Genetic architecture of age-related cognitive decline in African Americans

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

Trans-pQTL study identifies immune crosstalk between Parkinson and Alzheimer loci

Objective: Given evidence from genetic studies, we hypothesized that there may be a shared component to the role of myeloid function in Parkinson and Alzheimer disease (PD and AD) and assessed whether PD susceptibility variants influenced protein expression of well-established AD-associated myeloid genes in human monocytes. Methods: We repurposed data in which AD-related myeloid proteins CD33, TREM1, TREM2, TREML2, TYROBP, and PTK2B were measured by flow cytometry in monocytes from 176 participants of the PhenoGenetic Project (PGP) and Harvard Aging Brain Study. Linear regression was used to identify associations between 24 PD risk variants and protein expression. The 2 cohorts were meta-analyzed in a discovery analysis, and the 4 most strongly suggestive results were validated in an independent cohort of 50 PGP participants. Results: We discovered and validated an association between the PD risk allele rs12456492G in the RIT2 locus and increased CD33 expression (pjoint = 3.50 × 10−5) and found strongly suggestive evidence that rs11060180A in the CCDC62/HIP1R locus decreased PTK2B expression (pjoint = 1.12 × 10−4). Furthermore, in older individuals, increased CD33 expression on peripheral monocytes was associated with a greater burden of parkinsonism (p = 0.047), particularly bradykinesia (p = 6.64 × 10−3). Conclusions: We find that the rs12456492 PD risk variant affects expression of AD-associated protein CD33 in peripheral monocytes, which suggests that genetic factors for these 2 diseases may converge to influence overlapping innate immune-mediated mechanisms that contribute to neurodegeneration. Furthermore, the effect of the rs12456492G PD risk allele on increased CD33 suggests that the inhibition of certain myeloid functions may contribute to PD susceptibility, as is the case for AD

Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a genetically complex autoimmune disease characterized by loss of immune tolerance to nuclear and cell surface antigens. Previous genome-wide association studies (GWAS) had modest sample sizes, reducing their scope and reliability. Our study comprised 7,219 cases and 15,991 controls of European ancestry, constituting a new GWAS, a meta-analysis with a published GWAS and a replication study. We have mapped 43 susceptibility loci, including ten new associations. Assisted by dense genome coverage, imputation provided evidence for missense variants underpinning associations in eight genes. Other likely causal genes were established by examining associated alleles for cis-acting eQTL effects in a range of ex vivo immune cells. We found an over-representation (n = 16) of transcription factors among SLE susceptibility genes. This finding supports the view that aberrantly regulated gene expression networks in multiple cell types in both the innate and adaptive immune response contribute to the risk of developing SLE

SPRAINS OF THE RHOMBOIDEUS MINOR MUSCLE: A STUDY OF ONE HUNDRED AND FIFTY INJURIES OF THE SHOULDER GIRDLE

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Mapping rich genotype-phenotype landscapes with single-cell CRISPR screens

A central goal of genetics is to define the phenotypic consequences of genetic perturbations. Single-cell CRISPR screens such as Perturb-seq (pooled single-cell RNA-sequencing CRISPR screens) represent an emerging tool to systematically construct genotype-phenotype maps by pairing high-dimensional genetic perturbations with rich phenotypic readouts in single cells. However, to date, these screens have been deployed at a limited scale.The first chapter of this thesis addresses a major technological hinderance to the scalable application of Perturb-seq: reliance on indirect indexing of single-guide RNAs (sgRNAs). I present direct-capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct-capture Perturb-seq enables detection of multiple distinct sgRNA sequences from individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries that contain dual-guide expression vectors. I then demonstrate the utility of this approach for high-throughput investigations of genetic interactions and, leveraging this ability, dissect epistatic interactions between cholesterol biogenesis and DNA repair. Using direct capture Perturb-seq, I also show that targeting individual genes with multiple sgRNAs per cell improves the efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for large-scale single-cell screens. Last, I show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.The second chapter builds on the first chapter by applying direct capture Perturb-seq and multiplexed CRISPR interference (CRISPRi) to perform the first genome-scale Perturb-seq screens. From these data, I yield a blueprint for the construction and analysis of rich genotype-phenotype maps. I show that genes can be clustered by transcriptional phenotypes across many essential cellular processes and reveal new roles for poorly characterized genes in ribosome biogenesis, transcription, and respiration. Beyond clustering genes, these data enable in-depth dissection of the functional consequences of genetic perturbations on a remarkable array of complex, composite phenotypes—including RNA processing, differentiation, and chromosomal instability. Leveraging this ability, I comprehensively identify genetic drivers and consequences of aneuploidy, and I uncover unanticipated perturbation-specific regulation of the mitochondrial genome. This thesis establishes Perturb-seq as a scalable tool for the principled exploration of multidimensional cellular behaviors, gene function, and regulatory networks

"Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq" Replogle et al. 2022 - commonly requested supplemental files

Commonly requested supplemental files supporting the publication:   Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq, Replogle, Joseph M. et al. Cell, Volume 185, Issue 14, 2559 - 2575.e28 https://doi.org/10.1016/j.cell.2022.05.013: 1) All Anderson-Darling p-values for differential expression analysis 2) Embedding coordinates for Figure 2D 3) Downloadable interactive scatter plot of Fig. 2D 4) Z-normalized expression data used to make Figs. 2 and 4, annotated with the cluster identities from Supplementary Table 3 See related datasets at:  https://doi.org/10.25452/figshare.plus.20029387 https://doi.org/10.25452/figshare.plus.20127869 https://doi.org/10.25452/figshare.plus.20022944</p

Common risk alleles for inflammatory diseases are targets of recent positive selection

Genome-wide association studies (GWASs) have identified hundreds of loci harboring genetic variation influencing inflammatory-disease susceptibility in humans. It has been hypothesized that present day inflammatory diseases may have arisen, in part, due to pleiotropic effects of host resistance to pathogens over the course of human history, with significant selective pressures acting to increase host resistance to pathogens. The extent to which genetic factors underlying inflammatory-disease susceptibility has been influenced by selective processes can now be quantified more comprehensively than previously possible. To understand the evolutionary forces that have shaped inflammatory-disease susceptibility and to elucidate functional pathways affected by selection, we performed a systems-based analysis to integrate (1) published GWASs for inflammatory diseases, (2) a genome-wide scan for signatures of positive selection in a population of European ancestry, (3) functional genomics data comprised of protein-protein interaction networks, and (4) a genome-wide expression quantitative trait locus (eQTL) mapping study in peripheral blood mononuclear cells (PBMCs). We demonstrate that loci for inflammatory-disease susceptibility are enriched for genomic signatures of recent positive natural selection, with selected loci forming a highly interconnected protein-protein interaction network. Further, we identify 21 loci for inflammatory-disease susceptibility that display signatures of recent positive selection, of which 13 also show evidence of cis-regulatory effects on genes within the associated locus. Thus, our integrated analyses highlight a set of susceptibility loci that might subserve a shared molecular function and has experienced selective pressure over the course of human history; today, these loci play a key role in influencing susceptibility to multiple different inflammatory diseases, in part through alterations of gene expression in immune cells

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Abstract Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds