1. American Psychological Association (2013): Diagnostic and Statistical Manual of Mental Disorders (DSM–5). Washington: American Psychological Association.
2. Geschwind, D. H. & State, M. W. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 14, 1109–1120 (2015).
3. Bailey, A. et al. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol. Med. 25, 63–77 (1995).
4. Lauritsen, M. B., Pedersen, C. B. & Mortensen, P. B. Effects of familial risk factors and place of birth on the risk of autism: a nationwide register-based study. J. Child Psychol. Psychiatry 46, 963–971 (2005).
5. Gene, S. Gene scoring. 2008. https://gene.sfari.org/database/gene-scoring/.
6. Eissa, N. et al. Current enlightenment about etiology and pharmacological treatment of autism spectrum disorder. Front. Neurosci. 12, 304 (2018).
7. Traylor, M., Markus, H. & Lewis, C. M. Homogeneous case subgroups increase power in genetic association studies. Eur. J. Hum. Genet. 23, 863–869 (2015).
8. Mukherjee, S. et al. Genetic data and cognitively defined late-onset Alzhei- mer’s disease subgroups. Mol. Psychiatry. 2018. https://doi.org/10.1038/s41380- 018-0298-8.
9. Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & van der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).
10. Lavoie-Charland, E., Berube, J. C., Boulet, L. P. & Bosse, Y. Asthma susceptibility variants are more strongly associated with clinically similar subgroups. J. Asthma 53, 907–913 (2016).
11. Chaste, P. et al. A genome-wide association study of autism using the Simons simplex collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol. Psychiatry 77, 775–784 (2015).
12. MacQueen, J. Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967, pp 281–297.
13. World Medical Association. World medical association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310, 2191–2194 (2013).
14. Fischbach, G. D. & Lord, C. The simons simplex collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).
15. Beggiato, A. et al. Gender differences in autism spectrum disorders: divergence among specific core symptoms. Autism Res. 10, 680–689 (2017).
16. Kuriyama, S. et al. Pyridoxine treatment in a subgroup of children with per- vasive developmental disorders. Dev. Med. Child Neurol. 44, 284–286 (2002).
17. Obara, T. et al. Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods. Sci. Rep. 8, 14840 (2018).
18. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
19. Nam, J. M. Simple approximation for calculating sample sizes for detecting linear trend in proportions. Biometrics 43, 701–705 (1987).
20. Cutting, D. R., Karger, D. R., Pedersen J. O. & Tukey J. W. Scatter/gather: a cluster-based approach to browsing large document collections. In: Pro- ceedings of the 15th Annual ACM SIGIR Conference on Research and Development in Information Retrieval. 318–329 (New York: Association for Computing Machinery (ACM), 1992).
21. Raykov, Y. P., Boukouvalas, A., Baig, F. & Little, M. A. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLoS ONE 11, e0162259 (2011).
22. Guo, G., Chen, L., Ye, Y.& Jiang, Q. Cluster validation method for determining the number of clusters in categorical sequences. IEEE Trans. Neural Netw. Learn Syst. 28, 2936–2948 (2017).
23. Sanders, S. J. et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011).
24. Spielman, R. S. & Ewens, W. J. A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am. J. Hum. Genet 62, 450–458 (1998).
25. Freidlin, B., Zheng, G., Li, Z. & Gastwirth, J. L. Trend tests for case-control studies of genetic markers: power, sample size and robustness. Hum. Hered. 53, 146–152 (2002).
26. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2002).
27. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
28. Wang, Y. et al. Genome-wide association study of piglet uniformity and far- rowing interval. Front. Genet. 8, 194 (2017).
29. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
30. Redies, C., Hertel, N. & Hubner, C. A. Cadherins and neuropsychiatric disorders. Brain Res. 1470, 130–144 (2012).
31. Varghese, M. et al. Autism spectrum disorder: neuropathology and animal models. Acta Neuropathol. 134, 537–566 (2017).
