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An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort

The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the...

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Autores principales: Chan, Yingleong, Tung, Michael, Garruss, Alexander S., Zaranek, Sarah W., Chan, Ying Kai, Lunshof, Jeantine E., Zaranek, Alexander W., Ball, Madeleine P., Chou, Michael F., Lim, Elaine T., Church, George M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384003/
https://www.ncbi.nlm.nih.gov/pubmed/28387241
http://dx.doi.org/10.1038/srep46148
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author Chan, Yingleong
Tung, Michael
Garruss, Alexander S.
Zaranek, Sarah W.
Chan, Ying Kai
Lunshof, Jeantine E.
Zaranek, Alexander W.
Ball, Madeleine P.
Chou, Michael F.
Lim, Elaine T.
Church, George M.
author_facet Chan, Yingleong
Tung, Michael
Garruss, Alexander S.
Zaranek, Sarah W.
Chan, Ying Kai
Lunshof, Jeantine E.
Zaranek, Alexander W.
Ball, Madeleine P.
Chou, Michael F.
Lim, Elaine T.
Church, George M.
author_sort Chan, Yingleong
collection PubMed
description The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the extent and willingness and to encourage and guide participants to contribute phenotypes, we developed an algorithm to score and rank the phenotypes and participants of the PGP. The scoring algorithm calculates the participation index (P-index) for every participant, where 0 indicates no reported phenotypes and 100 indicate complete phenotype reporting. We calculated the P-index for all 5,015 participants in the PGP and they ranged from 0 to 96.7. We found that participants mainly have either high scores (P-index > 90, 29.5%) or low scores (P-index < 10, 57.8%). While, there are significantly more males than female participants (1,793 versus 1,271), females tend to have on average higher P-indexes (P = 0.015). We also reported the P-indexes of participants based on demographics and states like Missouri and Massachusetts have better P-indexes than states like Utah and Minnesota. The P-index can therefore be used as an unbiased way to measure and rank participant’s phenotypic contribution towards the PGP.
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spelling pubmed-53840032017-04-11 An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort Chan, Yingleong Tung, Michael Garruss, Alexander S. Zaranek, Sarah W. Chan, Ying Kai Lunshof, Jeantine E. Zaranek, Alexander W. Ball, Madeleine P. Chou, Michael F. Lim, Elaine T. Church, George M. Sci Rep Article The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the extent and willingness and to encourage and guide participants to contribute phenotypes, we developed an algorithm to score and rank the phenotypes and participants of the PGP. The scoring algorithm calculates the participation index (P-index) for every participant, where 0 indicates no reported phenotypes and 100 indicate complete phenotype reporting. We calculated the P-index for all 5,015 participants in the PGP and they ranged from 0 to 96.7. We found that participants mainly have either high scores (P-index > 90, 29.5%) or low scores (P-index < 10, 57.8%). While, there are significantly more males than female participants (1,793 versus 1,271), females tend to have on average higher P-indexes (P = 0.015). We also reported the P-indexes of participants based on demographics and states like Missouri and Massachusetts have better P-indexes than states like Utah and Minnesota. The P-index can therefore be used as an unbiased way to measure and rank participant’s phenotypic contribution towards the PGP. Nature Publishing Group 2017-04-07 /pmc/articles/PMC5384003/ /pubmed/28387241 http://dx.doi.org/10.1038/srep46148 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Chan, Yingleong
Tung, Michael
Garruss, Alexander S.
Zaranek, Sarah W.
Chan, Ying Kai
Lunshof, Jeantine E.
Zaranek, Alexander W.
Ball, Madeleine P.
Chou, Michael F.
Lim, Elaine T.
Church, George M.
An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title_full An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title_fullStr An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title_full_unstemmed An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title_short An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
title_sort unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384003/
https://www.ncbi.nlm.nih.gov/pubmed/28387241
http://dx.doi.org/10.1038/srep46148
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