Genome-wide association study highlights novel loci and hiding heritability for amyotrophic lateral sclerosis in 740,868 individuals 

Genome-wide association study highlights novel loci and hiding heritability for amyotrophic lateral sclerosis in 740,868 individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide association study highlights novel loci and hiding heritability for amyotrophic lateral sclerosis in 740,868 individuals Fengzhen Liu, Shan Gao, Ping Zhu, Shiyang Wu, Yijie He, Shuyuan Hu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8993465/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genome-wide association studies (GWAS) have identified several amyotrophic lateral sclerosis (ALS) risk loci, however only explained a small proportion of ALS variance. We consider that GWAS sample sizes and rare variants may explain the hiding heritability. Here, we collected six publicly available biobanks/cohorts, and conducted the largest multi-ancestry ALS GWAS meta-analysis in 740,868 participants (31,254 ALS and 709,614 controls) from European, East Asian, and African ancestries using genetic variants with the minor allele frequency of 0.01%. We identified 36 loci (22 new) explaining 26% of ALS variance. We integrated ALS GWAS with multi-omics data, and identified 321 risk genes. Using bulk tissue and single-nucleus RNA-seq, we demonstrated significantly differential expression of 218 genes including 21 GWAS loci. Drug-gene interaction analysis identified 4 genes as the potential therapeutic targets for ALS. Collectively, our findings highlight the hiding heritability of ALS and provide valuable insights into the potential underlying mechanisms of ALS. amyotrophic lateral sclerosis genome-wide association study expression quantitative trait loci hiding heritability RNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Amyotrophic lateral sclerosis (ALS), commonly referred to as motor neuron disease, is a progressive and fatal neurodegenerative condition affecting adults of all age groups, resulting in muscle weakness and atrophy due to damage in the motor neuron [ 1 ]. Genetic factors significantly contribute to the etiology of ALS [ 2 ]. Approximately 10% of ALS cases exhibit a clear familial history, referred to as familial ALS [ 2 ]. 90–95% of ALS cases are sporadic, known as sporadic ALS, which is a complex disease caused by a combination of genetic, environmental, and lifestyle factors [ 2 ]. The heritability of sporadic ALS from pedigree-based studies ( h 2 PED ) was estimated as 61% using twin data [ 3 ], and 50% using a prospective population-based study [ 4 ]. Until now, genetic association studies particularly genome-wide association studies (GWAS) have identified several ALS risk loci, including C9orf72 , UNC13A , SARM1 , C21orf2 , KIF5A , ACSL5 / ZDHHC6 , and GPX3/TNIP1 [ 5 – 9 ]. The largest GWAS meta-analysis including 29,612 ALS and 122,656 controls identified 15 genome-wide significance ( P < 5 × 10 − 8 ), and SOD1 was the only locus tagged by a rare genetic variant in individuals of European ancestry [ 2 ]. These ALS GWAS estimated the single nucleotide polymorphism (SNP)-based heritability on liability scale ( h 2 SNP ), which is the overall proportion of phenotypic variance explained by the additive genetic effects of common SNPs, ranging from 2.8% to 35% [ 2 , 5 , 10 ]. However the amount of phenotypic variance explained by the GWAS significant SNPs ( h 2 GWAS ) remains substantially lower than h 2 SNP , ranging from 0.1% to 0.3% [ 2 , 5 , 10 ]. The gap between h 2 GWAS and h 2 SNP (using common variants) was known as ‘hiding heritability’ [ 11 ]. Two major factors may explain the hiding heritability including GWAS sample sizes and rare variants [ 11 ]. The gap is expected to vanish with the increase in GWAS sample sizes, which may contribute to discover more ALS GWAS loci [ 11 ]. It is known that GWAS was designed to broadly capture the common genetic variants with a minor allele frequency (MAF) greater than 1% [ 11 – 13 ]. Rare variants were not tagged by common genetic variants from genotyping arrays and imputation [ 11 – 13 ]. In fact, evidence shows that rare variants around ALS GWAS loci such as C9orf72 and TBK1 had large effects in familial ALS [ 2 ]. However, the role of rare variants remains largely unknown in sporadic ALS. Until recently, publicly available biobanks using whole-genome sequence offered an unprecedented opportunity to assess the effects of both common and rare genetic variants on human traits and diseases, highlighted the missing heritability (the gap between h 2 PED and h 2 SNP using common variants), large effects and significant contribution of rare variants [ 11 , 14 – 17 ]. Here, we hypothesized that ALS GWAS using both common and rare variants may contribute to (1) increase the number of novel susceptibility loci, (2) identify the rare variants of large effects, and (3) increase the