Citations

Abstract Background: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power but often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. Results: In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that the association signals of a subset of top disease risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Conclusions: Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordance came largely from quality differences and if the variants from one batch had better quality scores.

AbstractIntroductionAbnormal gene expression patterns may contribute to the onset and progression of late‐onset Alzheimer's disease (LOAD).MethodsWe performed transcriptome‐wide meta‐analysis (N = 1440) of blood‐based microarray gene expression profiles as well as neuroimaging and cerebrospinal fluid (CSF) endophenotype analysis.ResultsWe identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid beta (Aβ) accumulation, especially in the entorhinal cortex region. cis‐expression quantitative trait loci mapping analysis of CREB5 detected five significant associations (P < 5 × 10−8), where rs56388170 (most significant) was also significantly associated with global cortical Aβ deposition measured by [18F]Florbetapir positron emission tomography and CSF Aβ1‐42.DiscussionRNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to Alzheimer's disease. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity, and learning and memory.

AbstractIntroductionMAPT H1 haplotype is implicated as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD).MethodsUsing Alzheimer's Disease Genetics Consortium (ADGC) genome‐wide association study (GWAS) data (n = 18,841), we conducted a MAPT H1/H2 haplotype–stratified association to discover MAPT haplotype–specific AD risk loci.ResultsWe identified 11 loci—5 in H2‐non‐carriers and 6 in H2‐carriers—although none of the MAPT haplotype–specific associations achieved genome‐wide significance. The most significant H2 non‐carrier–specific association was with a NECTIN2 intronic (P = 1.33E‐07) variant, and that for H2 carriers was near NKX6‐1 (P = 1.99E‐06). The GABRG2 locus had the strongest epistasis with MAPT H1/H2 variant rs8070723 (P = 3.91E‐06). Eight of the 12 genes at these loci had transcriptome‐wide significant differential expression in AD versus control temporal cortex (q < 0.05). Six genes were members of the brain transcriptional co‐expression network implicated in “synaptic transmission” (P = 9.85E‐59), which is also enriched for neuronal genes (P = 1.0E‐164), including MAPT.DiscussionThis stratified GWAS identified loci that may confer AD risk in a MAPT haplotype–specific manner. This approach may preferentially enrich for neuronal genes implicated in synaptic transmission.

AbstractObjectiveTo assess the relationships between MRI volumetry and [18F]flortaucipir PET of typical and atypical clinical phenotypes of Alzheimer’s disease, by genarian (age by decade).MethodsFive‐hundred and sixty‐four participants including those with typical (n = 86) or atypical (n = 80) Alzheimer’s dementia and normal controls (n = 398) underwent apolipoprotein E genotyping, MRI, flortaucipir, and 11C‐PiB; all 166 Alzheimer’s participants were beta‐amyloid positive and all controls were beta‐amyloid negative. Grey matter volume and flortaucipir standard uptake value ratios were calculated for hippocampus, entorhinal cortex, and neocortex. Ratios of hippocampal‐to‐neocortical and entorhinal‐to‐neocortical volume and flortaucipir uptake were also calculated. Linear regression models assessed relationships among regional volume, flortaucipir uptake, and ratios and phenotypes, within three genarians (50–59, 60–69, and 70+). Voxel‐level analyses were also performed.ResultsFor 50–59 greater medial temporal atrophy and flortaucipir uptake was observed in the typical compared with atypical phenotype. The typical phenotype also showed greater frontal neocortex uptake with the voxel‐level analysis. For 60–69 and 70+ there was greater hippocampal volume loss in the typical compared with atypical phenotype while only the 60–69, but not the 70+ group, showed a difference in hippocampal flortaucipir uptake. We also observed a pattern for higher neocortical flortaucipir uptake to correlate with younger age decade for both phenotypes.InterpretationMRI volumetry versus flortaucipir PET relationships differ across Alzheimer’s clinical phenotypes, and also within phenotype across age decades. This suggests that there is potential risk of masked effects by not accounting for genarian in participants with beta‐amyloid and tau‐positive biomarker defined Alzheimer’s disease.

Abstract OBJECTIVE Our goal is to identify new pathways perturbed in and which may lead to GRS. METHODS We collected brain transcriptome levels from healthy controls (n=5), patients with GRS (n=9), glioma without seizures (non-GRS, n=8), and idiopathic temporal lobe epilepsy (iTLE, n=7) using Illumina-based poly-A capture high-throughput RNA sequencing approach. Differential gene expression analysis was conducted to ascertain expression differences between the four groups. We identified molecular pathways enriched for differentially expressed genes (DEG) using Ingenuity Pathway Analysis. DEGs were defined as those with log2 fold change ≥ 2 and q-value < 0.01 when compared to GRS. RESULTS Principal component analysis revealed that diagnostic grouping was the main determinant of the transcriptome variance. We identified 212 DEGs in the GRS vs. non-GRS analysis. Among these, 108 genes were up-regulated and 104 genes were down-regulated. Among the genes that were down‐regulated, there was significant enrichment for those involved in acute phase response signaling and immune cell trafficking (e.g. H19, BMP5, CXCL8). Compared to iTLE samples, there were 1015 DEGs in the GRS samples. Among these 877 genes were up-regulated and 138 genes were down-regulated. There was significant enrichment for genes involved in cell-to-cell signaling among those that were either uniquely up‐regulated (e.g. NRK, RGS10, SLC4A4, and SELPLG) or down‐regulated in the GRS samples (e.g. SLC9A3 –Na+/H+ exchanger; SLC5A5-sodium-glucose co-transporter family). Further, two of the most down-regulated genes in samples with GRS, SCLC17A6- vesicular glutamate transporter 2 and FOSB, have previously been shown to be involved in glutamatergic transmission. CONCLUSIONS Our findings suggest that inflammation-related processes and alterations in cell-to cell signaling may underlie the pathogenesis of GRS. Our results also provide support for perturbations in glutamatergic transmission in GRS. Our findings require replication in larger human cohorts and functional experiments to explore the function of the genes perturbed in GRS.