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?
Continue reading ‘Incorporating false discovery rates into genetic association in autism’
Editor’s note: this guest post was contributed by ten leading psychiatric geneticists (see author list at the end of the post) in response to the headline-grabbing claims of a recent paper claiming to have identified eight genetic sub-types of schizophrenia. Similar text has also been posted on PubMed Commons and elsewhere. [DM]
In a study published on September 15, Arnedo et al. asserted that schizophrenia is a heterogeneous group of disorders underpinned by different genetic networks mapping to differing sets of clinical symptoms. As a result of their analyses, Arnedo et al. have made remarkable and perhaps unprecedented claims regarding their capacity to subtype schizophrenia. This paper has received considerable media attention. One claim features in many media reports, that schizophrenia can be delineated into “8 types”. If these claims are replicable and consistent, then the work reported in this paper would constitute an important advance into our knowledge of the etiology of schizophrenia.
Unfortunately, these extraordinary claims are not justified by the data and analyses presented. Their claims are based upon complex (and we believe flawed) analyses that are said to reveal links between clusters of clinical data points and patterns of data generated by looking at millions of genetic data points. Instead of the complexities favored by Arnedo et al., there are far simpler alternative explanations for the patterns they observed. We believe that the authors have not excluded important alternative explanations – if we are correct, then the major conclusions of this paper are invalidated.
Continue reading ‘Eight types of schizophrenia? Not so fast…’
This guest post was contributed by Karol Estrada, a postdoctoral research fellow in the Analytic and Translational Research Unit at Massachusetts General Hospital and the Broad Institute of MIT and Harvard. It is dedicated to the memory of Laura Riba.
Genome-wide association studies (GWAS) of common variants have successfully implicated more than 70 genomic regions in type 2 diabetes, revealing new biological pathways and potential drug targets. However, most large studies have examined genetic variation only in northwestern European populations, despite the rich genetic diversity in other populations around the world. Most studies have also been limited in their ability to detect variants present in fewer than 5 percent of people. Much remains to be learned.
In this post, we discuss our new paper, published in the Journal of the American Medical Association, on a low-frequency missense variant in the gene HNF1A that raises risk of type 2 diabetes five-fold, and was seen only in Latinos. This variant was the only rare variant to reach genome-wide significance in an exome sequencing study of almost 4,000 people, the largest such study to date. We explain the ramifications for sample sizes of rare-variant studies, note the importance of studying populations outside of northwestern Europe, and caution against simplistic dichotomous interpretations of disease as either complex or monogenic. Finally, we note that a low-frequency or rare variant might guide therapeutic modification.
Continue reading ‘A rare variant in Mexico with far-reaching implications’
Authors: Daniel MacArthur and Chris Gunter.
New DNA sequencing technologies are rapidly transforming the diagnosis of rare genetic diseases, but they also carry a risk: by allowing us to see all of the hundreds of “interesting-looking” variants in a patient’s genome, they make it potentially easy for researchers to spin a causal narrative around genetic changes that have nothing to do with disease status. Such false positive reports can have serious consequences: incorrect diagnoses, unnecessary or ineffective treatment, and reproductive decisions (such as embryo termination) based on spurious test results. In order to minimize such outcomes the field needs to decide on clear statistical guidelines for deciding whether or not a variant is truly causally linked with disease.
In a paper in Nature this week we report the consensus statement from a workshop sponsored by the National Human Genome Research Institute, on establishing guidelines for assessing the evidence for variant causality. We argue for a careful two-stage approach to assessing evidence, taking into account the overall support for a causal role of the affected gene in the disease phenotype, followed by an assessment of the probability that the variant(s) carried by the patient do indeed play a causal role in that patient’s disease state. We argue for the primacy of statistical genetic evidence for new disease genes, which can be supplemented (but not replaced by) additional informatic and experimental support; and we emphasize the need for all forms of evidence to be placed within a statistical framework that considers the probability of any of the reported lines of evidence arising by chance.
The paper itself is open access, so you can read the whole thing – we won’t rehash a complete summary here. However, we did want to discuss the back story and expand on a few issues raised in the paper.
Continue reading ‘Guidelines for finding genetic variants underlying human disease’
This guest post was contributed by Taru Tukiainen, a postdoctoral research fellow in the Analytic and Translational Research Unit at Massachusetts General Hospital and the Broad Institute of MIT and Harvard.
The X chromosome contains around 5% of DNA in the human genome, but has remained largely unexplored in genome-wide association studies (GWAS) – to date, roughly two thirds of GWAS have thrown the X-chromosomal data out of their analyses. In a paper published in PLOS Genetics yesterday we dig into X chromosome associations and demonstrate why this stretch of DNA warrants particular attention in genetic association and sequencing studies. This post will focus on one of our key results: the possibility that some of the X chromosome loci contribute to sexual dimorphism, i.e. biological differences between men and women.
Continue reading ‘The undiscovered chromosome’
About Guest Co-Author: Dr Ewan Birney is Associate Director of the EMBL European Bioinformatics Institute and a fellow blogger.
The ACMG recommendations on clinical genomic screening released earlier this year generated quite a storm. Criticisms broadly related to:
- the principle of whether we are ready and able to offer genomic screening to people undergoing exome/genome sequencing (the topic of this post!);
- to whom the recommendations should apply
- whether individuals have a right to refuse genomic screening results; and
- the exact content of the list of genes/variants to be screened.