proportion of ALS variance explained by GWAS loci ( h 2 GWAS ). We collected six publicly available biobanks/cohorts, and conducted the largest multi-ancestry ALS GWAS meta-analysis in 740,868 participants (31,254 ALS and 709,614 controls) from three ancestral populations: European, East Asian, and African using genetic variants with the MAF > 0.01%. We further systematically characterized the genetic architecture of ALS by integrating the large-scale GWAS with multi-omics data. An overview of the workflow is provided in Fig. 1 . Figure 1 . Methods ALS GWAS datasets We obtained six ALS GWAS datasets in individuals of European, African and East Asian ancestries from previous ALS GWAS dataset (cohort 1, European, 27,205 ALS and 110,881 controls) [ 2 ], Million Veteran Program (MVP) (cohort 2, European, 748 ALS and 314,920 controls) [ 18 ], FinnGen R12 (cohort 3, European, 667 ALS and 216,760 controls) [ 19 ], MVP (cohort 4, European and African, 975 ALS and 370,198 controls) [ 18 ], China (cohort 5, East Asian, 1,234 ALS and 2,850 controls) [ 7 ], and Japan (cohort 6, East Asian, 1,173 ALS and 8,925 controls) [ 8 ] (Fig. 1 , STable 1). Quality control Before meta-analysis, rigorous quality control was applied to each GWAS dataset. All GWAS datasets were processed to obtain a harmonized format. We converted all GWAS datasets corresponding build 37 (hg19) coordinates using CrossMap [ 20 ]. We performed a genetic variant quality control to exclude (1) SNPs with non-standard alleles (other than A, T, C, G); (2) monomorphic SNPs; (3) SNPs absent from the reference and SNPs located on the X or Y chromosome [ 21 , 22 ], 10,439,566, 18,568,388, 17,804,658, 16,459,111, 6,077,307, and 4,026,065 SNPs are available for subsequent analysis in cohort 1, cohort 2, cohort 3, cohort 4, cohort 5, cohort 6 (Fig. 1 . b), respectively. (4) SNPs with a MAF below 0.01%. Consequently, only biallelic SNPs with > 0.01% were included [ 23 ]. ALS GWAS meta-analysis We conducted two-stage GWAS meta-analyses using an IVW fixed-effects method implemented in METAL (March 25, 2011 release), weighted by effect size and stander error (SE) [ 24 ]. In Stage 1, GWAS meta-analysis was performed in participants of European ancestry from three ALS GWAS summary statistics including 671,181 participants (28,620 ALS and 642,561 controls) (cohort 1, cohort 2 and cohort 3; Fig. 1 b). In stage 2, a GWAS meta-analysis was performed in participants of European, African and East Asian ancestries from five ALS GWAS summary statistics including 740,868 individuals (31,254 ALS and 709,614 controls) (cohort 1, cohort 3, cohort 4, cohort 5 and cohort 6; Fig. 1 b). Finally, 12,875,607 and 12,679,307 SNPs were available for Stage1 and Stage2. Genetic variants were considered to be significant if reaching the genome-wide significance threshold ( P < 5× 10 − 8 ). To assess the potential genomic inflation and residual confounding due to population stratification in meta-analysis, we calculated the λGC and LDSC intercept using LDSC (v1.0.1) and LD scores are from the 1000 Genomes Project phase 3 reference population [ 25 ]. Identification of genetic risk loci We identified genomic risk loci using Functional Mapping and Annotation (FUMA v1.5.2) from the GWAS meta-analysis by selecting the LD scores r 2 from 1000 Genomes Project phase 3 reference panel [ 26 ]. Independent significant SNPs are identified by first clumping all significant variants with P < 5× 10 − 8 and the LD threshold r 2 < 0.6 [ 26 ]. Lead SNPs are identified by second clumping all independent significant SNPs with the r 2 < 0.1 [ 26 ]. The genomic locus is defined by merging LD blocks of all independent significant SNPs within 250 kb of each other, with each locus was represented by the lead SNP with the most significant P value [ 26 ]. All genetic risk loci were compared to the previously known ALS risk loci [ 2 ], and are defined to be novel if they were not within 1Mb. Manhattan plots were generated using GWASLab (v3.4.40) and Locus plots were generated using LocusZoom (v1.4) [ 27 ]. Gene mapping To map the genome-wide significant ALS loci to specific genes, we selected the independent significant SNPs and SNPs in LD ( r 2 ≥ 0.6) with the independent significant SNPs, and mapped them to specific protein-coding genes using positional mapping (within 10 kb from the locus) and eQTLs mapping ( P < 1×10 − 3 ) using eQTLs datasets from GTEx v8 (13 brain tissues, skeletal muscle and whole blood) [ 28 ] and eQTLGen (whole blood) [ 29 ] (STable 2 and STable 3). Heritability analysis Using LDSC (v1.0.1), we estimated the h 2 SNP of ALS in each stage ALS GWAS meta-analysis: the proportion of variation that could be explained by the aggregated effect of common genetic variants mapped to HapMap3. ALS population prevalence estimate of 0.26% was used to convert h 2 SNP from the observation scale to the liability scale [ 4 ]. Meanwhile, we calculated h 2 GWAS , the