Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.

Alzheimer disease (AD) specific transcription, DNA methylation and splicing in twenty AD associated loci.

Genome-wide association studies have identified twenty loci associated with late-onset Alzheimer disease (LOAD). We examined each of the twenty loci, specifically the ±50kb region surrounding the most strongly associated variant, for changes in gene(s) transcription specific to LOAD. Post-mortem human brain samples were examined for expression, methylation, and splicing differences. LOAD specific differences were detected by comparing LOAD to normal and "disease" controls. Eight loci, prominently ABCA7, contain LOAD specific differences. Significant changes in the CELF1 and ZCWPW1 loci occurred in genes not located nearest the associated variant, suggesting that these genes should be investigated further as LOAD candidates.

Integrated whole transcriptome and DNA methylation analysis identifies gene networks specific to late-onset Alzheimer's disease.

Previous transcriptome studies observed disrupted cellular processes in late-onset Alzheimer's disease (LOAD), yet it is unclear whether these changes are specific to LOAD, or are common to general neurodegeneration. In this study, we address this question by examining transcription in LOAD and comparing it to cognitively normal controls and a cohort of "disease controls." Differential transcription was examined using RNA-seq, which allows for the examination of protein coding genes, non-coding RNAs, and splicing. Significant transcription differences specific to LOAD were observed in five genes: C10orf105, DIO2, a lincRNA, RARRES3, and WIF1. These findings were replicated in two independent publicly available microarray data sets. Network analyses, performed on 2,504 genes with moderate transcription differences in LOAD, reveal that these genes aggregate into seven networks. Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD. Of the five significant genes, WIF1 and RARRES3 are directly implicated in the myelination process; the other three genes are located within the network. LOAD specific changes in DNA methylation were located throughout the genome and substantial changes in methylation were identified within the myelination network. Splicing differences specific to LOAD were observed across the genome and were decreased in all seven networks. DNA methylation had reduced influence on transcription within LOAD in the myelination network when compared to both controls. These results hint at the molecular underpinnings of LOAD and indicate several key processes, genes, and networks specific to the disease.

Rare Variants and Transcriptomics in Alzheimer disease.

Alzheimer disease (AD) is the most common dementia in the elderly, still without effective treatment. Early-onset AD (EOAD) is caused by mutations in the genes APP, PSEN1 and PSEN2. Genome-wide association studies have identified >20 late-onset AD (LOAD) susceptibility genes with common variants of small risk, with the exception of APOE. We review rare susceptibility variants in LOAD with larger effects that have been recently identified in the EOAD gene APP and the newly discovered AD genes TREM2 and PLD3. Human genetic studies now consistently support the amyloid hypothesis of AD for both EOAD and LOAD. Moreover, they identified biological processes that overlap with human transcriptomics studies in AD across different tissues, such as inflammation, cytoskeletal organization, synaptic functions, etc. Transcriptomic profiles of pre-symptomatic AD-associated variant carriers already reflect specific molecular mechanisms reminiscent to those of AD patients. This might provide an avenue for personalized medicine.