Social and non-social autism symptom and trait domains are genetically dissociable

The core diagnostic criteria for autism comprise two symptom domains – social and communication difficulties, and unusually repetitive and restricted behaviour, interests and activities. There is some evidence to suggest that these two domains are dissociable, yet, this hypothesis has not been tested using molecular genetics. We test this using a GWAS of a non-social autistic trait, systemizing (N = 51,564), defined as the drive to analyse and build systems. We demonstrate that systemizing is heritable and genetically correlated with autism. In contrast, we do not identify significant genetic correlations between social autistic traits and systemizing. Supporting this, polygenic scores for systemizing are significantly positively associated with restricted and repetitive behaviour but not with social difficulties in autistic individuals. These findings strongly suggest that the two core domains of autism are genetically dissociable, and point at how to fractionate the genetics of autism

Social and non-social autism symptoms and trait domains are genetically dissociable

Abstract: The core diagnostic criteria for autism comprise two symptom domains – social and communication difficulties, and unusually repetitive and restricted behaviour, interests and activities. There is some evidence to suggest that these two domains are dissociable, though this hypothesis has not yet been tested using molecular genetics. We test this using a genome-wide association study (N = 51,564) of a non-social trait related to autism, systemising, defined as the drive to analyse and build systems. We demonstrate that systemising is heritable and genetically correlated with autism. In contrast, we do not identify significant genetic correlations between social autistic traits and systemising. Supporting this, polygenic scores for systemising are significantly and positively associated with restricted and repetitive behaviour but not with social difficulties in autistic individuals. These findings strongly suggest that the two core domains of autism are genetically dissociable, and point at how to fractionate the genetics of autism

Phenotypic effects of genetic variants associated with autism

While over 100 genes have been associated with autism, little is known about the prevalence of variants affecting them in individuals without a diagnosis of autism. Nor do we fully appreciate the phenotypic diversity beyond the formal autism diagnosis. Based on data from more than 13,000 individuals with autism and 210,000 undiagnosed individuals, we estimated the odds ratios for autism associated to rare loss-of-function (LoF) variants in 185 genes associated with autism, alongside 2,492 genes displaying intolerance to LoF variants. In contrast to autism-centric approaches, we investigated the correlates of these variants in individuals without a diagnosis of autism. We show that these variants are associated with a small but significant decrease in fluid intelligence, qualification level and income and an increase in metrics related to material deprivation. These effects were larger for autism-associated genes than in other LoF-intolerant genes. Using brain imaging data from 21,040 individuals from the UK Biobank, we could not detect significant differences in the overall brain anatomy between LoF carriers and non-carriers. Our results highlight the importance of studying the effect of the genetic variants beyond categorical diagnosis and the need for more research to understand the association between these variants and sociodemographic factors, to best support individuals carrying these variants

Des spectres MS/MS à l'identification des protéines - Interprétation des données issues de l'analyse d'un mélange de protéines d'un organisme non séquencé