proportion of ALS variance explained by the GWAS significant loci [ 30 ]: $$\begin{array}{c}{h}_{GWAS}^{2}\text{=}\sum_{\text{j}\text{=1}}^{\text{k}}\text{2*EAF*}\left(\text{1-EAF}\right)\text{*}{\text{β}}_{\text{j}}^{\text{2}}\end{array}$$ where k is the number of lead genetic variants representing genomic risk loci (as defined by FUMA), EAF is the effect allele frequency of SNP j , and β j is the effect size of SNP j . Gene-based association test, gene set and tissue enrichment analyses Multi-marker Analysis of Genomic Annotation (MAGMA v1.10) was used to perform the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of ALS GWAS meta-analysis summary data [ 31 ]. MAGMA mapped all SNPs from ALS GWAS meta-analysis to 17,903 protein-coding genes using the SNP-wise mean model, genomic location and boundary information from human genome build 37, and the ancestry-matched LD information from the 1000 Genomes Project phase 3 reference panel[ 31 ]. MAGMA calculated a gene-based association score based on the aggregate of all SNPs inside each gene [ 31 ]. MAGMA performed a gene set enrichment analysis through competitive analysis to identify the genes in a gene set that are more strongly associated with the phenotype of interest than other genes [ 31 ]. Here, we focused on 16,228 GO terms including biological processes, cellular components and molecular functions from the Molecular Signatures Database (MSigDB) (v7.0, version 2025.1.Hs) [ 32 ]. MAGMA determined whether ALS heritability is enriched in specific tissues by integrating the ALS GWAS meta-analysis summary data with gene expression data from 54 GTEx v8 tissues including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood [ 33 ]86. BH-adjusted P < 0.05 was considered statistically significant for gene-based association test gene set enrichment analysis and tissue enrichment analysis. Transcriptome-wide association study TWAS was performed using FUSION several predictive models (ENET, LASSO, SUSIE, and TOP1) to identify genes whose expression levels are associated with ALS by integrating the ALS GWAS meta-analysis summary data and eQTLs datasets in 15 relevant tissues including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood from GTEx v8. In each tissue, BH-adjusted P < 0.05 was considered as statistically significant. Colocalization analysis We conducted a colocalization analysis of the TWAS significant signals with BH-adjusted P < 0.05 using COLOC implemented by FUSION to investigate whether ALS and gene expression are likely influenced by the same underlying genetic variant within a specific region [ 34 ]. Basically, five configurations are calculated including H0: neither trait has a genetic association in the region, H1: only trait 1 has a genetic association in the region, H2: only trait 2 has a genetic association in the region, H3: both traits are associated, but with different causal variants, H4: both traits are associated and share a single causal variant. A posterior probability for H4 (PP4) ≥ 0.75 indicated strong evidence that two traits share a same causal variant [ 26 ]. Summary-based Mendelian randomization TWAS aims to identify genes whose expression levels are associated with a trait, and SMR tests whether that correlation is likely causal [ 35 , 36 ]. Here, we conducted a SMR using online SMR-Portal [ 37 ] to integrate the ALS GWAS meta-analysis summary data with eQTLs datasets in relevant tissues from GTEx v8 including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood [ 33 ], BrainMeta (brain) [ 38 ], and eQTLGen (whole blood) [ 29 ]. Meanwhile, we conducted a SMR (v1.3.1) analysis with default settings by integrating the ALS GWAS meta-analysis summary data with brain single-nucleus eQTLs datasets from eight brain cell types including excitatory neurons, oligodendrocytes, astrocytes, inhibitory neurons, oligodendrocyte precursor cells/committed oligodendrocyte precursors (OPCs/COPs), microglia, endothelial cells, pericytes derived from prefrontal cortex, temporal cortex and white matter of 192 participants [ 39 ], and seven brain cell types including astrocyte, endothelial cells, excitatory neurons, inhibitory neurons, microglia, oligodendrocytes, and oligodendrocyte progenitor cells derived from the dorsolateral prefrontal cortex of 424 older participants [ 40 ]. SMR statistically significant genes were defined using BH-adjusted P < 0.05 [ 35 ]. Differential gene expression analysis Firstly, we performed a differential gene expression analysis of ALS risk genes using five RNA-seq datasets from ALS and control bulk tissues including cervical spinal cords (8 ALS including 6 sporadic and 2 fALS, and 4 age-and sex-matched non-neurological controls, GEO accession: GSE287256) [ 41 ], cervical spinal cords (139 ALS and 35 non-neurological controls) [ 42 ], thoracic spinal cords (42 ALS and 10 non-neurological controls) [ 42 ], lumbar spinal cords (122 ALS and 21 