Interpretation of association signals and identification of causal variants from genome-wide association studies.
Am J Hum Genet
; 86(5): 730-42, 2010 May 14.
Article
en En
| MEDLINE
| ID: mdl-20434130
GWAS have been successful in identifying disease susceptibility loci, but it remains a challenge to pinpoint the causal variants in subsequent fine-mapping studies. A conventional fine-mapping effort starts by sequencing dozens of randomly selected samples at susceptibility loci to discover candidate variants, which are then placed on custom arrays or used in imputation algorithms to find the causal variants. We propose that one or several rare or low-frequency causal variants can hitchhike the same common tag SNP, so causal variants may not be easily unveiled by conventional efforts. Here, we first demonstrate that the true effect size and proportion of variance explained by a collection of rare causal variants can be underestimated by a common tag SNP, thereby accounting for some of the "missing heritability" in GWAS. We then describe a case-selection approach based on phasing long-range haplotypes and sequencing cases predicted to harbor causal variants. We compare this approach with conventional strategies on a simulated data set, and we demonstrate its advantages when multiple causal variants are present. We also evaluate this approach in a GWAS on hearing loss, where the most common causal variant has a minor allele frequency (MAF) of 1.3% in the general population and 8.2% in 329 cases. With our case-selection approach, it is present in 88% of the 32 selected cases (MAF = 66%), so sequencing a subset of these cases can readily reveal the causal allele. Our results suggest that thinking beyond common variants is essential in interpreting GWAS signals and identifying causal variants.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Variación Genética
/
Haplotipos
/
Polimorfismo de Nucleótido Simple
/
Grupos de Población
/
Estudio de Asociación del Genoma Completo
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Límite:
Humans
Idioma:
En
Revista:
Am J Hum Genet
Año:
2010
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos