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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Bibliographic Details
Published in:PLoS genetics 2011-04, Vol.7 (4), p.e1002051-e1002051
Main Authors: Makowsky, Robert, Pajewski, Nicholas M, Klimentidis, Yann C, Vazquez, Ana I, Duarte, Christine W, Allison, David B, de los Campos, Gustavo
Format: Article
Language:English
Subjects:
Accuracy
Bayes Theorem
Biology
Body Height - genetics
Breeding of animals
Data mining
Estimates
Genetic research
Genome, Human
Genome-Wide Association Study
Genomes
Genomics
Genotype
Humans
Information management
Mathematics
Methods
Phenotype
Polymorphism, Single Nucleotide
Population
Quantitative Trait, Heritable
Single nucleotide polymorphisms
Studies
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Summary: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 (
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1002051