non-neurological controls) [ 42 ] from the New York Genome Center ALS Consortium, and motor cortex (112 ALS and 59 controls) from the King’s College London BrainBank [ 43 ].Here, we performed differential gene expression analysis using Limma R package [ 41 ], or searched the ALS risk genes of interest in the corresponding supplementary materials provided by the original studies [ 42 , 43 ]. Secondly, we conducted a differential gene expression analysis of ALS risk genes using single-nucleus RNA-seq data from 8 ALS (6 sporadic and 2 familial ALS) and 4 age-and sex-matched non-neurological controls (GEO accession: GSE287257) [ 41 ]. SnRNA-seq data was analyzed using R package Seurat 5.3.0 with R version 4.4.3. In brief, we filter out the cells using three criteria: fewer than 200 detected genes, or more than 9,000 detected genes, or more than 20% mitochondrial genes. After the quality controls, all the raw reads were normalized using the LogNormalize method from the NormalizeData function. The most 3000 variable features were identified using the. FindVariableFeatures function with the “vst” method. ScaleData converts normalized gene expression to Z-score (values centered at 0 and with variance of 1). RunPCA was used to run principal component analysis and reduce dimensionality. Cell clustering is performed using FindNeighbors and FindClusters. Cell types were assigned by identifying genes unique to each cluster and by cross-referencing known markers of each cell type from existing published datasets [ 41 ] and CellMarker2.0 database [ 44 ]. RunUMAP was used to visualize the two conditions side-by-side. Differential expression analysis was carried out using FindMarkers. The differentially expressed genes were identified with the following parameters and thresholds: two-tailed unpaired Wilcoxon rank sum test P value 0.1, |log2(fold change)| > 0.1. Here, we defined the statistically significant differential expression using |log2(fold change)| > 0.10 and the BH-adjusted P < 0.05. Drug-gene interaction analysis The Drug Gene Interaction Database (DGIdb v5.0) [ 45 ] is an online database that integrates information from drug–gene interaction databases (accessed December 2024). DGIdb contains over 10,000 genes and 20,000 drugs involved in nearly 70,000 drug-gene interactions or belonging to one of 43 potentially druggable gene categories [ 45 ].A interaction score is used to rank results in an interaction search result set [ 45 ]. Genetic correlation across neurodegenerative diseases We evaluated the genetic association of ALS with other 7 neurodegenerative diseases including Dementia [ 19 ], Alzheimer’s disease [ 46 ], Lewy body dementia [ 47 ], Vascular dementia [ 19 ], Frontotemporal dementia [ 48 ], Parkinson’s disease [ 49 ], and Multiple sclerosis [ 50 ] using LDSC (v1.0.1) default parameters [ 25 ] and 1000 genomes phase 3 European reference panel (STable 19). All GWAS summary statistics were filtered according to HapMap3. We defined the statistically significant genetic association using the Bonferroni-adjusted P < 0.05/7. Results European-specific GWAS meta-analysis We observed the genomic inflation factor (λGC) = 1.0988 and linkage disequilibrium (LD) score regression (LDSC) intercept 1.0261 (s.e.= 0.0068), which showed little evidence of genetic inflation. We observed the SNP-based heritability on liability scale h 2 SNP =1.91% (s.e.= 0.0021) assuming a population prevalence of 0.0026 [ 4 ]. Using a fixed-effects inverse variance weighted (IVW) meta-analysis method [ 24 ], we revealed 28 independent genome-wide significant loci ( P < 5 × 10 − 8 ) by confirming all 12 previously known loci including MOBP , TNIP1 , ERGIC1 , HLA , C9orf72 , KIF5A , TBK1 , SCFD1 , UNC13A , SLC9A8 , SOD1 and C21orf2 from European-specific GWAS meta-analysis [ 2 ], and highlighting 16 novel loci including 4 loci DHX30 , NEK1 , BAG6 , and SARM1 tagged by common variants, and 12 loci tagged by rare variants (Fig. 2 a and Table 1 ). These 28 GWAS loci explained 26.98% of ALS variance including h 2 GWAS =11.86% from loci tagged by common variants and h 2 GWAS =15.12% from loci tagged by rare variants. Using positional mapping and eQTLs mapping, we identified 183 ALS risk genes using gene mapping (STable 2). Table 1 Genome-wide significant ALS loci from European-specific GWAS meta-analysis ( P < 5E-8, Stage1) rsID Chr Pos Allele1 Allele2 MAF Effect StdErr P NearestGene Old/Novel rs535959354 1 102297068 T C 0.0033 1.3039 0.2297 1.38e-08 OLFM3 Novel rs187245629 1 162105498 A G 0.0014 1.8913 0.3348 1.609e-08 NOS1AP Novel rs631312 3 39508968 A G 0.2923 -0.0747 0.0115 9.194e-11 MOBP Old rs34711187 3 47835889 T C 0.0892 -0.1087 0.0191 1.327e-08 DHX30 Novel rs536126574 3 148066370 T C 0.0008 2.9288 0.51 9.317e-09 CPB1 Novel rs73020386 3 159216860 A C 0.0012 2.2698 0.3863 4.196e-09 IQCJ-SCHIP1 Novel rs553661670 4 139775493 T G 0.0008 1.9382 0.3476 2.454e-08 NOCT Novel rs6831487 4 170331972 T G 