Tag Archive for 'risk prediction'

Eight types of schizophrenia? Not so fast…

30/09/2014
Categories: Analysis and Guest Posts
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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.
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Guest post: the perils of genetic risk prediction in autism

28/10/2013
Categories: Guest Posts
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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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