A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data.
Hao HuJared C RoachHilary CoonStephen L GutheryKarl V VoelkerdingRebecca L MargrafJacob D DurtschiSean V Tavtigiannull ShankaracharyaWilfred WuPaul ScheetShuoguo WangJinchuan XingGustavo GlusmanRobert HubleyHong LiVidu GargBarry MooreLeroy HoodDavid J GalasDeepak SrivastavaMartin G ReeseLynn B JordeMark YandellChad D HuffPublished in: Nature biotechnology (2014)
High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.
Keyphrases
- genome wide
- case control
- dna methylation
- copy number
- endothelial cells
- high throughput
- electronic health record
- single cell
- hiv testing
- induced pluripotent stem cells
- data analysis
- high throughput sequencing
- mitochondrial dna
- amino acid
- genome wide identification
- health information
- healthcare
- intellectual disability
- bioinformatics analysis
- transcription factor
- deep learning