0.352 -0.0625 0.0111 1.678e-08 NEK1 Novel rs181900403 5 119560991 T G 0.0012 2.0563 0.3263 2.948e-10 PRR16 Novel rs10463311 5 150410835 T C 0.2532 -0.0757 0.0121 4.042e-10 TNIP1 Old rs517339 5 172354731 T C 0.3959 0.0588 0.0108 4.935e-08 ERGIC1 Old rs183247446 5 179941250 T C 0.001 2.2995 0.4197 4.277e-08 CNOT6 Novel rs2077492 6 31606392 A G 0.4887 0.0589 0.0107 4.076e-08 BAG6 Novel rs9275477 6 32672641 A C 0.0979 0.1343 0.0196 7.498e-12 HLA-DQA2 Old rs527769781 9 7470838 A C 0.0005 2.4431 0.4186 5.313e-09 KDM4C Novel rs139185008 9 27491942 T C 0.0157 -1.6173 0.1109 3.46e-48 MOB3B Old rs145112002 9 116856846 T C 0.0004 -3.467 0.5619 6.837e-10 KIF12 Novel rs374866773 11 1777036 T C 0.0008 -2.4691 0.4163 3.017e-09 CTSD Novel rs113247976 12 57975700 T C 0.0172 0.3163 0.0456 3.863e-12 KIF5A Old rs61931525 12 64749141 T C 0.1172 0.0969 0.0166 5.707e-09 C12orf56 Old rs7154847 14 31059969 A G 0.3078 0.0903 0.0115 3.576e-15 G2E3 Old rs74008540 16 7091457 A G 0.0014 2.3933 0.421 1.314e-08 RBFOX1 Novel rs739439 17 26723822 T C 0.1802 -0.0796 0.0139 1.03e-08 SARM1 Novel rs79307092 18 57586914 T C 0.0046 1.2488 0.2207 1.519e-08 PMAIP1 Novel rs12973192 19 17753239 C G 0.3418 -0.1227 0.0118 3.638e-25 UNC13A Old rs17785991 20 48438761 A T 0.3475 0.0724 0.0114 2.076e-10 SLC9A8 Old rs80265967 21 33039603 A C 0.0072 -1.2291 0.0996 5.887e-35 SOD1 Old rs75087725 21 45753117 A C 0.0129 0.3553 0.0587 1.439e-09 C21orf2 Old Abbreviations: rsID, rsID of the SNP; Chr, Chromosome; Pos, SNP position in base pairs (GR37 Human Genome Build/hg19 coordinates); Allele1, effect allele; Allele2, non-effect allele; MAF, minor allele frequency; Effect, effect size; StdErr,standard error of Effect. Table 1 . Figure 2 . Cross-ancestry GWAS meta-analysis The λGC = 1.1019 and LDSC intercept of 1.027 (s.e.= 0.0069) showed little evidence of genetic inflation. We identified the SNP-based heritability on liability scale h 2 SNP =1.85% (s.e.=0.0021) with the population prevalence of 0.0026 [ 4 ]. Using the fixed-effects IVW meta-analysis method [ 24 ], we revealed 28 independent genome-wide significant loci by confirming 14 previously known loci including MOBP , NEK1 , TNIP1 , ERGIC1 , HLA , C9orf72 , KIF5A , TBK1 , COG3 , SCFD1 , UNC13A , SLC9A8 SOD1 and CFAP410 from cross-ancestry GWAS meta-analysis [ 2 ], and highlighting 14 novel loci including 3 loci DHX30 , BAG6 , SARM1 tagged by common variants, and 11 loci tagged by rare variants (Fig. 2 a and Table 2 ). These 28 GWAS loci explained 26.20% of ALS variance including h 2 GWAS =11.77% from loci tagged by common variants and h 2 GWAS =14.43% from loci tagged by rare variants. Using positional mapping and eQTLs mapping, we identified 167 ALS risk genes (STable 3). It is noted all subsequent analyses were performed using the Stage 2 GWAS summary statistics. Table 2 Genome-wide significant ALS loci from Cross-ancestry GWAS meta-analysis ( P < 5E-8, Stage2) rsID chr pos Allele1 Allele2 MAF Effect StdErr P NearestGene Old/Novel rs187245629 1 162105498 A G 0.0013 1.8652 0.3334 2.21e-08 NOS1AP Novel rs631312 3 39508968 A G 0.3554 -0.0759 0.011 4.846e-12 MOBP Old rs34711187 3 47835889 T C 0.0887 -0.1077 0.0191 1.69e-08 DHX30 Novel rs182172717 3 78033854 C G 0.0005 2.2306 0.4024 2.978e-08 ROBO2 Novel rs6831487 4 170331972 T G 0.345 -0.0629 0.0109 7.689e-09 NEK1 Old rs116038694 5 119581355 A C 0.0011 -2.0321 0.3259 4.499e-10 PRR16 Novel rs10463311 5 150410835 T C 0.2763 -0.0723 0.0114 2.215e-10 TNIP1 Old rs2431213 5 172356957 A G 0.4428 0.0581 0.0105 3.549e-08 ERGIC1 Old rs2077492 6 31606392 A G 0.4877 0.06 0.0107 1.852e-08 BAG6 Novel rs9275477 6 32672641 A C 0.0974 0.1335 0.0196 8.602e-12 HLA-DQA2 Old rs145947991 8 123704220 A G 0.0005 2.9109 0.498 5.058e-09 ZHX2 Novel rs139185008 9 27491942 T C 0.0154 -1.6142 0.1105 2.402e-48 MOB3B Old rs374866773 11 1777036 T C 0.0008 -2.4122 0.4127 5.06e-09 CTSD Novel rs570212709 11 24606549 A T 0.0008 3.1341 0.5233 2.107e-09 LUZP2 Novel rs577231108 11 59140723 T C 0.0012 1.7479 0.3127 2.273e-08 OR5AN1 Novel rs113247976 12 57975700 T C 0.0172 0.316 0.0455 3.911e-12 KIF5A Old rs61933200 12 64873122 A G 0.1097 -0.1002 0.0163 7.509e-10 TBK1 Old rs2985994 13 46113984 T C 0.2421 -0.0678 0.0116 5.187e-09 FAM194B Old rs229176 14 31027267 T C 0.3693 -0.0837 0.0111 3.728e-14 G2E3 Old rs148781797 14 34154275 A G 0.0012 -2.1062 0.3266 1.125e-10 NPAS3 Novel rs138395815 14 56240482 C G 0.001 2.9453 0.5223 1.71e-08 KTN1 Novel rs739439 17 26723822 T C 0.1732 -0.0749 0.0136 3.937e-08 SARM1 Novel rs79307092 18 57586914 T C 0.004 1.2091 0.2188 3.295e-08 PMAIP1 Novel rs12608932 19 17752689 A C 0.403 -0.1179 0.011 8.314e-27 UNC13A Old rs2869935 20 48493927 A G 0.3329 -0.0668 0.0109 9.496e-10 SLC9A8 Old rs150723682 21 32601335 A G 0.0094 1.0104 0.1721 4.319e-09 TIAM1 Novel rs80265967 21 33039603 A C 0.0073 -1.2263 0.1 1.49e-34 SOD1 Old rs75087725 21 45753117 A C 0.0128 0.3534 0.0587 1.706e-09 C21orf2 Old Abbreviations: rsID, rsID of the