skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
Tipo de recurso
criteria input
qualquer lugar do registro
no título
como autor
no assunto
Data de publicação
lsr01
lsr02
lsr03
lsr04
Orientador
Show Results with:
no título
Show Results with:
qualquer lugar do registro
no título
como autor
no assunto
Data de publicação
lsr01
lsr02
lsr03
lsr04
Orientador
Mostra resultados com:
criteria input
que contêm minhas palavras de busca
com a frase exata
começa com
Mostra resultados com:
Índice
criteria input
E
OU
NÃO
This feature requires javascript
Power and predictive accuracy of polygenic risk scores
Dudbridge, Frank Wray, Naomi R.
PLoS genetics, 2013-03, Vol.9 (3), p.e1003348-e1003348
[Periódico revisado por pares]
United States: Public Library of Science
Texto completo disponível
Citações
Citado por
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
Nº de Citações
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
Power and predictive accuracy of polygenic risk scores
Autor:
Dudbridge, Frank
Wray, Naomi R.
Assuntos:
Accuracy
;
Biology
;
Disease
;
Estimates
;
Genome-Wide Association Study
;
Genomics
;
Humans
;
Liability
;
Linear Models
;
Mathematics
;
Mean square errors
;
Medical research
;
Medicine
;
Models, Theoretical
;
Multifactorial Inheritance - genetics
;
Polymorphism, Single Nucleotide
;
Population genetics
;
Quantitative genetics
;
Quantitative Trait Loci - genetics
;
Schizophrenia
;
Studies
É parte de:
PLoS genetics, 2013-03, Vol.9 (3), p.e1003348-e1003348
Notas:
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
The author has declared that no competing interests exist.
Conceived and designed the experiments: FD. Performed the experiments: FD. Analyzed the data: FD. Wrote the paper: FD.
Descrição:
Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.
Editor:
United States: Public Library of Science
Idioma:
Inglês
Links
View this record in MEDLINE/PubMed
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_PRODUCAO),scope:(USP_EBOOKS),scope:("PRIMO"),scope:(USP),scope:(USP_EREVISTAS),scope:(USP_FISICO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript