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Brasher, M. S.

Publications and source records attributed to Brasher, M. S..

3 recordsLinked to original sources

X chromosome association analyses using multiple models identifies 18 genetic loci influencing dietary intake in UK Biobank

Although dietary intake is a leading risk factor for many common diseases, adherence to dietary recommendations remains low. This may partly reflect limited consideration of individual differences in eating behavior that arise from both environmental and genetic factors. While genome-wide association studies (GWAS) of dietary intake have identified hundreds of associated loci, the X chromosome has largely been ignored. To address this gap, we applied multiple X-chromosome-wide association study (X-WAS) models on dietary intake phenotypes to identify novel associations. We performed X-WAS of 46 dietary intake traits from food frequency questionnaires in up to 424,758 European participants from the UK Biobank. Phenotypes included quantitative measures (e.g., fruit intake), binary traits (e.g., decaffeinated vs caffeinated coffee), and principal component-derived food groups. We tested for genetic associations using several models: a traditional sex-combined additive GWAS, additive models stratified by sex, and two joint models accounting for sex-interaction effects and non-additivity. We also conducted X-WAS in five additional genetic ancestry groups and performed a sex-combined multi-ancestry additive GWAS meta-analysis with up to 445,773 individuals. We identified 18 loci associated with 20 dietary intake traits (P < 5x10-8), including 17 variants without prior associations in the GWAS Catalog. Among these loci, 10 were significant across multiple X-WAS models, and 5 were strongest in a model other than the traditional sex-combined additive GWAS, highlighting the value of approaches that address known complexities of the X chromosome. These results demonstrate that incorporating the X chromosome in GWAS can reveal novel loci, even for complex behavioral traits such as dietary intake. Applying multiple association models further improves discovery by accounting for unique features of the X chromosome. Author SummaryAlthough diet is a major risk factor for many common diseases, adherence to healthy eating guidelines remains low. One reason is that current recommendations do not account for individual differences in food choice that arise from environmental or genetic factors. Previous genetic studies have identified hundreds of genetic variants associated with dietary behaviors, but most have excluded the X chromosome due to its analytical complexity and differences between males and females. However, accumulating evidence suggests that the X chromosome contains important genetic variation that impacts complex traits. We analyzed data from hundreds of thousands of individuals to identify genetic variants on the X chromosome associated with dietary intake. To address the unique features of the X chromosome, we applied multiple different models that account for sex-differences and non-additive genetic effects. We identified 18 regions in the genome associated with at least one dietary intake trait. These results reveal new insights into the genetics underlying eating behavior and highlight the importance of incorporating the X chromosome in genetic studies of complex traits.

genetics↗

Transcriptome-Wide Gene-Gene Interaction Association Study Elucidates Pathways and Functional Enrichment of Complex Traits

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover and replicate several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is likely widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.

genetics↗

Testing Human Anxiety Associations of Genes Previously Implicated by Mouse Anxiety Models

Anxiety disorders are common and can be debilitating, with effective treatments remaining hampered by an incomplete understanding of the underlying genetic etiology. Improvements have been made in understanding the genetic influences on mouse behavioral models of anxiety, yet it is unclear the extent to which genes identified in these experimental systems contribute to genetic variation in human anxiety phenotypes. Leveraging new and existing large-scale human genome-wide association studies, we tested whether sets of genes previously identified in mouse anxiety-like behavior studies contribute to a range of human anxiety disorders. When tested as individual genes, thirteen mouse-identified genes were associated with human anxiety phenotypes, suggesting an overlap of individual genes contributing to both mouse models of anxiety-like behaviors and human anxiety traits. When genes were tested as sets, we did identify fourteen significant associations between mouse gene sets and human anxiety, but the majority of gene sets showed no significant association with human anxiety phenotypes. These few significant associations indicate a need to identify and develop more translatable mouse models by identifying sets of genes that match between model systems and specific human phenotypes of interest. We suggest that continuing to develop improved behavioral paradigms and finer-scale experimental data, for instance from individual neuronal subtypes or cell-type-specific expression data, is likely to improve our understanding of the genetic etiology and underlying functional changes in anxiety disorders.

genetics↗