A Framework for the Interpretation of De Novo Mutation in Human Disease

Overview

Journal Nat Genet

Specialty Genetics

Date 2014 Aug 4

PMID 25086666

Citations 556

Authors

Kaitlin E Samocha

Elise B Robinson

Stephan J Sanders

Christine Stevens

Aniko Sabo

Lauren M McGrath

Jack A Kosmicki

Karola Rehnstrom

Swapan Mallick

Andrew Kirby

Dennis P Wall

Daniel G MacArthur

Stacey B Gabriel

Mark DePristo

Shaun M Purcell

Aarno Palotie

Eric Boerwinkle

Joseph D Buxbaum

Edwin H Cook Jr

Richard A Gibbs

Gerard D Schellenberg

James S Sutcliffe

Bernie Devlin

Kathryn Roeder

Benjamin M Neale

Mark J Daly

Affiliations

Soon will be listed here.

Abstract

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

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