SNP; Chr, Chromosome; Pos, SNP position in base pairs (GR37 Human Genome Build/hg19 coordinates); Allele1, effect allele; Allele2, non-effect allele; MAF, minor allele frequency; Effect, effect size; StdErr,standard error of Effect;. Table 2 . Gene-based association test, gene set and tissue enrichment analyses Gene-based association test identified 114 statistically significant genes with the Benjamini-Hochberg (BH)-adjusted P < 0.05, including 13 of 28 GWAS significant loci tagged by common genetic variants including DHX30 , NEK1 , TNIP1 , ERGIC1 , BAG6 , HLA-DQA2 , MOB3B , KIF5A , TBK1 , G2E3 , UNC13A , SLC9A8 , and SOD1 (STable 4). Meanwhile, gene-based association test confirmed 33 of 167 genes from gene mapping. The top 10 significant genes include IFNK , MOB3B , C9orf72 , G2E3 , SCFD1 , TBK1 , TNIP1 , BAG6 , TSPAN31 , and SOD1 around the MOB3B , G2E3 , TBK1 , TNIP1 , BAG6 , KIF5A , and SOD1 loci. Gene set enrichment analysis identified 11 statistically significant gene ontology (GO) biological processes with the BH-adjusted P < 0.05 especially the regulation of synaptic vesicle exocytosis (GO:2000300, P = 8.304× 10 − 7 ), synaptic vesicle exocytosis (GO:0016079, P = 2.339 × 10 − 6 ), regulation of neurotransmitter transport (GO:0051588, P = 4.371× 10 − 6 ), regulation of neurotransmitter secretion (GO:0046928, P = 1.760 × 10 − 5 ), neurotransmitter transport (GO:0006836, P = 1.990× 10 − 5 ), and neurotransmitter secretion (GO:0007269, P = 7.906 × 10 − 5 ) (STable 5). Tissue enrichment analysis revealed the highest enrichment in ten GTEx v8 brain tissues including cerebellum, cerebellar hemisphere, cortex, frontal cortex, nucleus accumbens, anterior cingulate cortex, putamen, hippocampus, caudate, and hypothalamus with P < 0.05, In contrast, skeletal muscle did not show significant enrichment ( P = 0.71942). Importantly, cerebellum and cerebellar hemisphere passed the threshold of statistical significance with BH-adjusted P < 0.05 (STable 6). Transcriptome-wide association study We identified 118 TWAS significant genes including 88 genes in brain tissues, 21 genes in skeletal muscle and 31 genes in whole blood with BH-adjusted P < 0.05 (STable 7 and STable 8). TWAS confirmed 5 ALS loci including BAG6 (skeletal muscle, Fig. 3 b), G2E3 (skeletal muscle, Fig. 3 b), MOBP (cortex), SLC9A8 (cerebellum in Fig. 3 c, cerebellar hemisphere in Fig. 3 d, hypothalamus and spinal cord), TNIP1 (whole blood, Fig. 3 e), 20 of 167 genes from gene mapping, and 46 of 114 genes from gene-based association test. Specifically, C9orf72 around the MOB3B locus is identified in 13 tissues including 11 brain tissues, skeletal muscle and whole blood (Fig. 3 a). SCFD1 around the G2E3 locus is identified in 8 tissues including 6 brain tissues, skeletal muscle and whole blood (Fig. 3 a). Importantly, TWAS highlighted some novel genes outside the ALS GWAS loci, including DHRS11 (10 tissues, chr 17), MYO19 (10 tissues, chr 17), RESP18 (9 tissues, chr 2), GGNBP2 (8 tissues, chr 17), AP3B2 (8 tissues, chr 5), RANBP10 (7 tissues, chr 16), and TPP1 (skeletal muscle and cerebellum, chr 11) (Fig. 3 a). Figure 3 . Summary-based Mendelian randomization Using bulk tissue eQTLs datasets, we identified 24 statistically significant genes with BH-adjusted P < 0.05 (Fig. 4 a, STable 7 and STable 9). We confirmed 6 ALS GWAS loci including G2E3 (BrainMeta, Fig. 4 b), MOBP (BrainMeta, Fig. 4 b), SARM1 (BrainMeta, Fig. 4 b), SLC9A8 (BrainMeta cerebellum and cerebellar hemisphere, Fig. 4 a), TNIP1 (whole blood in Fig. 4 c and eQTLGen in Fig. 4 d), TBK1 (whole blood in Fig. 4 c and eQTLGen in Fig. 4 d), 10 of 167 genes from gene mapping, 16 of 114 genes from gene-based association test, and 16 of 118 TWAS significant genes. Specifically, C9orf72 around the MOB3B locus is identified in 11 GTEx v8 tissues (9 brain tissues, skeletal muscle, whole blood), BrainMeta and eQTLGen. SCFD1 around the G2E3 locus is identified in 5 GTEx v8 tissues (3 brain tissues, skeletal muscle and whole blood), BrainMeta and eQTLGen (Fig. 4 a). Importantly, SMR confirmed the TWAS significant genes outside the ALS GWAS loci, including DHRS11 , MYO19 , RESP18 , and GGNBP2 . It is noted that RESP18 is identified in 9 GTEx v8 brain tissues and BrainMeta (Fig. 4 a). SMR using single-nucleus RNA sequencing eQTLs datasets highlighted 14 statistically significant genes with BH-adjusted P < 0.05 especially in excitatory neuron including C9orf72 (excitatory neuron, inhibitory neuron, astrocyte and microglia), SCFD1 (excitatory neuron, inhibitory neuron, and microglia), SLC9A8 (excitatory neuron, inhibitory neuron, and oligodendrocyte), MOB3B (excitatory neuron), MYO19 (excitatory neuron), PRRC2A (excitatory neuron), MOBP (oligodendrocyte), EIF2AK3 (excitatory neuron), TAF11 (excitatory neuron), DHRS11 (excitatory neuron), DNAJB1 (excitatory neuron), PDZD7 (excitatory neuron), CAMLG (excitatory neuron), MINK1 (excitatory neuron) and