Lara, M. K. | Search bioRxiv

bioRxiv2023

Genome-wide association study of delay discounting in Heterogenous Stock rats

Delay discounting refers to the behavioral tendency to devalue rewards as a function of their delay in receipt. Heightened delay discounting has been associated with substance use disorders, as well as multiple co-occurring psychopathologies. Genetic studies in humans and animal models have established that delay discounting is a heritable trait, but only a few specific genes have been associated with delay discounting. Here, we aimed to identify novel genetic loci associated with delay discounting through a genome-wide association study (GWAS) using Heterogenous Stock rats, a genetically diverse outbred population derived from eight inbred founder strains. We assessed delay discounting in 650 male and female rats using an adjusting amount procedure in which rats chose between smaller immediate sucrose rewards or a larger reward at variable delays. Preference switch points were calculated for each rat and both exponential and hyperbolic functions were fitted to these indifference points. Area under the curve (AUC) and the discounting parameter k of both functions were used as delay discounting measures. GWAS for AUC, exponential k, and indifference points for a short delay identified significant loci on chromosomes 20 and 14. The gene Slc35f1, which encodes a member of the solute carrier family of nucleoside sugar transporters, was the only gene within the chromosome 20 locus. That locus also contained an eQTL for Slc35f1, suggesting that heritable differences in the expression of that gene might be responsible for the association with behavior. The gene Adgrl3, which encodes a member of the latrophilin family of G-protein coupled receptors, was the only gene within the chromosome 14 locus. These findings implicate novel genes in delay discounting and highlight the need for further exploration.

genomics↗

bioRxiv2023

A multi-network approach to Alzheimers Disease gene prioritization complements cis-regulatory prioritization with molecular quantitative trait loci

Gene prioritization within mapped disease-risk loci from genome-wide association studies (GWAS) remains one of the central bioinformatic challenges of human genetics. This problem is abundantly clear in Alzheimers Disease (AD) which has several dozen risk loci, but no therapeutically effective drug target. Dominant strategies emphasize alignment between molecular quantitative trait loci (mQTLs) and disease risk loci, under the assumption that cis-regulatory drivers of gene expression or protein abundance mediate disease risk. However, mQTL data do not capture clinically relevant time points or they derive from bulk tissue. These limitations are particularly significant in complex diseases like AD where access to diseased tissue occurs only in end-stage disease, while genetically encoded risk events accumulate over a lifetime. Network-based functional predictions, where bioinformatic databases of gene interaction networks are used to learn disease-associated gene networks to prioritize genes, complement mQTL-based prioritization. The choice of input network, however, can have a profound impact on the output gene rankings, and the optimal tissue network may not be known a priori. Here, we develop a natural extension of the popular NetWAS approach to gene prioritization that allows us to combine information from multiple networks at once. We applied our multi-network (MNFP) approach to AD GWAS data to prioritize candidate genes and compared the results to baseline, single-network models. Finally, we applied the models to prioritize genes in recently mapped AD risk loci and compared our prioritizations to the state-of-the-art mQTL approach used to functionally prioritize genes within those loci. We observed a significant concordance between the top candidates prioritized by our MNFP method and those prioritized by the mQTL approach. Our results show that network-based functional predictions are a strong complement to mQTL-based approaches and are significant to the AD genetics community as they provide a strong functional rationale to mechanistically follow-up novel AD-risk candidates. Author SummaryRisk genes give us insight into the failing molecular mechanisms that drive disease phenotypes. However, these risk genes are several layers of complexity removed from the emergent phenotypes they are influencing, the p-value that denotes their risk status gives little insight into the functional implications of that risk, and it is not clear when that risk gene may be having its effect. Methods like network-based functional prediction start to address several of these limitations by contextualizing risk genes in their broader genetic neighborhood within disease-relevant tissues. For complex diseases like Alzheimers, there are many possible relevant tissues incorporating everything from individual brain cell types to whole lobes of the brain. The work in this paper expands upon the traditional network-based functional prediction approach by considering a genes connections in multiple relevant tissue networks to prioritize candidate genes. Unlike traditional genetic risk studies, this kind prioritization benefits the Alzheimers genetics community as it provides a strong functional rationale to mechanistically follow-up on novel gene candidates.

genetics↗