Tag Archive for 'autism'

Incorporating false discovery rates into genetic association in autism

This guest post was contributed by Joseph Buxbaum, Mark Daly, Silvia De Rubeis, Bernie Devlin, Kathryn Roeder, and Kaitlin Samocha from the Autism Sequencing Consortium (see affiliations and details at the end of the post).

Autism spectrum disorder (ASD) is a highly heritable condition characterized by deficits in social communication, and by the presence of repetitive behaviors and/or stereotyped interests. While it is clear from family and twin studies that genetic factors contribute strongly to the onset of this disorder, the search for specific risk genes for ASD has only recently begun to yield fruit. Finding these specific genes is critical not only for providing potential diagnoses for individual families, but also for obtaining insights into the pathological processes that underlie this neurodevelopmental disorder, which may ultimately lead to novel therapeutic approaches. Identification of ASD genes may at some point also reveal part of what makes us social beings.

In a paper published in Nature last week we and the other members of the Autism Sequencing Consortium (ASC) describe the application of whole exome sequencing (WES), selectively sequencing the coding regions of the genome, to identify rare genetic variants and then genes associated with risk for ASD. WES data were analyzed from nearly 4,000 individuals with autism and nearly 10,000 controls. In these analyses, we identify and subsequently analyze a set of 107 autosomal genes with a false discovery rate (FDR) of <30%; in total, this larger set of genes harbor de novo loss of function (LoF) mutations in 5% of cases, and numerous de novo missense and inherited LoF mutations in additional cases. Three critical pathways contributing to ASD were identified: chromatin remodelling, transcription and splicing, and synaptic function. Chromatin remodelling controls events underlying neural connectivity. Risk variation also impacted multiple components of synaptic networks. Because a wide set of synaptic genes is disrupted in ASD, it seems reasonable to suggest that altered chromatin dynamics and transcription, induced by disruption of relevant genes, leads to impaired synaptic function as well.

In this post we wanted to focus on an easily-overlooked aspect of this paper: the use of a false discovery rate (FDR) approach to identifying genes for follow-up analysis. While FDR is a well-recognized approach in biology, one could also argue for using a family wise error rate (FWER), which has been the norm in recent large-scale, genome-wide association studies (GWAS). So why did we decide to take this alternative approach here?
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Guest post: the perils of genetic risk prediction in autism

This guest post from Daniel Howrigan, Benjamin Neale, Elise Robinson, Patrick Sullivan, Peter Visscher, Naomi Wray and Jian Yang (see biographies at end of post) describes their recent rebuttal of a paper claiming to have developed a new approach to genetic prediction of autism. This story has also been covered by Ed Yong and Emily Willingham. Genomes Unzipped authors Luke Jostins, Jeff Barrett and Daniel MacArthur were also involved in the rebuttal.

autism-puzzleLast year, in a paper published in Molecular Psychiatry, Stan Skafidas and colleagues made a remarkable claim: a simple genetic test could be used to predict autism risk from birth. The degree of genetic predictive power suggested by the paper was unprecedented for a common disease, let alone for a disease as complex and poorly understood as autism. However, instead of representing a revolution in autism research, many scientists felt that the paper illustrated the pitfalls of pursuing genetic risk prediction. After nearly a year of study, two papers have shown how the Skafidas et al. study demonstrates the dangers of poor experimental design and biases due to important confounders.

The story in a nutshell: the Skafidas paper proposes a method for generating a genetic risk score for autism spectrum disorder (ASD) based on a small number of SNPs. The method is fairly straightforward – analyze genetic data from ASD case samples and from publicly available controls to develop, test, and validate a prediction algorithm for ASD. The stated result – Skafidas et al. claim successful prediction of ASD based on a subset of 237 SNPs. For the downstream consumer, the application is simple – have your doctor take a saliva sample from your newborn baby, send in the sample to get genotyped, and get a probability of your child developing ASD. It would be easy to test fetuses and for prospective parents to consider abortions if the algorithm suggested high risk of ASD.

The apparent simplicity is refreshing and, from the lay perspective, the result will resonate above all the technical jargon of multiple-testing correction, linkage disequilibrium (LD), or population stratification that dominates our field. This is what makes this paper all the more dangerous, because lurking beneath the appealing results is flawed methodology and design as we describe below.

We begin our critique with the abstract from Skafidas et al. (emphasis added):
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Guest post by Ben Neale: Evaluating the impact of de novo coding mutation in autism

[Dr. Neale is currently an Assistant in Genetics in the Analytic and Translational Genetics Unit at Massachusetts General Hospital and Harvard Medical School and an affiliate of the Broad Institute of Harvard and MIT. Dr. Neale’s research centers on statistical genetics and how to apply those methods to complex traits, with a particular focus on childhood psychiatric illness such as autism and ADHD.]

Today, in Nature, three letters (1, 2, 3) were published on the role of de novo coding mutations in the development of autism. I am lead author on one of these manuscripts, working in collaboration with the ARRA Autism Consortium. In this post, I’ll describe the main findings of our work as they relate to autism and how we approached the interpretation of de novo mutations. In essence, de novo point mutation is likely relevant to autism in ~10% of cases, but a single de novo event is not likely to be sufficient to cause autism. Underscoring this is that fewer than half of the cases had an obviously functional point mutation in the exome. However, three genes, SCN2A, KATNAL2 and CHD8 have emerged as likely candidates for contributing to autism pathogenesis.

De novo is Latin for “from the beginning,” and when describing genetic variation or mutation means that the variant has spontaneously arisen and was not inherited from either parent. In autism, de novo copy number variants are among the earliest clearly identified genetic risk factors (see Sanders et al. and Pinto et al. for reviews). Given that these events are novel, natural selection has not acted on them, except for instances where the point mutation is lethal in early life. With next generation sequencing (NGS), we now have the opportunity to identify these events directly.

In this study we explored the impact of de novo mutations on autism by performing targeted sequencing of the protein-coding regions of the genome (known collectively as the exome, and comprising just 1.5% of the genome as a whole) in 175 mother-father-child trios in which the child was diagnosed as autistic. Having sequence from all three members of each family allowed us to find mutations that had arisen spontaneously in a patient’s genome, rather than being inherited from their parents.

We have made a pre-formatted version of our manuscript available here. In this post I just wanted to highlight some of the key lessons emerging from our study.
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