Biology subjects

Daw, E. W.

Publications and source records attributed to Daw, E. W..

4 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

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits

We present Gene-Embedded Multi-modal Networks (GEM-Net), a semi-supervised framework for constructing multi-modal networks centered on genes. GEM-Net uses gene-level modules and selectively incorporates heterogeneous omics profiles using a correlated meta-analysis strategy that accounts for scale imbalance, missingness, and intra-modular correlation. Prior to network inference, we developed a harmonized data processing protocol that adjusts each omic layer independently through a shared mathematical workflow involving transformation, dimensionality reduction, and regression-based covariate adjustment. GEM-Net modules were inferred and benchmarked against unsupervised methods using transcriptomic, metabolomic, and lipidomic data from the Long Life Family Study (LLFS), a unique cohort enriched for exceptional familial longevity and health. GEM-Net modules were more diverse and biologically interpretable, with stronger support from protein- protein interactions, transcriptional regulation, and metabolic annotations. Applying GEM-Net to metabolic health in LLFS revealed an axis between the microbiome-derived metabolite N-acetylglycine and immune genes (FCER1A, HDC, CPA3, MS4A2) associated with improved insulin sensitivity and reduced inflammation in healthy older individuals. GEM-Nets offer a reusable reference from a long-lived population and a generalizable framework for multi-omics discovery. https://doi.org/10.5281/zenodo.15003731.

systems biology↗

bioRxiv2024

A Paradigm For Calling Sequence In Families: The Long Life Family Study

Over Several years, we have developed a system for assuring the quality of whole genome sequence (WGS) data in the LLFS families. We have focused on providing data to identify germline genetic variants with the aim of releasing as many variants on as many individuals as possible. We aim to assure the quality of the individual calls. The availability of family data has enabled us to use and validate some filters not commonly used in population-based studies. We developed slightly different procedures for the autosomal, X, Y, and Mitochondrial (MT) chromosomes. Some of these filters are specific to family data, but some can be used with any WGS data set. We also describe the procedure we use to construct linkage markers from the SNP sequence data and how we compute IBD values for use in linkage analysis.

genetics↗

bioRxiv2024

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS)

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c ({Delta}HbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed {Delta}HbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing {Delta}HbA1c. Using growth curve modeling adjusted for multiple covariates, we derived {Delta}HbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with {Delta}HbA1c. RNA transcription of ARHGAP44 was associated with {Delta}HbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for {Delta}HbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

genetics↗