In the UK, this debate has come into sharp focus following the launch of the NHS 100,000 genome project, where details of data interpretation and data sharing are still rather hazy. The central policy question is clear: in the context of clinical practice, how should we be using genomic data, and with whom, in order to maximise its benefits for patients? (In the context of research, as broad as possible sharing consistent with patient consent is most desirable.) Last month, we published a paper in the BMJ – along with a number of genetic scientists, clinical geneticists and other health specialists – advocating an evidence-based approach that places the emphasis on targeted diagnosis in the short term, and gathering evidence for possible broader uses in future.
Continue reading ‘Pertinent and Non-pertinent Genomic Findings’
This is a guest post from Mari Niemi at the Wellcome Trust Sanger Institute. Mari is a graduate researcher whose research combines the results of human genetic studies with zebrafish models to study human disease.
The turn of the year 2012/13 saw the emergence of a new and exciting – and some may even say revolutionary – technique for targeted genome engineering, namely the clustered regularly interspaced short palindromic repeat (CRISPR)-system. Harboured with the cells of many bacteria and archaea, in the wild CRISPRs act as an adaptive immune defence system chopping up foreign DNA. However, they are now being harnessed for genetic engineering in several species, most notably in human cell lines and the model animals mouse (Mus musculus) and zebrafish (Danio rerio). This rapid genome editing is letting us to study the function of genes and mutations and may even help improve the treatment of genetic diseases. But what makes this technology better than what came before, what are its downsides, and how revolutionary will it really be?
Genetic engineering – then and now
Taking a step backward, the ability to edit specific parts of an organism’s genetic material is certainly not novel practice. In the last decade or two, zinc finger nucleases (ZFNs) and more recently employed transcription activator-like endonucleases (TALENs) saw the deletion and introduction of genetic material, from larger segments of DNA to single base-pair point mutations, at desired sites become reality. ZFNs and TALENs are now fairly established methods, yet constructing these components and applying them in the laboratory can be extremely tedious and time-consuming due to the complex ways in which they binding with DNA. Clearly, there is much room for improvement and a desire for faster, cheaper and more efficient techniques in the prospect of applying genome engineering in treatment of human disease.
Continue reading ‘How emerging targeted mutation technologies could change the way we study human genetics’
Last week, the FDA sent a sternly-worded letter to the personal genomics company 23andMe, arguing that the company is marketing an unapproved diagnostic device. Many have weighed in on this, but I’d like to highlight a thoughtful post by Mike Eisen.
Eisen makes the important point that interpreting the genetics literature is complicated, and a company (like 23andMe) that provides this interpretation as a service could potentially add value. I’d like to add a simple point: this is absolutely not limited to genetics. In fact, there are already many software applications that calculate your risk for various diseases based on standard (i.e. non-genetic) epidemiology. For example, here’s a (NIH-based) site for calculating your risk of having a heart attack:
And here’s a site for calculating your risk of having a stroke in the next 10 years:
And here’s one for diabetes. And colorectal cancer. And breast cancer. And melanoma. And Parkinson’s.
I don’t point this out because it leads to an obvious conclusion; it doesn’t. But all of the scientific points made about risk prediction from 23andMe (the models are not very predictive, they’re missing a lot of important variables, there are likely errors in measurements, etc.) of course apply to traditional epidemiology as well. Ultimately, I think a lot rides on the question: what is the aspect of 23andMe that sets them apart from these websites and makes them more suspect? Is it because they focus on genetic risk factors rather than “traditional” risk factors (though note several of these sites ask about family history, which of course implicitly includes genetic information)? Is it the fact that they’re a for-profit company selling a product? Is it something about the way risks are reported, or the fact that risks for many diseases are presented on a single site? Is it because some genetic risk factors (like BRCA1) have strong effects, while standard epidemiological risk factors are usually of small effect? Or is it something else?
This is a guest post by Danny Wilson from the University of Oxford. Danny was recently awarded a Wellcome Trust/Royal Society fellowship at the Nuffield Department of Medicine, and in this post he tells us why you cannot understand human genetics without studying the genetics of microbes. If you are a geneticist who finds this post interesting, he is currently hiring.
Never mind about sequencing your own genome. Only 10% of cells on your “human” body are human anyway, the rest are microbial. And their genomes are far more interesting.
For one thing, there’s a whole ecosystem out there, made up of many species. Typically a person harbours 1,000 or more different species in their gut alone. For another, a person’s health is to a large part determined by the microbes that live on their body, whether that be as part of a long-term commensal relationship or an acute pathogenic interaction.
With 20% of the world’s deaths still attributable to infectious disease, the re-emergence of ancient pathogens driven by ever-increasing antibiotic resistance, and the UK’s 100K Genome Project– many of which will have to be genomes from patients (i.e. microbes) rather than patients’ own genomes given its budget – pathogen genomics is very much at the top of the agenda.
So what do pathogen genomes have to tell us? Continue reading ‘Guest post: Human genetics is microbial genomics’
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.
Last 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):
Continue reading ‘Guest post: the perils of genetic risk prediction in autism’