Biology subjects

Moghaddam, V. A.

Publications and source records attributed to Moghaddam, V. A..

2 recordsLinked to original sources

bioRxiv2025

Identification of novel protective loci for executive function using the trail making test part B in the Long Life Family Study

The Trail Making Test (TMT) Part B (TMT-B), a well-established assessment of cognitive function, is a frequent component of diagnostic assessments for Mild Cognitive Impairment and dementia in older adults. Identifying the genetic variants associated with the TMT-B will not only gain insights of genetic determinants of cognitive function, but also the molecular mechanisms for dementia. Published GWAS to date for TMT-B suffer from relatively low power due to the use of population level data and imputation methods. To address these deficits, we used a family-based study design to identify the genetic variants associated with the TMT-B incorporating both genome-wide linkage analysis (GWLS) and whole genome sequencing (WGS). As such, we examined the sequenced genetic determinants of TMT-B using GWLS in over 2000 participants from Long Life Family Study (LLFS). In GWLS, the estimated heritability of TMT-B was 0.29. We detected one significant linkage peak at 15q25 (LOD>3.0). Statistical fine-mapping nominated five variants including three SNPs (NTRK3-rs74031103, protective CEMIP-rs2271159, and protective AGBL1-rs4134376) and two INDELs (protective KLHL25-15:85882445:IND, and protective CEMIP-15:80893381:IND) contributing to the linkage peak. Four out of these five variants are protective for TMT-B. The rs2271159 SNP influences CEMIP expression in cerebellum and hippocampus, while the 15:80893381:IND modulates CEMIP expression in blood. Additionally, the variant rs4134376 is a basal ganglia-specific eQTL for AGBL1. In conclusion, we utilized GWLS, leveraged multi-omics data (whole genome sequence genomic data, transcriptomic data, and lipidomic data), and identified novel protective variants and genes for TMT-B performance.

genomics↗

bioRxiv2025

FISHNET: A Network-based Tool for Analyzing Gene-level P-values to Identify Significant Genes Missed by Standard Methods

FISHNET uses prior biological knowledge, represented as gene interaction networks and gene function annotations, to identify genes that do not meet the genome-wide significance threshold but replicate nonetheless. Its input is gene-level P-values from any source, including omicsWAS, aggregation of GWAS P-values, CRISPR screens, or differential expression analysis. It is based on the idea that genes whose P-values are low due to sampling error are distributed randomly across networks and functions, so genes with suggestive P-values that cluster in densely connected subnetworks and share common functions are less likely to reflect sampling error and more likely to replicate. FISHNET combines network and function analysis with permutation-based P-value thresholds to identify a small set of exceptional genes that we call FISHNET genes. Applied to 11 cardiovascular risk traits, FISHNET identified 19 gene-trait relationships that missed genome-wide significance thresholds but, nonetheless, replicated in an independent cohort. The replication rate of FISHNET genes matched or exceeded that of other genes with similar P-values. FISHNET identified a novel association between RUNX1 expression and HDL that is supported by experimental evidence that RUNX1 promotes white fat browning, which increases HDL cholesterol levels. FISHNET also identified an association between LTB expression and BMI that is supported by experimental evidence that higher LTB expression increases BMI via activation of the LT{beta}R pathway. Both associations failed genome-wide significance thresholds, highlighting FISHNETs ability to uncover meaningful relationships missed by traditional methods. FISHNET software is freely available at https://doi.org/10.5281/zenodo.14765850.

bioinformatics↗