Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Jagadeesh, Karthik A.; Dey, Kushal Kumar; Montoro, Daniel T.; Mohan, Rahul; Gazal, Steven; Engreitz, Jesse M.; Xavier, Ramnik J.; Price, Alkes L.; Regev, Aviv

doi:10.1101/2021.03.19.436212

Cited by 34 publications

(33 citation statements)

References 194 publications

(366 reference statements)

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“…The estimates of proportions of h 2 gene explained by the top 200 genes varied widely across diseases/traits, from 255% (neuroticism) to 404% (height) to 8618% (total cholesterol) (Supplementary Table 20; also see Ge estimates below). Results were similar when restricting to genes expressed in disease-critical cell-types 74 (as proposed in ref. 44 ) (Supplementary Figure 6).…”

Section: Leveraging the Combined S2g Strategy To Empirically Assess Disease Omnigenicitysupporting

confidence: 60%

“…For example, in contrast to the prevailing approach of applying S-LDSC 11 to gene sets using ±100kb windows to define SNP annotations 50,80 , using cS2G to define SNP annotations produced larger heritability enrichments and standardized effect sizes (Supplementary Figure 10; also see ref. 74 ); we further note the importance of including appropriate SNP annotations in the model used by S-LDSC in analyses of enriched gene sets, in order to avoid biased enrichment estimates (see Methods and Supplementary Figure 11). Investigating the relative performance of different combined S2G strategies in analyses of gene sets that are enriched for disease heritability is a direction for future research; although we have focused here on S2G strategies defined using all available tissues/cell-types (see Supplementary Figure 3), tissuespecific S2G strategies may be preferred when analyzing gene sets reflecting genes that are specifically expressed in a particular tissue/cell-type 74 (or when larger data sets become available; see below).…”

Section: Discussionmentioning

confidence: 99%

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Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

Gazal

Weissbrod

Hormozdiari

et al. 2021

Preprint

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Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies.

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Section: Leveraging the Combined S2g Strategy To Empirically Assess Disease Omnigenicitysupporting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

Gazal

Weissbrod

Hormozdiari

et al. 2021

Preprint

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“…Third, the choice of gene sets that we have analyzed is not comprehensive. In particular, analysis of specifically expressed gene sets derived from single-cell RNA-seq data [53] is a promising direction for future research. Fourth, GCSC may be impacted by pleiotropic effects involving SNPs that independently impact gene expression and disease risk (analogous to TWAS).…”

Section: Discussionmentioning

confidence: 99%

Leveraging gene co-regulation to identify gene sets enriched for disease heritability

Siewert

Kim

Yao

et al. 2021

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Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWAS) can be used to detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a new method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by the predicted expression of causal disease genes in the gene set. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; GCSC determines that a gene set is enriched for disease heritability if genes with high co-regulation to the gene set have higher TWAS chi-square statistics than genes with low co-regulation to the gene set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well-calibrated, and well-powered to identify gene sets that are enriched for disease heritability explained by predicted expression. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits (average N=344K), analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched gene sets, recapitulating known biology. For Alzheimer's disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify gene sets associated to disease.

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“…G. P. van der Wijst et al, 2018;Zhernakova et al, 2017), time point and stimulation (Cuomo et al, 2020;Strober et al, 2019;Ye et al, 2014) all induce a diversity of expression patterns and interactions with disease-associated genetic loci. Recent studies have combined single cell expression atlases with genetic signals (Jagadeesh et al, 2021;Skene et al, 2018;Smillie et al, 2019;Weeks et al, 2020) to associate risk genes with specific cell types and states in relevant tissues.…”

Section: Introductionmentioning

confidence: 99%

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

Eraslan

Drokhlyansky

Anand

et al. 2021

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Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

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Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Cited by 34 publications

References 194 publications

Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

Leveraging gene co-regulation to identify gene sets enriched for disease heritability

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

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