title: Detecting multiple causal rare variants in exome sequence data.
abstract: Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants. Weaknesses of the exome sequence data and the relative strengths and weaknesses of the five approaches are discussed.
authors: Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
authors: Kenny Q
authors: KQ
authors: Ye
authors: Corinne D
authors: CD
authors: Engelman
mesh_terms: D015984:Causality; D059472:Exome / Q000235:genetics; D020022:Genetic Predisposition to Disease; D016045:Human Genome Project; D006801:Humans; D008957:Models, Genetic; D017720:Molecular Epidemiology / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D017421:Sequence Analysis