Showing papers by "Lauren A. Weiss published in 2022"
TL;DR: In this paper , the authors derive testable hypotheses from the Liability Threshold Model (LTM) for ASD, investigating heritability, familial recurrence, correlation between ASD penetrance and sex ratio, population traits, clinical features, the stability of the sex ratio across diagnostic changes, and highlight other key prerequisites.
9 citations
TL;DR: In this article , the authors identify the 33-Mb p-arm of chromosome 16 (16p) as harboring the greatest excess of autism's common polygenic influences, including the mechanistically cryptic and autism-associated 16p11.2 copy number variant.
Abstract: The canonical paradigm for converting genetic association to mechanism involves iteratively mapping individual associations to the proximal genes through which they act. In contrast, in the present study we demonstrate the feasibility of extracting biological insights from a very large region of the genome and leverage this strategy to study the genetic influences on autism. Using a new statistical approach, we identified the 33-Mb p-arm of chromosome 16 (16p) as harboring the greatest excess of autism's common polygenic influences. The region also includes the mechanistically cryptic and autism-associated 16p11.2 copy number variant. Analysis of RNA-sequencing data revealed that both the common polygenic influences within 16p and the 16p11.2 deletion were associated with decreased average gene expression across 16p. The transcriptional effects of the rare deletion and diffuse common variation were correlated at the level of individual genes and analysis of Hi-C data revealed patterns of chromatin contact that may explain this transcriptional convergence. These results reflect a new approach for extracting biological insight from genetic association data and suggest convergence of common and rare genetic influences on autism at 16p.
7 citations
TL;DR: It is concluded that genetic polymorphisms with sexually dimorphic effects on biometric traits not only contribute to fundamental embryogenic processes, but later in life play an outsized role in disease risk.
Abstract: Phenotypic differences across sexes are pervasive, but the genetic architecture of sex differences within and across phenotypes is mostly unknown. In this study, we aimed to improve detection power for sex-differentially contributing SNPs previously demonstrated to be enriched in disease association, and we investigate their functions in health, pathophysiology, and genetic function. We leveraged GIANT and UK Biobank summary statistics and defined a set of 2,320 independent SNPs having sexually dimorphic effects within and across biometric traits (MAF > 0.001, P < 5x10-8). Biometric trait sex-heterogeneous SNPs (sex-het SNPs) showed enrichment in association signals for 20 out of 33 diseases/traits at 5% alpha compared to sex-homogeneous matched SNPs (empP < 0.001), and were significantly overrepresented in muscle, skeletal and stem cell development processes, and in calcium channel and microtubule complexes (FDR < 0.05, empP < 0.05). Interestingly, we found that sex-het SNPs significantly map to predicted expression quantitative trait loci (Pr-eQTLs) across brain and other tissues, methylation quantitative trait loci (meQTLs) during development, and transcription start sites, compared to sex-homogeneous SNPs. Finally, we verified that the sex-het disease/trait enrichment was not explained by Pr-eQTL enrichment alone, as sex-het Pr-eQTLs were more enriched than matched sex-homogeneous Pr-eQTLs. We conclude that genetic polymorphisms with sexually dimorphic effects on biometric traits not only contribute to fundamental embryogenic processes, but later in life play an outsized role in disease risk. These sex-het SNPs disproportionately influence gene expression and have a greater influence on disorders of body and brain than other expression-regulatory variation. Together, our data emphasize the genetic underpinnings of sexual dimorphism and its role in human health.
3 citations
TL;DR: These findings suggest that mechanistic insights for CNV pathology may require combinational models, and it is found that BMI and IQ have a significant association with a regionwide score.
Abstract: The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as the effects of pairs of genes. We generated pairwise expression imputation models for CNV genes and then applied these models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the trait variance explained by pairs with the variance explained with single genes and with traditional interaction models. We also modeled polygene region-wide effects using summed ranks across all genes in the region. In all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes, which was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have a significant association with a regionwide score. Genetic architecture differs by trait and region, but 9/10 CNV-trait combinations showed evidence for multigene contribution, and for most of these, the importance of combinatorial models appeared unique to CNV regions. Our findings suggest that mechanistic insights for CNV pathology may require combinational models.
1 citations