"Autism spectrum disorder (ASD) is a lifelong developmental condition that affects about 1 in 110 individuals, with onset before the age of three years. It is characterized by abnormalities in communication, impaired social function, repetitive behaviors and restricted interests ."ASD is more common among males than females, with a 4:1 male to female ratio. Numerous studies in the literature have shown evidence for a strong genetic component of autism, with a risk up to 25 times higher among siblings compared to the general population. However, if you look at the literature, you find that these numbers change pretty dramatically from study to study. This is often the case when you look at rare disorders in conjunction with rare mutations (WARNING: the rest of the paragraph is a statistical digression, feel free to skip to the next section). The smaller the effect you are trying to measure, the more subjects you will need in your study. This is also true if you are testing many variants, as for example in GWAS studies, which investigate variants in the whole genome. If the effect is big enough, you will find statistical support for your association, however, if your sample size is not big enough, the effect you are trying to measure will vary greatly from study to study. This is because the smaller the sample size, the larger the variance, which is stat jargon to say that whatever you are trying to measure (typically an increase in risk) is likely to be different if you repeat the study.
What do we know about the genetic etiology of ASD? About 10% of people diagnosed with ASD have some underlying genetic syndrome (including mitochondrial genes). About 5% are due to rare chromosome rearrangements, for example changes in the size, shape, or number of some chromosomes. Another 5% has been associated to both inherited and de novo "copy number variations" (CNV), the presence of extra copies of some genes . CNV is not rare among humans, as it accounts for approximately 0.4% of the variation between unrelated genomes. Identical twins also differ in CNV, and, even though they have identical genomes, the copy number of the genes may differ between the two. Despite this, in some families with a history of ASD the proportion of de novo CNV's has been found to be up to five times higher than in families without a history of ASD. Finally, thanks to recent advances in sequencing technology, de novo point mutations throughout hundreds of genes have been found and implicated in about 15% of ASD cases .
In light of the variety of mutations, genes, and phenotypes associated with ASD, two studies published in the last issue of Cell addressed the following question:
"do these genetic loci converge on specific biological processes, and where does the phenotypic specificity of ASD arise, given its genetic overlap with intellectual disability (ID)? "
"if and when, in what brain regions, and in which cell types specific groups of ASD-related mutations converge during human brain development " ?Of the two papers, I've so far only read the one by Willsey et al. , who combined their own data with already published data and identified 144 de novo "loss-of-function (LoF)" mutations, in other words, mutations that impair the functionality of the gene (hence the corresponding protein is no longer produced). They called genes with 2 or more de novo LoF mutations "hcASD", or "high confidence" ASD because statistically they had a high probability of being truly associated with ASD. They also analyzed a less-likely set of genes with only one de novo LoF mutation, which they called "pASD genes".
Next, the researchers investigated when and where these genes are expressed during brain development. The way they did this is a bit technical, but to think about it in simple terms think of it this way: (1) they needed samples from brain tissues taken at different developmental stages; (2) they needed to look not just at one gene, but at families of genes that are likely to interact together and influence one another's likelihood of getting turned "on" and "off". When a gene is turned "on", the gene is coding a protein, and we say that the gene is "expressed."
To carry on their analysis, Willsey et al. used data published by Kang et al. (Nature, 2011) from "57 clinically unremarkable postmortem brains of diverse ancestry (31 males, 26 females) that span 15 consecutive periods of neurodevelopment and adulthood from 5.7 postconceptual weeks (PCW) to 82 years." The gene expression values were determined for each gene by brain region and by postmortem brain sample. Brain regions were grouped according to transcriptional similarity during fetal development. These data were used to generate 52 gene coexpression networks, each network composed of the hcASD genes and their top correlated genes. This coexpression network analysis is a technique that's been extensively used lately to analyze patterns of co-expressions of genes. Each gene in the network is represented by a node, and any two nodes (genes) at any given time are connected if the genes are expressed at that time.
Using this set-up, the researchers were able to link the ASD genes to particular brain regions and developmental phases.
"Our analysis identifies robust, statistically significant evidence for convergence of the input set of hcASD and pASD risk genes in glutamatergic projection neurons in layers 5 and 6 of human midfetal prefrontal and primary motor-somatosensory cortex (PFC-MSC). Given the extensive genetic and phenotypic heterogeneity underlying ASD and the small fraction of risk genes that we have examined in this study, this likely represents only one of several such points of convergence. Nonetheless, the analytic approach presented here clarifies key variables relevant for productive functional studies of specific ASD genes carrying LoF mutations, providing an important step in moving from gene discovery to an actionable understanding of ASD biology ."Cortical glutamatergic projection neurons (CPNs) are a class of neocortical neurons. They are called "projection" neurons because they transmit information from the neocortex to other neocortical and central nervous system regions. During development, projection neurons are generated in the neocortical germinal zone and migrate radially to their final neocortical position. In their study, Wyllsey et al found that the development of midfetal CPNs is particularly vulnerable to ASD. Furthermore, the set of ASD genes they identified as associated to ASD are functionally diverse and encode proteins found in distinct cell compartments, confirming the theory that ASD can be caused by different and distinct pathways.
"Given recent studies suggesting that as many as 1,000 genes or more could contribute to ASD (He et al., 2013; Iossifov et al., 2012; Sanders et al., 2012), our analysis has uncovered a surprising degree of developmental convergence. Despite starting with only nine hcASD seed genes, we have identified highly significant and robust evidence for the contribution of coexpression networks relevant to L5 and L6 CPNs in two overlapping periods of midfetal human development (3–5 and 4–6) corresponding to 10–24 PCW ."The importance of these studies lies in the understanding of not just the genetic association per se, but in the mechanisms that drive these associations, and, most importantly, how the numerous genes interact and when.
 Devlin and Schrer (2012). Genetic architecture in autism spectrum disorder Genetics & Development DOI: 10.1016/j.gde.2012.03.002
 Neelroop N. Parikshak, Rui Luo, Alice Zhang, Hyejung Won, Jennifer K. Lowe, Vijayendran Chandran, Steve Horvath, Daniel H. Geschwind (2013). Integrative Functional Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism Cell DOI: 10.1016/j.cell.2013.10.031
 A. Jeremy Willsey, Stephan J. Sanders, Mingfeng Li, Shan Dong, Andrew T. Tebbenkamp, Rebecca A. Muhle, Steven K. Reilly, Leon Lin, Sofia Fertuzinhos, Jeremy A. Miller, Michael T. Murtha, Candace Bichsel, Wei Niu, Justin Cotney, A. Gulhan Ercan-Sencicek, J (2013). Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism Cell DOI: 10.1016/j.cell.2013.10.020