Figure 1. RefMap identifies ALS risk genes by integrating ALS GWAS data with the molecular profiling of motor neurons.

(A) Schematic of the study design. (1 and 2) We sequenced the transcriptome and epigenome of the iPSC-derived MNs. By integrating (3) ALS GWAS data with functional genomics of MNs, (4) a machine learning model called RefMap was developed to fine-map ALS-associated regions. (5) After linking those identified regions to their regulatory targets, 690 ALS-associated genes were pinpointed. (6) Transcriptome analysis based on iPSC-derived MNs, human tissues, and mouse models, as well as (7) network analysis were performed to demonstrate the functional significance of RefMap ALS genes. (8) CRISPR/Cas9 reproduction of identified ALS-associated mutations experimentally verified the proposed link to neuronal toxicity. The LD heatmap matrix in (4) is visualized in both R² (red) and D’ (blue) using LDmatrix (https://ldlink.nci.nih.gov/?tab=ldmatrix). cCRE, candidate cis-regulatory element; GO, gene ontology. (B) A region (chr12:112,036,001–112,038,000) around ATXN2 precisely pinpointed by RefMap because of elevated SNP Z-scores as well as enriched epigenetic peaks (ATAC-seq, H3K27ac and H3K4me3 histone ChIP-seq). The output of RefMap is labeled as Q-score. ATAC-seq and ChIP-seq signals are shown in fold change (FC) based on one replicate from sample CS14. See also Figure S1D and Supplemental Note.