Quantitative trait locus analysis for endophenotypes reveals genetic substrates of core symptom domains and neurocognitive function in autism spectrum disorder

Five interdisciplinary tensions and opportunities in neurodiversity research

Improving our understanding of autism, ADHD, dyslexia and other neurodevelopmental conditions requires collaborations between genetics, psychiatry, the social sciences and other fields of research.

Association between Neuroligin-1 polymorphism and plasma glutamine levels in individuals with autism spectrum disorder

BACKGROUND

Unravelling the relationships between candidate genes and autism spectrum disorder (ASD) phenotypes remains an outstanding challenge. Endophenotypes, defined as inheritable, measurable quantitative traits, might provide intermediary links between genetic risk factors and multifaceted ASD phenotypes. In this study, we sought to determine whether plasma metabolite levels could serve as endophenotypes in individuals with ASD and their family members.

METHODS

We employed an untargeted, high-resolution metabolomics platform to analyse 14,342 features across 1099 plasma samples. These samples were collected from probands and their family members participating in the Autism Genetic Resource Exchange (AGRE) (N = 658), compared with neurotypical individuals enrolled in the PrecisionLink Health Discovery (PLHD) program at Boston Children's Hospital (N = 441). We conducted a metabolite quantitative trait loci (mQTL) analysis using whole-genome genotyping data from each cohort in AGRE and PLHD, aiming to prioritize significant mQTL and metabolite pairs that were exclusively observed in AGRE.

FINDINGS

Within the AGRE group, we identified 54 significant associations between genotypes and metabolite levels (P < 5.27 × 10-11), 44 of which were not observed in the PLHD group. Plasma glutamine levels were found to be associated with variants in the NLGN1 gene, a gene that encodes post-synaptic cell-adhesion molecules in excitatory neurons. This association was not detected in the PLHD group. Notably, a significant negative correlation between plasma glutamine and glutamate levels was observed in the AGRE group, but not in the PLHD group. Furthermore, plasma glutamine levels showed a negative correlation with the severity of restrictive and repetitive behaviours (RRB) in ASD, although no direct association was observed between RRB severity and the NLGN1 genotype.

INTERPRETATION

Our findings suggest that plasma glutamine levels could potentially serve as an endophenotype, thus establishing a link between the genetic risk associated with NLGN1 and the severity of RRB in ASD. This identified association could facilitate the development of novel therapeutic targets, assist in selecting specific cohorts for clinical trials, and provide insights into target symptoms for future ASD treatment strategies.

FUNDING

This work was supported by the National Institute of Health (grant numbers: R01MH107205, U01TR002623, R24OD024622, OT2OD032720, and R01NS129188) and the PrecisionLink Biobank for Health Discovery at Boston Children's Hospital.

Characterization of Vps13b-mutant mice reveals neuroanatomical and behavioral phenotypes with females less affected

The vacuolar protein sorting-associated protein 13B (VPS13B) is a large and highly conserved protein. Disruption of VPS13B causes the autosomal recessive Cohen syndrome, a rare disorder characterized by microcephaly and intellectual disability among other features, including developmental delay, hypotonia, and friendly-personality. However, the underlying mechanisms by which VPS13B disruption leads to brain dysfunction still remain unexplained. To gain insights into the neuropathogenesis of Cohen syndrome, we systematically characterized brain changes in Vps13b-mutant mice and compared murine findings to 235 previously published and 17 new patients diagnosed with VPS13B-related Cohen syndrome. We showed that Vps13b is differentially expressed across brain regions with the highest expression in the cerebellum, the hippocampus and the cortex with postnatal peak. Half of the Vps13b-/- mice die during the first week of life. The remaining mice have a normal lifespan and display the core phenotypes of the human disease, including microcephaly, growth delay, hypotonia, altered memory, and enhanced sociability. Systematic 2D and 3D brain histo-morphological analyses reveal specific structural changes in the brain starting after birth. The dentate gyrus is the brain region with the most prominent reduction in size, while the motor cortex is specifically thinner in layer VI. The fornix, the fasciculus retroflexus, and the cingulate cortex remain unaffected. Interestingly, these neuroanatomical changes implicate an increase of neuronal death during infantile stages with no progression in adulthood suggesting that VPS13B promotes neuronal survival early in life. Importantly, whilst both sexes were affected, some neuroanatomical and behavioral phenotypes were less pronounced or even absent in females. We evaluate sex differences in Cohen patients and conclude that females are less affected both in mice and patients. Our findings provide new insights about the neurobiology of VPS13B and highlight previously unreported brain phenotypes while defining Cohen syndrome as a likely new entity of non-progressive infantile neurodegeneration.

What are quantitative traits and how can they be used in autism research?

Quantitative traits are measurable characteristics distributed along a continuous scale thought to relate to underlying biology. There is growing interest in the use of quantitative traits in behavioral and psychiatric research, particularly in research on conditions diagnosed based on reports of behaviors, including autism. This brief commentary describes quantitative traits, including defining what they are, how we can measure them, and key considerations for their use in autism research. Examples of measures include behavioral report scales like the Social Responsiveness Scale and Broader Autism Phenotype Questionnaire, as well as biological measurements, like certain neuroimaging metrics; such measures can capture quantitative traits or constructs like the broader autism phenotype, social communication, and social cognition. Quantitative trait measures align with the Research Domain Criteria (RDoC) approach and can be used in autism research to help gain a better understanding of causal pathways and biological processes. They can also be used to aid identification of genetic and environmental factors involved in such pathways, and thereby lead to an understanding of influences on traits across the entire population. Finally, in some cases, they may be used to gauge treatment response, and assist screening and clinical characterization of phenotype. In addition, practical benefits of quantitative trait measures include improved statistical power relative to categorical classifications and (for some measures) efficiency. Ultimately, research across autism fields may benefit from incorporating quantitative trait measures as a complement to categorical diagnosis to advance understanding of autism and neurodevelopment.