32. Atsem, S. et al. Paternal age effects on sperm FOXK1 and KCNA7 methylation and transmission into the next generation. Hum. Mol. Genet. 25, 4996–5005 (2016).
33. Barnby, G. et al. Candidate-gene screening and association analysis at the autism-susceptibility locus on chromosome 16p: evidence of association at GRIN2A and ABAT. Am. J. Hum. Genet. 76, 950–966 (2005).
34. Minhas, H. M. et al. An unbalanced translocation involving loss of 10q26.2 and gain of 11q25 in a pedigree with autism spectrum disorder and cerebellar juvenile pilocytic astrocytoma. Am. J. Med. Genet. A 161a, 787–791 (2013).
35. Abou-Donia, M. B., Suliman, H. B., Siniscalco, D., Antonucci, N. & ElKafrawy, P. De novo blood biomarkers in autism: autoantibodies against neuronal and glial proteins. Behav. Sci. (Basel) 9, E47 (2019).
36. Lo Vasco, V. R. Role of phosphoinositide-specific phospholipase C η2 in iso- lated and syndromic mental retardation. Eur. Neurol. 65, 264–269 (2011).
37. Potkin, S. G. et al. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol. Psychiatry 14, 416–428 (2009).
38. Konopaske, G. T. et al. Dysbindin-1 contributes to prefrontal cortical dendritic arbor pathology in schizophrenia. Schizophr. Res. 201, 270–277 (2018).
39. Openshaw, R. L. et al. JNK signalling mediates aspects of maternal immune activation: importance of maternal genotype in relation to schizophrenia risk. J. Neuroinflamm. 16, 18 (2019).
40. Ikeda, M. et al. Identification of novel candidate genes for treatment response to risperidone and susceptibility for schizophrenia: integrated analysis among pharmacogenomics, mouse expression, and genetic case-control association approaches. Biol. Psychiatry 67, 263–269 (2010).
41. Teng, X. et al. KCTD: a new gene family involved in neurodevelopmental and neuropsychiatric disorders. CNS Neurosci. Ther. 25, 887–902 (2019).
42. Lin, C. H., Huang, M. W., Lin, C. H., Huang, C. H. & Lane, H. Y. Altered mRNA expressions for N-methyl-D-aspartate receptor-related genes in WBC of patients with major depressive disorder. J. Affect. Disord. 245, 1119–1125 (2019).
43. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid pro- cessing. Nat. Genet. 51, 414–430 (2019).
44. Cimino, P. J., Sokal, I., Leverenz, J., Fukui, Y. & Montine, T. J. DOCK2 is a microglial specific regulator of central nervous system innate immunity found in normal and Alzheimer’s disease brain. Am. J. Pathol. 175, 1622–1630 (2009).
45. Sepulveda-Diaz, J. E. et al. HS3ST2 expression is critical for the abnormal phosphorylation of tau in Alzheimer’s disease-related tau pathology. Brain 138, 1339–1354 (2015).
46. Ghosh, D., Levault, K. R. & Brewer, G. J. Relative importance of redox buffers GSH and NAD(P)H in age-related neurodegeneration and Alzheimer disease- like mouse neurons. Aging Cell 13, 631–640 (2014).
47. Zong, Y. et al. miR-29c regulates NAV3 protein expression in a transgenic mouse model of Alzheimer’s disease. Brain Res. 1624, 95–102 (2015).
48. Irimia, M. et al. A highly conserved program of neuronal microexons is mis- regulated in autistic brains. Cell 159, 1511–1523 (2014).
49. Quesnel-Vallieres, M. et al. Misregulation of an activity-dependent splicing network as a common mechanism underlying autism spectrum disorders. Mol. Cell 64, 1023–1034 (2016).
50. DeMichele-Sweet, M. A. A. et al. Genetic risk for schizophrenia and psychosis in Alzheimer disease. Mol. Psychiatry 23, 963–972 (2018).