang, Y, Folkersen, L, Borné, Y, Amlien, I, Buil, A, Orho-Melander, M, Børglum, AD, Hougaard, DM, Lotta, LA, Jones, M, Baras, A, Melander, O, Engström, G, Werge, T, Lage, K & Regeneron Genetics Center 2021, 'Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction', Nature Communications, bind 12, 5276. https://doi.org/10.1038/s41467-021-25014-7

APA

Sun, J., Wang, Y., Folkersen, L., Borné, Y., Amlien, I., Buil, A., Orho-Melander, M., Børglum, A. D., Hougaard, D. M., Lotta, L. A., Jones, M., Baras, A., Melander, O., Engström, G., Werge, T., Lage, K., & Regeneron Genetics Center (2021). Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. Nature Communications, 12, [5276]. https://doi.org/10.1038/s41467-021-25014-7

Vancouver

Sun J, Wang Y, Folkersen L, Borné Y, Amlien I, Buil A o.a. Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. Nature Communications. 2021;12. 5276. https://doi.org/10.1038/s41467-021-25014-7

Author

Sun, Jiangming ; Wang, Yunpeng ; Folkersen, Lasse ; Borné, Yan ; Amlien, Inge ; Buil, Alfonso ; Orho-Melander, Marju ; Børglum, Anders D. ; Hougaard, David M. ; Lotta, Luca Andrea ; Jones, Marcus ; Baras, Aris ; Melander, Olle ; Engström, Gunnar ; Werge, Thomas ; Lage, Kasper ; Regeneron Genetics Center. / Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. I: Nature Communications. 2021 ; Bind 12.

Bibtex

@article{848e0b0c2fec4726b587ce7f7d0fa0ee,

title = "Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction",

abstract = "A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual{\textquoteright}s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.",

author = "Jiangming Sun and Yunpeng Wang and Lasse Folkersen and Yan Born{\'e} and Inge Amlien and Alfonso Buil and Marju Orho-Melander and B{\o}rglum, {Anders D.} and Hougaard, {David M.} and Lotta, {Luca Andrea} and Marcus Jones and Aris Baras and Olle Melander and Gunnar Engstr{\"o}m and Thomas Werge and Kasper Lage and {Regeneron Genetics Center}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

doi = "10.1038/s41467-021-25014-7",

language = "English",

volume = "12",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

AU - Sun, Jiangming

AU - Wang, Yunpeng

AU - Folkersen, Lasse

AU - Borné, Yan

AU - Amlien, Inge

AU - Buil, Alfonso

AU - Orho-Melander, Marju

AU - Børglum, Anders D.

AU - Hougaard, David M.

AU - Lotta, Luca Andrea

AU - Jones, Marcus

AU - Baras, Aris

AU - Melander, Olle

AU - Engström, Gunnar

AU - Werge, Thomas

AU - Lage, Kasper

AU - Regeneron Genetics Center

PY - 2021

Y1 - 2021

N2 - A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

AB - A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

U2 - 10.1038/s41467-021-25014-7

DO - 10.1038/s41467-021-25014-7

M3 - Journal article

C2 - 34489429

AN - SCOPUS:85114717685

VL - 12

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 5276

ER -

ID: 280124570