Discovery and reproducibility of sparse components enriched for disease heritability.

(A) Heat map of known covariates and correlation with individual scores from each of the 500 components (left) and 56 sparse components (right) in FF data. (B) Heat map of condition specificity scores for the sparse components in FF data. Each row is a stimulation (naïve, LPS, or IFN) and each column is a sparse component. (C) Component replication between component 282 (CG) and component 337 (FF LPS24) for the most highly scored genes. The gene scores from the two components (in CG and FF) are highly correlated. (D) Proportion of heritability for 18 selected complex traits that can be attributed to each sparse component from the FF data. Shown here are enrichment statistics (with standard error) comparing the proportion of SNP heritability within the components divided by the proportion of total SNPs represented at FDR-corrected P < 0.05. (E) Gene Ontology (GO) enrichment for genes in component 61 and 22. AD: Alzheimer’s disease; PD: Parkinson’s disease; AUT: Autism; MS: Multiple Sclerosis; SCZ: Schizophrenia; T2D: Type 2 Diabetes; LUP: Lupus; PBC; Primary Biliary Cirrhosis; RA: Rheumatoid Arthritis; IBD: Inflammatory Bowel Disease; CRN: Crohn’s Disease; CEL: Celiac Disease; UC: Ulcerative Colitis; HDL: High-density lipoprotein Cholesterol; LDL: Low-density lipoprotein Cholesterol; BMI: Body Mass Index; CAD: Coronary Artery Disease; HGT: Height.</p

Overview of the study design and method.

(A) The gene expression datasets used in this study. Stimulated monocyte gene expression profiles from Fairfax et al. [12] in four conditions: response to lipopolysaccharide at 24 hours (LPS24) and at 2 hours (LPS2), interferon-γ (IFN-γ), and naive (left panel). Peripheral blood monocytes (MP) and macrophages (MC) from the Cardiogenics Consortium (right panel). (B) Overview of the SDA approach. An illustration of decomposition of gene expression datasets to yield component vectors for relative contribution of each individual, gene and condition. The individual scores matrices are then used as phenotypes with SNP genotypes in order to identify genetic variation correlated with the components (top). The stimulation or cell-activity scores matrix is used to identify the contribution of each condition for the components (middle). The gene scores matrix is used to identify the contribution of each gene within the components (bottom).</p

Macrophage-specific <i>trans</i>-eQTL colocalized in Parkinson’s disease associated CTSB locus.

(A) The genotype for Parkinson’s disease susceptibility allele rs1296028 is significantly associated with the individual scores of CG component 46. The rs1296028 affects the expression of 16 genes in component 46 in macrophage (CG) (left panel). Tissue specificity scores suggest CG component 46 is active in macrophage (Right). P-value: ***: trans-eGenes with PIP > 0.5 and 2.5% distributional cut-off (green dotted line) are shown. Trans-eQTL associations that replicated (FDR CTSB). The beta coefficients from Mendelian randomization analysis are shown for the significant trans-eGenes. (D) Experimental validation using THP-1 derived macrophages. CTSB was knocked-down using siRNA during 48 h and the levels of the top-scoring genes in the component were measured by qPCR. Data was normalized against scramble siRNA (SCR). P-value: *: <0.05 | ***: <0.001.</p

<i>Trans</i>-eQTL colocalized in Alzheimer’s disease associated CLU locus.

(A) The genotypes for Alzheimer’s disease susceptibility allele rs9331896 are significantly associated with the individual scores of component 22 (left panel). The component is active in monocytes (FF) in response to interferon-γ (right panel). (B). Circular plot demonstrating the chromosomal position of trans-eSNP (rs9331896) and the 38 trans target genes. The minor and AD-protective allele rs9331896-C is associated with decreased (blue lines) and increased (red lines) expression of trans-eGenes. The colored dots are trans-eQTL that replicates in ImmVar IFN stimulated monocytes (red and yellow color dots denote trans-eQTL association at FDR trans-eGenes with PIP > 0.5 and 2.5% distributional cut-off are shown. (C) IPA pathway analysis of the trans-eGenes in this component. The IPA canonical pathway enrichment P-values are shown in the lower right. The pathway network is grouped by IPA biological functions. The functional groups are defined by different colors and symbols (top right).</p

<i>Trans</i>-eQTL colocalized in Alzheimer’s disease associated <i>MS4A</i> locus.

