Author Correction: The genetic landscape of autism spectrum disorder in an ancestrally diverse cohort
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Polygenic scores for autism are associated with reduced neurite density in adults and children from the general population
Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.
A multimodal neural signature of face processing in autism within the fusiform gyrus
Atypical face processing is commonly reported in autism. Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how variation in brain anatomy and function jointly impacts face processing and social functioning. Here we leveraged a large multimodal sample to study the cross-modal signature of face processing within the FFG across four imaging modalities (structural magnetic resonance imaging (MRI), resting-state functional magnetic resonance imaging, task-functional magnetic resonance imaging and electroencephalography) in 204 autistic and nonautistic individuals aged 7–30 years (case–control design). We combined two methodological innovations—normative modeling and linked independent component analysis—to integrate individual-level deviations across modalities and assessed how multimodal components differentiated groups and informed social functioning in autism. Groups differed significantly in a multimodal component driven by bilateral resting-state functional MRI, bilateral structure, right task-functional MRI and left electroencephalography loadings in face-selective and retinotopic FFG. Multimodal components outperformed unimodal ones in differentiating groups. In autistic individuals, multimodal components were associated with cognitive and clinical features linked to social, but not nonsocial, functioning. These findings underscore the importance of elucidating multimodal neural associations of social functioning in autism, offering potential for the identification of mechanistic and prognostic biomarkers.
Associations between common genetic variants and income provide insights about the socio-economic health gradient
We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1–5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.
Deep learning-driven semantic segmentation and spatial analysis of quarry relic landscapes using point cloud data: insights from the Shaoxing quarry relics
Quarry relic landscapes hold significant historical and cultural value, yet current research often lacks the depth to understand their complex spatial structure. This study addresses this gap by utilizing 3D point cloud data and deep learning to analyze quarry relic landscapes, focusing on the Shaoxing quarry relics. In this paper, point cloud models of four quarry relic landscapes were established, as well as the performance of the PointNet + + network in segmenting complex and variable quarry relic landscape spaces. Based on the semantic segmentation results, quantitative and clustering analyses were conducted on various landscape elements of the four quarry relics, thereby exploring the cultural value of Shaoxing quarry relic’s heritage. The study demonstrates the following key findings: 1. The feasibility of combining 3D laser scanning and UAV photogrammetry to gather detailed site data for documenting quarry relic landscapes has been proven. 2. The PointNet + + deep learning network is particularly effective for the semantic segmentation of landscape elements associated with quarry relics. 3. The Shaoxing quarry relic exhibits a composite spatial form, with a nearly equal ratio of positive to negative space (approximately 1:1). Plants and bare rocks predominantly occupy the positive space, while rocks and stone pits exhibit the highest heritage value. 4. The development of the QLIM&PMS system has facilitated the comprehensive digitalization of the quarry relic landscape, offering examples and technical support for the preservation and utilization of quarry relic sites.
The genetic landscape of autism spectrum disorder in an ancestrally diverse cohort
Autism spectrum disorder (ASD) comprises neurodevelopmental disorders with wide variability in genetic causes and phenotypes, making it challenging to pinpoint causal genes. We performed whole exome sequencing on a modest, ancestrally diverse cohort of 195 families, including 754 individuals (222 with ASD), and identified 38,834 novel private variants. In 68 individuals with ASD (~30%), we identified 92 potentially pathogenic variants in 73 known genes, including BCORL1, CDKL5, CHAMP1, KAT6A, MECP2, and SETD1B. Additionally, we identified 158 potentially pathogenic variants in 120 candidate genes, including DLG3, GABRQ, KALRN, KCTD16, and SLC8A3. We also found 34 copy number variants in 31 individuals overlapping known ASD loci. Our work expands the catalog of ASD genetics by identifying hundreds of variants across diverse ancestral backgrounds, highlighting convergence on nervous system development and signal transduction. These findings provide insights into the genetic underpinnings of ASD and inform molecular diagnosis and potential therapeutic targets.
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