Mass spectrometry is a general method used in proteomics to identify unknown proteins in a sample. The mass spectrometer measures the masses of several protein's fragments and provide spectra. A spectrum is a series of peaks that indicate the presence of those fragments. By studying these spectra, we aim at retrieving the analyzed protein in a reference databank. In this thesis, we propose a new method to study these spectra. However, our solution must be able to work on proteins coming from unsequenced species, which means that we can't find exactly the same proteins in the databank, only similar ones. At first, we propose a new spectra comparison algorithm: PacketSpectralAlignment. This algorithm allows to compare experimental spectra produced by a mass spectrometer to spectra created from the reference databank data in presence of modifications. This comparison allows to associate to each spectrum, a peptide from the databank. Then, we explain several preprocessing and filtering methods that enhance the results of our new algorithm. All of those methods are used in the SIFpackets framework. Finally, we validate PacketSpectralAlignment and SIFpackets results using several experimental datasets.La spectrométrie de masse est une technique utilisée en protéomique pour identifier des protéines inconnues dans un échantillon. Le spectromètre mesure la masse de fragments de la protéine et fournit ainsi des spectres expérimentaux qui sont des représentations, sous forme de séries de pics, de la présence de ces différents fragments. En étudiant ces spectres, nous espérons pouvoir identifier la protéine d'origine en la retrouvant dans une banque. L'objectif de cette thèse est de proposer de nouvelles méthodes permettant d'étudier ces spectres. Cependant, ces méthodes doivent fonctionner sur des organismes non séquencés. Dans ce cas particulier, nous ne retrouverons pas exactement ces protéines dans la banque, mais uniquement des protéines qui y ressemblent. Nous proposons tout d'abord un nouvel algorithme dit de comparaison de spectres : PacketSpectralAlignment. Cet algorithme permet de comparer des spectres expérimentaux à des spectres créés à partir des données contenues dans la banque, et ce, même en présence de modifications. Cette comparaison permet l'association de chacun des spectres à un peptide de cette banque. Ensuite, nous détaillerons différents prétraitements et filtrages permettant d'améliorer l'exploitation de notre nouvel algorithme. Tous ces éléments sont intégrés dans une plate-forme intitulé SIFpackets. Enfin, nous validons les résultats de PacketSpectralAlignment ainsi que de SIFpackets sur différents jeux de données réelles

Operative list of genes associated with autism and neurodevelopmental disorders based on database review

International audienceThe genetics of neurodevelopmental disorders (NDD) has made tremendous progress during the last few decades with the identification of more than 1,500 genes associated with conditions such as intellectual disability and autism. The functional roles of these genes are currently studied to uncover the biological mechanisms influencing the clinical outcome of the mutation carriers. To integrate the data, several databases and curated gene lists have been generated. Here, we provide an overview of the main databases focusing on the genetics of NDD, that are widely used by the medical and scientific communities, and extract a list of high confidence NDD genes (HC-NDD). This gene set can be used as a first filter for interpreting large scale omics dataset or for diagnostic purposes. Overall HC-NDD genes (N = 1,586) are expressed at very early stages of fetal brain development and enriched in several biological pathways such as chromosome organization, cell cycle, metabolism and synaptic function. Among those HC-NDD genes, 204 (12,9%) are listed in the synaptic gene ontology SynGO and are enriched in genes expressed after birth in the cerebellum and the cortex of the human brain. Finally, we point at several limitations regarding the relatively poor standardized information available, especially on the carriers of the mutations. Progress on the phenotypic characterization and genetic profiling of the carriers will be crucial to improve our knowledge on the biological mechanisms and on risk and protective factors for NDD

Decreased phenol sulfotransferase activities associated with hyperserotonemia in autism spectrum disorders

International audienceHyperserotonemia is the most replicated biochemical abnormality associated with autism spectrum disorders (ASD). However, previous studies of serotonin synthesis, catabolism, and transport have not elucidated the mechanisms underlying this hyperserotonemia. Here we investigated serotonin sulfation by phenol sulfotransferases (PST) in blood samples from 97 individuals with ASD and their first-degree relatives (138 parents and 56 siblings), compared with 106 controls. We report a deficient activity of both PST isoforms (M and P) in platelets from individuals with ASD (35% and 78% of patients, respectively), confirmed in autoptic tissues (9 pineal gland samples from individuals with ASD-an important source of serotonin). Platelet PST-M deficiency was strongly associated with hyperserotonemia in individuals with ASD. We then explore genetic or pharmacologic modulation of PST activities in mice: variations of PST activities were associated with marked variations of blood serotonin, demonstrating the influence of the sulfation pathway on serotonemia. We also conducted in 1645 individuals an extensive study of SULT1A genes, encoding PST and mapping at highly polymorphic 16p11.2 locus, which did not reveal an association between copy number or single nucleotide variations and PST activity, blood serotonin or the risk of ASD. In contrast, our broader assessment of sulfation metabolism in ASD showed impairments of other sulfation-related markers, including inorganic sulfate, heparansulfate, and heparin sulfate-sulfotransferase. Our study proposes for the first time a compelling mechanism for hyperserotonemia, in a context of global impairment of sulfation metabolism in ASD