LY6G5C (excitatory neuron and inhibitory neuron) (STable 7, STable 10 and STable 11). Interestingly, these findings from snRNA-seq eQTLs confirmed 6 genes from gene mapping ( MOBP , PRRC2A , MOB3B , C9orf72 , SCFD1 , SLC9A8 ), 7 genes from gene-based association test ( MOB3B , SCFD1 , C9orf72 , SLC9A8 , PRRC2A , DHRS11 , MYO19 ), 9 TWAS significant genes ( C9orf72 , CAMLG , DHRS11 , MOBP , MYO19 , PDZD7 , SCFD1 , SLC9A8 , TAF11 ), and 7 SMR significant genes using tissue eQTLs ( C9orf72 , DHRS11 , EIF2AK3 , MOBP , MYO19 , SCFD1 , SLC9A8 ). Figure 4 . Differential gene expression analysis We identified 321 risk genes using GWAS, gene mapping, gene-based association test, TWAS and SMR (STable 12). We performed a differential expression analysis of these 321 risk genes using RNA-seq from ALS and control cervical spinal cords [ 41 ], cervical spinal cords [ 42 ], thoracic spinal cords [ 42 ], lumbar spinal cords [ 42 ], and motor cortex [ 43 ]. 14, 109, 2, 70, and 8 genes showed statistically significant differential expression with |log2(fold change)|>0.10 and BH-adjusted P < 0.05, respectively (STable 13, STable 14, STable 15 and Fig. 5 ). We found evidence of differential expression of ALS GWAS loci including 9 loci in cervical spinal cords including DHX30 (novel locus), NEK1 , ERGIC1 , TNIP1 , HLA-DQA2 , ZHX2 (novel locus), CTSD (novel locus), KIF5A , UNC13A (STable 13 and Fig. 5 a) [ 41 ]; 11 loci in cervical spinal cords including NOS1AP (novel locus), MOBP , DHX30 (novel locus), NEK1 , ERGIC1 , ZHX2 (novel locus), MOB3B , CTSD (novel locus), KIF5A , SARM1 (novel locus) and UNC13A (STable 14 and Fig. 5 b) [ 42 ]; 6 loci in lumbar spinal cords including NOS1AP (novel locus), MOBP , ERGIC1 , MOB3B , CTSD (novel locus) and SARM1 (novel locus) (STable 14 and Fig. 5 c) [ 42 ]; 4 loci in motor cortex including LUZP2 (novel locus), G2E3 , SARM1 (novel locus), SOD1 (STable 15 and Fig. 5 d) [ 43 ]. Meanwhile, genes from gene mapping, gene-based association test, TWAS and SMR also showed evidence of differential expression including LY6G5C (cervical, and lumbar), PRRC2A (cervical and motor cortex), DHRS11 (cervical and motor cortex), RESP18 (spinal cords), GGNBP2 (cervical and lumbar), AP3B2 (cervical and cervical), RANBP10 (cervical, lumbar and motor cortex), TPP1 (cervical), C9orf72 (cervical) and SCFD1 (cervical). Figure 5 . We further performed a differential expression analysis of these 321 risk genes using single-nucleus RNA-seq from ALS and control cervical spinal cords [ 41 ]. We found evidence of differential expression of 12 previously known ALS GWAS loci including MOBP , TNIP1 , ERGIC1 , CNOT6 , HLA-DQA2 , MOB3B , KIF5A , G2E3 , UNC13A , SLC9A8 , SOD1 and TBK1 and 14 novel loci including NOS1AP (astrocyte, oligodendrocyte, OPC, neuron and microglia, Fig. 6 b), IQCJ-SCHIP1 (astrocyte and oligodendrocyte), NOCT (oligodendrocyte), NEK1 (astrocyte, oligodendrocyte, OPC and neuron, Fig. 6 b), PRR16 , (astrocyte, OPC and neuron), KDM4C (astrocyte, oligodendrocyte and neuron), CTSD (astrocyte, oligodendrocyte, OPC and neuron), RBFOX1 (oligodendrocyte, neuron, OPC and fibroblast), SARM1 (oligodendrocyte and OPC), ROBO2 (astrocytes, OPC and neuron), ZHX2 (astrocyte, neuron and OPC, Fig. 6 b), LUZP2 (astrocyte, microglia, oligodendrocyte and OPC), NPAS3 (OPC, neuron, microglia and fibroblast, Fig. 6 b), KTN1 (astrocyte and oligodendrocyte), TIAM1 (astrocyte, oligodendrocyte, neuron, microglia and fibroblast). Meanwhile, some genes from gene mapping, gene-based association test, TWAS and SMR also showed evidence of differential expression including C9orf72 (astrocyte and microglia), SCFD1 (OPC, neuron and microglia, Fig. 6 b), GGNBP2 (OPC), AP3B2 (OPC), RANBP10 (neuron), TPP1 (OPC, oligodendrocyte and neuron), PRRC2A (oligodendrocyte), EIF2AK3 (astrocyte, microglia and pericyte/SMC), TAF11 (astrocyte), DNAJB1 (OPC and neuron), MINK1 (astrocyte, OPC and microglia) (STable 16). Figure 6 . Drug-gene interaction analysis ALS risk genes showed evidence of interactions with Food and Drug Administration (FDA)-approved drugs (STable 17). C9orf72 showed evidence of interaction with ustekinumab (interaction score = 5.25) that was approved to treat inflammatory bowel disease [ 51 ]. SLC9A8 showed evidence of interactions with infliximab (interaction score = 1.684) and adalimumab (interaction score = 1.582), which were approved to treat autoimmune diseases [ 52 ]. SOD1 indicated a strong interaction with tofersen (interaction score = 26.10) that was approved to treat ALS patients with a SOD1 mutation [ 53 ]. TPP1 from gene-based association test and TWAS indicated the strongest interaction (interaction score = 156.6) with cerliponase alfa, which was approved to treat neuronal ceroid lipofuscinosis type 2 and slow motor and language function decline [ 54 , 55 ]. BAG6 (interaction score = 2.13) and PRRC2A (interaction score = 0.71) showed evidence of interactions with