44 research outputs found

    Genetic architecture of age-related cognitive decline in African Americans

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    Objective: To identify genetic risk factors associated with susceptibility to age-related cognitive decline in African Americans (AAs). Methods: We performed a genome-wide association study (GWAS) and an admixture-mapping scan in 3,964 older AAs from 5 longitudinal cohorts; for each participant, we calculated a slope of an individual's global cognitive change from neuropsychological evaluations. We also performed a pathway-based analysis of the age-related cognitive decline GWAS. Results: We found no evidence to support the existence of a genomic region which has a strongly different contribution to age-related cognitive decline in African and European genomes. Known Alzheimer disease (AD) susceptibility variants in the ABCA7 and MS4A loci do influence this trait in AAs. Of interest, our pathway-based analyses returned statistically significant results highlighting a shared risk from lipid/metabolism and protein tyrosine signaling pathways between cognitive decline and AD, but the role of inflammatory pathways is polarized, being limited to AD susceptibility. Conclusions: The genetic architecture of aging-related cognitive in AA individuals is largely similar to that of individuals of European descent. In both populations, we note a surprising lack of enrichment for immune pathways in the genetic risk for cognitive decline, despite strong enrichment of these pathways among genetic risk factors for AD

    Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

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    Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk

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    Multiple sclerosis is a complex neurological disease, with 3c20% of risk heritability attributable to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that much of the remaining heritability is also due to additive effects of common variants rather than epistasis between these variants or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that up to 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common-variant signals, highlighting key pathogenic roles for regulatory T cell homeostasis and regulation, IFN\u3b3 biology, and NF\u3baB signaling. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS. In a large multi-cohort study, unexplained heritability for multiple sclerosis is detected in low-frequency coding variants that are missed by GWAS analyses, further underscoring the role of immune genes in MS pathology

    The effect of background noise and its removal on the analysis of single-cell expression data

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    Abstract Background In droplet-based single-cell and single-nucleus RNA-seq experiments, not all reads associated with one cell barcode originate from the encapsulated cell. Such background noise is attributed to spillage from cell-free ambient RNA or barcode swapping events. Results Here, we characterize this background noise exemplified by three scRNA-seq and two snRNA-seq replicates of mouse kidneys. For each experiment, cells from two mouse subspecies are pooled, allowing to identify cross-genotype contaminating molecules and thus profile background noise. Background noise is highly variable across replicates and cells, making up on average 3–35% of the total counts (UMIs) per cell and we find that noise levels are directly proportional to the specificity and detectability of marker genes. In search of the source of background noise, we find multiple lines of evidence that the majority of background molecules originates from ambient RNA. Finally, we use our genotype-based estimates to evaluate the performance of three methods (CellBender, DecontX, SoupX) that are designed to quantify and remove background noise. We find that CellBender provides the most precise estimates of background noise levels and also yields the highest improvement for marker gene detection. By contrast, clustering and classification of cells are fairly robust towards background noise and only small improvements can be achieved by background removal that may come at the cost of distortions in fine structure. Conclusions Our findings help to better understand the extent, sources and impact of background noise in single-cell experiments and provide guidance on how to deal with it
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