Science Summary: Response to Predicting the Diagnosis of Autism Spectrum Disorder Using Gene Pathway Analysis
Posted Oct 01 2013 12:00am
Letter to the Editor
Molecular Psychiatry advance online publication 22 October 2013; doi: 10.1038/mp.2013.125
Response to ‘Predicting the diagnosis of autism spectrum disorder using gene pathway analysis’ Open
E B Robinson1,2,3, D Howrigan1,2,3, J Yang4,5, S Ripke1,2,3,6, V Anttila1,2,3,6, L E Duncan3,7,8,9, L Jostins10, J C Barrett10, S E Medland11, D G MacArthur1,2,3, G Breen12, M C O'Donovan13, N R Wray4,5, B Devlin14, M J Daly1,2,3,6, P M Visscher4,5, P F Sullivan15 and B M Neale1,2,3,6
1Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 2Department of Medicine, Harvard Medical School, Boston, MA, USA 3Medical and Population Genetics Program, Broad Institute for Harvard and MIT, Cambridge, MA, USA 4The University of Queensland, Queensland Brain Institute, Brisbane, QLD, Australia 5The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia 6Stanley Center for Psychiatric Research, Broad Institute for Harvard and MIT, Cambridge, MA, USA 7Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA 8Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts, General Hospital, Boston, MA, USA 9Department of Psychiatry, Harvard Medical School, Boston, MA, USA 10Wellcome Trust Sanger Institute, Cambridge, UK 11Queensland Institute of Medical Research, Brisbane, QLD, Australia 12Social Genetic and Developmental Psychiatry Center, Institute of Psychiatry, King’s College London, London, UK 13MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University School of Medicine, Cardiff, UK 14Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA 15Department of Genetics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
In a recent paper published online in Molecular Psychiatry, Skafidas et al.1 report a classifier for identifying individuals at risk for autism spectrum disorders (ASDs). Their classifier is based on 267 single-nucleotide polymorphisms (SNPs) that were selected from the results of a pathway analysis using cases from the Autism Genetic Resource Exchange (AGRE).1 Using within-sample cross-validation, the authors claim a classification accuracy for ASDs of 85.6%. They subsequently applied their classifier to ASD cases from the Simons Foundation Autism Research Initiative (SFARI) and controls from the Wellcome Trust Birth Cohort (WTBC) and report ASD classification accuracy of 71.7%.
We believe that the claims made by Skafidas et al.1 are inconsistent with current knowledge of the genetics of ASDs,2 and inconsistent with the expected precision of risk predictions for complex psychiatric disorders. Further, as classification accuracy depends on the size of the discovery sample, the results are also inconsistent with the size of the sample they employed (only 123 controls were included in the discovery set).
To examine the validity of Skafidas et al.’s claims, we pursued a range of analyses to assess the evidence for association between ASDs and (1) the individual SNPs named in their paper as most predictive, (2) their genetic classifier, to the extent it was described and (3) the pathways identified in the report, from which the predictive SNPs were selected. For each analysis, where possible, we attempted to replicate the analytic approach of Skafidas et al.1 using data from the Psychiatric Genomics Consortium (PGC) autism group, which includes ~5400 cases, more than three times the number used in the original report. The methodology of these analyses is described in detail in Supplementary Information.
First, we found no evidence for single SNP associations between any of the 30 most contributory SNPs listed by Skafidas et al.1 in their Table 2 and ASDs in the PGC (Table 1). In the current PGC meta-analysis, the mean P-value for these SNPs was 0.47 with a minimum 0.007, and none are notable or survive a 30 SNP correction for multiple testing. Further information on these associations can be found in Supplementary Information.
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