Performance of case-control rare copy number variation annotation in classification of autism.

ABSTRACT:

Background

A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry.

Results

We analyzed 1,892 Caucasian ASD subjects and 2,342

SUBMITTER: Engchuan W 

PROVIDER: S-EPMC4315323 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature