Biology subjects

Honig, L.

Publications and source records attributed to Honig, L..

3 recordsLinked to original sources

bioRxiv2024

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

Alzheimers Disease (AD) exhibits a complex molecular and phenotypic profile. Investigating gene expression plays a crucial role in unraveling the diseases etiology and progression. Transcriptome data across ethnic groups lack, negatively impacting equity in intervention and research. We employed 565 brains across six U.S. brain banks (n=399 non-Hispanic Whites, n=113 Hispanics, n=12 African Americans) to generated bulk RNA sequencing from prefrontal cortex. We sought to identify cross-ancestry and ancestry-specific differentially expressed genes (DEG) across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We further validated our findings using the Religious Orders Study/Memory Aging Project brains (ROS/MAP; n=1,095) and performed metanalysis (n=1,660). We conducted Gene Set and Variation and Enrichment analysis (GSVA; GSEA). We employed a machine-learning approach for phenotype prediction and gene prioritization to construct a polytranscriptomics risk score (PTRS) splitting our sample into training and testing sub-samples, either randomly or by ethnicity ("ancestry-agnostic" and "ancestry-aware", respectively). Lastly, we validated top DEG using single-nucleus RNA sequencing (snRNAseq) data. We identified several DEG associated with Braak staging: AD-known genes VGF (Padj =3.78E- 07) and ADAMTS2 (Pad j=1.21E-04) were consistently differentially expressed across statistical models, ethnicities, and replicated in ROS/MAP. Genes from the heat shock protein (HSP) family, e.g. HSPB7 (Padj =3.78E-07), were the top differentially expressed genes and replicated in ROS/MAP. Ethnic-stratified analyses prioritized TNFSF14 and SPOCD1 as top Hispanics DEG. GSEA highlighted "Alzheimer disease" (Padj =4.24E-06) and "TYROBP causal network in microglia" (Padj =1.68E-08) pathways. Up- and down-regulated genes were enriched in several pathways (e.g. "Immune response activation signal pathways", "Vesicle-mediated transport in synapse", "cognition"). Ancestry-agnostic and ancestry-aware PTRS effectively classified brains (AUC=0.77 and 0.73 respectively) and replicated in ROS/MAP. snRNAseq validated prioritized genes, including VGF (downregulated in neurons; Padj=1.1 E-07). This is the largest diverse AD transcriptome in post-mortem brain tissue, to our knowledge. We identified perturbated genes, pathways and network expressions in AD brains resulting in cross- ethnic and ethnic-specific findings, ultimately highlighting the diversity within AD pathogenesis. The latter underscores the need for an integrative and personalized approach in AD studies.

genetics↗

bioRxiv2019

Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs

BackgroundMapping of allele-specific DNA methylation (ASM) can be a post-GWAS strategy for localizing regulatory sequence polymorphisms (rSNPs). However, the advantages of this approach, and the mechanisms underlying ASM in normal and neoplastic cells, remain to be clarified. ResultsWe performed whole genome methyl-seq on diverse normal cells and tissues and three types of cancers (multiple myeloma, lymphoma, glioblastoma multiforme). After excluding imprinting, the data pinpointed 15,114 high-confidence ASM differentially methylated regions (DMRs), of which 1,842 contained SNPs in strong linkage disequilibrium or coinciding with GWAS peaks. ASM frequencies were increased 5 to 9-fold in cancers vs. matched normal tissues, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in poised chromatin. Cancers showed increased allele switching at ASM loci, but disruptive SNPs in specific classes of CTCF and transcription factor (TF) binding motifs were similarly correlated with ASM in cancer and non-cancer. Rare somatic mutations affecting these same motif classes tracked with de novo ASM in the cancers. Allele-specific TF binding from ChIP-seq was enriched among ASM loci, but most ASM DMRs lacked such annotations, and some were found in otherwise uninformative "chromatin deserts". ConclusionsASM is increased in cancers but occurs by a shared mechanism involving disruptive SNPs in CTCF and TF binding sites in both normal and neoplastic cells. Dense ASM mapping in normal plus cancer samples reveals candidate rSNPs that are difficult to find by other approaches. Together with GWAS data, these rSNPs can nominate specific transcriptional pathways in susceptibility to autoimmune, neuropsychiatric, and neoplastic diseases. Custom genome browser tracks with annotated ASM loci can be viewed at a UCSC browser session hosted by our laboratory (https://bit.ly/tycko-asm)

genomics↗

bioRxiv2018

Heritability and genetic variance of dementia with Lewy bodies

Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinsons disease (PD) or Alzheimers disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

neuroscience↗