(A) The genotypes for Alzheimer’s disease susceptibility allele rs983392 are significantly associated with the individual scores of component 26. rs983392 is trans-eQTL to FF component 26 with 54 genes (left panel). The component 26 is active only at baseline (right panel). (B) Circular plot demonstrating the chromosomal position of trans-eSNP (rs983392) and the 54 trans target genes. The minor and AD-protective allele rs983392-G is associated with decreased (blue lines) and increased (red lines) expression of trans-eGenes. The colored dots are trans-eQTL that replicates in ImmVar baseline monocytes (red and yellow color dots denote trans-eQTL association at FDR trans-eGenes with PIP > 0.5 and 2.5% distributional cut-off are shown. (C) Alzheimer’s disease SNP rs983392 mediates trans-effects to two trans-eGenes through cis-mediator MS4A4A. (D) Experimental validation using THP-1 derived macrophages. MS4A4A was knock-down using siRNA for 48 h, IFNg 20 ng/ml was added during the last 24 h. The levels of the top-scoring genes in the component were measured by qPCR. Data was normalized against scramble siRNA (SCR). P-value: *: <0.05 | **: <0.01 | ***:<0.001 vs SCR. ##: <0.01 vs siMS4A4A. (N = 5 independent experiments).</p

<i>Trans</i>-eQTLs colocalized in disease or trait-associated GWAS loci.

(A) Significant trans-eQTLs in FF data (FDR trans-eSNP on Y-axis and trans-eQTL components on X-axis. The red colored boxes reflect the effect size for the trans-eQTLs while the horizontal colored header reflects the condition activity scores. Trans-eQTLs that are in Alzheimer’s or Parkinson’s disease-associated loci (right panel). Alzheimer’s disease includes GWAS susceptibility loci from Alzheimer’s related traits including the age of onset, age-related cognitive decline, and APOE ε4 carriers. (B) Colocalization of trans-eQTLs at Parkinson’s disease susceptibility locus CTSB (left panel) and Alzheimer’s disease-associated loci MS4A4A (middle panel) and CLU (right panel). The x-axis in each panel shows the physical position on the chromosome (Mb). The y-axis shows the -log10(P) association p-values for Parkinson’s disease [23–24] (left panel) and Alzheimer’s disease GWAS [20–22] (middle and right panels). Listed on top are ‘coloc’ posterior probability for hypothesis 3 (PP.H3) and 4 (PP.H4). PP.H3: Association with eQTL and GWAS, two independent causal SNPs. PP.H4: Association with eQTL and GWAS, one shared SNP. (C) Transcription factors whose binding sites occurrence is enriched in the target set of genes within each sparse component compared to the expected occurrence estimated from a background set.</p

PolyGR and polyPR knock-in mice reveal a conserved neuroprotective extracellular matrix signature in C9orf72 ALS/FTD neurons.

Dipeptide repeat proteins are a major pathogenic feature of C9orf72 amyotrophic lateral sclerosis (C9ALS)/frontotemporal dementia (FTD) pathology, but their physiological impact has yet to be fully determined. Here we generated C9orf72 dipeptide repeat knock-in mouse models characterized by expression of 400 codon-optimized polyGR or polyPR repeats, and heterozygous C9orf72 reduction. (GR)400 and (PR)400 knock-in mice recapitulate key features of C9ALS/FTD, including cortical neuronal hyperexcitability, age-dependent spinal motor neuron loss and progressive motor dysfunction. Quantitative proteomics revealed an increase in extracellular matrix (ECM) proteins in (GR)400 and (PR)400 spinal cord, with the collagen COL6A1 the most increased protein. TGF-β1 was one of the top predicted regulators of this ECM signature and polyGR expression in human induced pluripotent stem cell neurons was sufficient to induce TGF-β1 followed by COL6A1. Knockdown of TGF-β1 or COL6A1 orthologues in polyGR model Drosophila exacerbated neurodegeneration, while expression of TGF-β1 or COL6A1 in induced pluripotent stem cell-derived motor neurons of patients with C9ALS/FTD protected against glutamate-induced cell death. Altogether, our findings reveal a neuroprotective and conserved ECM signature in C9ALS/FTD