A statistical framework to guide sequencing choices in pedigrees.
Am J Hum Genet
; 94(2): 257-67, 2014 Feb 06.
Article
em En
| MEDLINE
| ID: mdl-24507777
ABSTRACT
The use of large pedigrees is an effective design for identifying rare functional variants affecting heritable traits. Cost-effective studies using sequence data can be achieved via pedigree-based genotype imputation in which some subjects are sequenced and missing genotypes are inferred on the remaining subjects. Because of high cost, it is important to carefully prioritize subjects for sequencing. Here, we introduce a statistical framework that enables systematic comparison among subject-selection choices for sequencing. We introduce a metric "local coverage," which allows the use of inferred inheritance vectors to measure genotype-imputation ability specifically in a region of interest, such as one with prior evidence of linkage. In the absence of linkage information, we can instead use a "genome-wide coverage" metric computed with the pedigree structure. These metrics enable the development of a method that identifies efficient selection choices for sequencing. As implemented in GIGI-Pick, this method also flexibly allows initial manual selection of subjects and optimizes selections within the constraint that only some subjects might be available for sequencing. In the present study, we used simulations to compare GIGI-Pick with PRIMUS, ExomePicks, and common ad hoc methods of selecting subjects. In genotype imputation of both common and rare alleles, GIGI-Pick substantially outperformed all other methods considered and had the added advantage of incorporating prior linkage information. We also used a real pedigree to demonstrate the utility of our approach in identifying causal mutations. Our work enables prioritization of subjects for sequencing to facilitate dissection of the genetic basis of heritable traits.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Linhagem
/
Análise de Sequência
/
Ligação Genética
/
Modelos Genéticos
Tipo de estudo:
Health_economic_evaluation
Limite:
Female
/
Humans
/
Male
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article