Genetic prediction for Autism
This could be a good early example of genetic prediction for a moderately complex trait (e.g., controlled by hundreds or a thousand or so loci). Data from 3,346 individuals with ASD and 4,165 of their relatives from Autism Genetic Resource Exchange (AGRE) and Simons Foundation Autism Research Initiative (SFARI).
Predicting the diagnosis of autism spectrum disorder using gene pathway analysis (Nature Molecular Psychology)
Skafidas E, Testa R, Zantomio D, Chana G, Everall IP, Pantelis C.
Centre for Neural Engineering, The University of Melbourne, Parkville, VIC, Australia.
Abstract
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.
It looks like they used a quasi-linear ("superadditive") prediction model after using biochemical pathway analysis to restrict to a subset of candidate genes. It doesn't matter how you get the candidate genes -- all that matters is that you obtain predictive power.
Predicting ASD phenotype based upon candidate SNPs
For each individual, a 775-dimensional vector was constructed, corresponding to 775 unique SNPs identified as part of the GSEA. To examine whether SNPs could predict an individual’s clinical status (ASD versus non-ASD), two-tail unpaired t-tests were used to identify which of the 775 SNPs had statistically significant differences in mean SNP value (P<0.005). This significance level provided low classification error while maintaining acceptable variance in estimation of regression coefficients for each SNP’s contribution status, and provided the set of SNPs that maximized the classifier output between the populations (Figure 2 and Supplementary S2). This resulted in 237 SNPs selected for regression analysis. Each dimension of the vector was assigned a value of 0, 1 or 3, dependent on a SNP having two copies of the dominant allele, heterozygous or two copies of the minor allele. The ‘0, 1, 3’ weighting provided greater classification accuracy over ‘0, 1, 2’. Such approaches using superadditive models have been used previously to understand genetic interactions.
Predicting the diagnosis of autism spectrum disorder using gene pathway analysis (Nature Molecular Psychology)
Skafidas E, Testa R, Zantomio D, Chana G, Everall IP, Pantelis C.
Centre for Neural Engineering, The University of Melbourne, Parkville, VIC, Australia.
Abstract
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.
It looks like they used a quasi-linear ("superadditive") prediction model after using biochemical pathway analysis to restrict to a subset of candidate genes. It doesn't matter how you get the candidate genes -- all that matters is that you obtain predictive power.
Predicting ASD phenotype based upon candidate SNPs
For each individual, a 775-dimensional vector was constructed, corresponding to 775 unique SNPs identified as part of the GSEA. To examine whether SNPs could predict an individual’s clinical status (ASD versus non-ASD), two-tail unpaired t-tests were used to identify which of the 775 SNPs had statistically significant differences in mean SNP value (P<0.005). This significance level provided low classification error while maintaining acceptable variance in estimation of regression coefficients for each SNP’s contribution status, and provided the set of SNPs that maximized the classifier output between the populations (Figure 2 and Supplementary S2). This resulted in 237 SNPs selected for regression analysis. Each dimension of the vector was assigned a value of 0, 1 or 3, dependent on a SNP having two copies of the dominant allele, heterozygous or two copies of the minor allele. The ‘0, 1, 3’ weighting provided greater classification accuracy over ‘0, 1, 2’. Such approaches using superadditive models have been used previously to understand genetic interactions.