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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| Format: | Online |
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Public Library of Science
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084207/ |
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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 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 (h2 up to 0.83, R2 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 R2 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. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Makowsky, Robert Pajewski, Nicholas M. Klimentidis, Yann C. Vazquez, Ana I. Duarte, Christine W. Allison, David B. de los Campos, Gustavo |
| spellingShingle |
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 |
| 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 |
| title |
Beyond Missing Heritability: Prediction of Complex Traits |
| title_short |
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_sort |
beyond missing heritability: prediction of complex traits |
| 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 (h2 up to 0.83, R2 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 R2 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. |
| publisher |
Public Library of Science |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3084207/ |
| _version_ |
1611450948046028800 |