Abstract
Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n = 285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders.
Highlights
Genome-wide interrogation approaches for mapping genes often are designed to detect the common variants associated with common phenotypes or disease, generally leaving rare variants undetected or untested [1,2,3,4,5]
The common disease–common variant model is based on the idea that sets of common variants explain a significant fraction of the variance found in common disease phenotypes
We use a resequencing approach on a cohort of 285 Autism Spectrum Disorder and Schizophrenia patients and preformed several analyses, enhanced with population genetic approaches, to identify variants associated with both diseases
Summary
Genome-wide interrogation approaches for mapping genes often are designed to detect the common variants associated with common phenotypes or disease, generally leaving rare variants undetected or untested [1,2,3,4,5]. Functional mutations that lead to an altered amino acid are often deleterious and potentially disease causing. Such mutations are subjected to natural selection, which either removes these alleles from the population or maintains them at low frequencies relative to the neutral expectations [3,4,6,7,8,9].
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