Predicting the diagnosis of autism spectrum disorder using gene pathway analysis (Nature Molecular Psychiatry)From my comments:
Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.
The approach taken here first selects 775 SNPs of interest based on pathway information (not considered in standard GWAS) and then only requires 5E-03 significance. A linear predictor is formed from the 237 SNPs that pass this threshold. The ultimate test is, of course, whether the predictor actually works on (independent) validation samples. Once you have a statistically valid predictor, it doesn't matter how you arrived at it.This recent letter to the editor of Molecular Psychiatry claims that the predictive power came from the fact that the case and control groups had slightly different ancestral origin (via neuroskeptic):
... cases have more diverse ancestral origins within Europe than controls. The putative risk alleles are more common in the Northeastern than in the Northwestern Europe, whereas the putative protective alleles reflect the opposite trend.But I don't understand how this explains the moderate success of the classifier in the Chinese cohort -- I'll have to look more carefully. Here are the title and abstract of the new paper.
Population structure confounds autism genetic classifier
T G Belgard, I Jankovic, J K Lowe and D H Geschwind
A classifier was recently reported to predict with 70% accuracy if an individual has an autism spectrum disorder using 237 single-nucleotide polymorphisms (SNPs).1 Biomarkers, genetic or otherwise, that would facilitate earlier autism spectrum disorder diagnosis are crucial; therefore, these results warrant careful scrutiny. One potential confounder of such genetic studies is bias when cases and controls have different ancestral origins.