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Beyond Missing Heritability: Prediction of Complex Traits

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses o...

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Autores principales: Makowsky, Robert, Pajewski, Nicholas M., Klimentidis, Yann C., Vazquez, Ana I., Duarte, Christine W., Allison, David B., de los Campos, Gustavo
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084207/
https://www.ncbi.nlm.nih.gov/pubmed/21552331
http://dx.doi.org/10.1371/journal.pgen.1002051
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author Makowsky, Robert
Pajewski, Nicholas M.
Klimentidis, Yann C.
Vazquez, Ana I.
Duarte, Christine W.
Allison, David B.
de los Campos, Gustavo
author_facet Makowsky, Robert
Pajewski, Nicholas M.
Klimentidis, Yann C.
Vazquez, Ana I.
Duarte, Christine W.
Allison, David B.
de los Campos, Gustavo
author_sort Makowsky, Robert
collection PubMed
description Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼0.80), substantial room for improvement remains.
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spelling pubmed-30842072011-05-06 Beyond Missing Heritability: Prediction of Complex Traits Makowsky, Robert Pajewski, Nicholas M. Klimentidis, Yann C. Vazquez, Ana I. Duarte, Christine W. Allison, David B. de los Campos, Gustavo PLoS Genet Research Article Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼0.80), substantial room for improvement remains. Public Library of Science 2011-04-28 /pmc/articles/PMC3084207/ /pubmed/21552331 http://dx.doi.org/10.1371/journal.pgen.1002051 Text en Makowsky et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Makowsky, Robert
Pajewski, Nicholas M.
Klimentidis, Yann C.
Vazquez, Ana I.
Duarte, Christine W.
Allison, David B.
de los Campos, Gustavo
Beyond Missing Heritability: Prediction of Complex Traits
title Beyond Missing Heritability: Prediction of Complex Traits
title_full Beyond Missing Heritability: Prediction of Complex Traits
title_fullStr Beyond Missing Heritability: Prediction of Complex Traits
title_full_unstemmed Beyond Missing Heritability: Prediction of Complex Traits
title_short Beyond Missing Heritability: Prediction of Complex Traits
title_sort beyond missing heritability: prediction of complex traits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084207/
https://www.ncbi.nlm.nih.gov/pubmed/21552331
http://dx.doi.org/10.1371/journal.pgen.1002051
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