Biology subjects

Rhemtulla, M.

Publications and source records attributed to Rhemtulla, M..

3 recordsLinked to original sources

bioRxiv2026

Ultra-Fast Implementation of Multivariate GWAS in Genomic SEM Using Flexible Analytic Estimation

Many medical, physiological, and psychiatric traits and disorders are highly polygenic and exhibit complex patterns of genetic sharing and differentiation. In 2018, we introduced Genomic Structural Equation Modelling (Genomic SEM) as a formal framework and free, open source, R-based software for modelling the multivariate genetic architecture of both continuous and binary Genome-Wide Association Study (GWAS) phenotypes, interrogating their joint and distinct functional genomic pathways, and leveraging empirical models of the genetic relations among phenotypes to guide multivariate GWAS discovery. Here we introduce a closed-form analytic solution for estimating SNP effects within multivariate GWAS in Genomic SEM. This estimator is over 800 times faster than the existing iterative estimator, drastically decreasing reliance on high performance computing (HPC). On a MacBook pro laptop with M4 Max chip, a multivariate GWAS ([~]1M SNPs) of 5 common factors underlying 13 phenotypes takes approximately 2 minutes. Concurrent with the release of this preprint, we are adding an analytic estimation option to the userGWAS function in the GenomicSEM package for alpha testing along with a tutorial on our GitHub wiki.

genetics↗

bioRxiv2018

Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced \"atlases\" of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

genetics↗

bioRxiv2018

Fatherhood alters gene expression within the MPOA

Female parenting is obligate in mammals, but fathering behavior among mammals is rare. Only 3-5% of mammalian species exhibit biparental care, including humans, and mechanisms of fathering behavior remain sparsely studied. However, in species where it does exist, paternal care is often crucial to the survivorship of offspring. The present study is the first to identify new gene targets linked to the experience of fathering behavior in a biparental species using RNA sequencing. In order to determine the pattern of gene expression within the medial preoptic area that is specifically associated with fathering behavior, we identified differentially expressed genes in male prairie voles (Microtus ochrogaster) that experienced one of three social conditions: virgin males, pair bonded males, and males with fathering experience. Differentially expressed genes from each comparison (i.e., Virgin vs Paired, Virgin vs Fathers, and Paired vs Fathers) were evaluated using the Gene Ontology enrichment analysis, and Kegg pathways analysis to reveal metabolic pathways associated with specific differentially expressed genes. Using these tools, we identified a group of genes that are differentially expressed in voles with different amounts of social experience. These genes are involved in a variety of processes, with particular enrichment in genes associated with immune function, metabolism, synaptic plasticity, and the remodeling of dendritic spines. The identification of these genes and processes will lead to novel insights into the biological basis of fathering behavior.

neuroscience↗