carbamazepine, which was approved for the treatment of epilepsy, neuropathic pain and bipolar disorder [ 56 ]. Genetic correlation across neurodegenerative diseases We evaluated the genetic association of ALS with other 7 neurodegenerative diseases including dementia, Alzheimer’s disease, Lewy body dementia, vascular dementia, frontotemporal dementia, Parkinson’s disease, and multiple sclerosis using LDSC [ 57 ] (STable 18). We found that ALS showed statistically significant positive genetic correlation with Parkinson’s disease ( r g =0.1843, P = 0.0012), Alzheimer’s disease ( r g =0.2952, P = 0.0068) using a Bonferroni-corrected threshold P < 7.14× 10 − 3 (0.05/7), and suggestive positive genetic correlation with dementia ( r g =0.2378, P = 0.01669), and lewy body dementia ( r g =0.4221, P = 0.038) (STable 19). Discussion Here, we performed the largest cross-ancestry ALS GWAS meta-analysis to date in 740,868 participants including 31,254 ALS and 709,614 controls from three ancestral populations including European, East Asian, and African using both common and rare genetic variants. Stage 1 European meta-analysis identified 28 independent genome-wide significant loci with h 2 GWAS =26.98% by confirming 12 previously known loci [ 2 ], and highlighting 16 novel loci. Stage 2 cross-ancestry GWAS meta-analysis revealed 28 independent genome-wide significant loci with h 2 GWAS =26.20% by confirming 14 previously known loci [ 2 ], and highlighting 14 novel loci. Collectively, we identified 36 unique independent genome-wide significant loci, and highlighted the hiding heritability of ALS, especially the contribution from rare variants. Of these novel loci, DHX30 encodes the mitochondrial nucleoid protein and plays an important role in the development of the brain [ 58 ]. DHX30 missense variants caused neurodevelopmental disorder with severe motor impairment and absent language [ 59 ]. DHX30 was a key molecule underlying mitochondrial dysfunction in ALS [ 60 ]. NEK1 is a risk gene for ALS, and its mutations caused 2–3% of all ALS cases by disrupting microtubule homeostasis and nuclear import [ 61 ]. In ALS, the RNA-binding protein TDP-43 is depleted from the nucleus of neurons in the brain and spinal cord [ 62 , 63 ]. ALS is a synaptopathy accompanied by the presence of cytoplasmic aggregates containing TDP-43, linked to about 97% of ALS cases [ 64 ]. BAG6 prevents the aggregation of TDP-43 fragments [ 65 ]. SARM1 was identified as a genome-wide significant locus for ALS in two previous GWAS [ 5 , 66 ]. However, this locus was not successfully replicated in subsequent GWAS [ 2 ]. Mendelian randomization revealed the causal association between SARM1 protein level and the risk of ALS [ 67 ]. NOS1AP is a novel molecular target and critical factor in TDP-43 pathology [ 68 ]. KDM4C was a susceptibility gene for schizophrenia and autism spectrum disorder [ 69 ]. KDM4C overexpression significantly upregulates ApoE expression, ultimately promoting proliferation in mouse hippocampal neural stem cells [ 70 ]. Until now, the role of KIF12 remains unclear. KIF5A and KIF12 are both kinesin motor proteins that move cargo along microtubules. KIF5A was a genome-wide significant ALS locus in our current study and previous studies [ 2 , 6 ]. KIF5A variant impaired motor unit recovery and maintenance in a knock-in mouse model [ 71 ], caused locomotor deficits, alterations of neuromuscular junctions, and motor neuron loss in Drosophila model [ 72 ], and abolished autoinhibition resulting in a toxic gain of function [ 73 ]. Both cathepsin D ( CTSD ) and cathepsin B ( CTSB ) are family of cathepsin proteins. CTSB was involved in the motor neuron degeneration in ALS [ 74 ]. Loss of CTSD activity caused lysosomal dysfunction and promoteed cell-to-cell transmission of α-synuclein aggregates [ 75 ]. Lack of CTSD in the central nervous system caused microglia and astrocyte activation and the accumulation of proteinopathy-related proteins [ 76 ]. Lnc-HIBADH-4 regulates autophagy-lysosome pathway in ALS by targeting CTSD [ 77 ]. Mendelian randomization showed that genetically determined CTSD circulating protein level was associated with a higher risk of ALS (OR = 1.06, 95% CI: 1.00-1.13, P = 0.049) [ 78 ]. RBFOX1 was associated with brain amyloidosis and involved in the pathogenesis of Alzheimer’s disease [ 79 ]. PMAIP1 expression was significantly downregulated in stage 2–3 ALS motor cortex compared to stage 4 ALS patients [ 80 ]. A recent sex-stratified GWAS identified LUZP2 as a male-specific genome-wide significant locus [ 81 ]. TIAM1 was identified as a genome-wide significant locus for ALS [ 82 ]. However, this locus was not successfully replicated in subsequent GWAS [ 2 ]. Loss-of-function variants in TIAM1 are associated with developmental delay, intellectual disability, and seizures [ 83 ]. Our current gene-based association test identified 114 statistically significant genes including genes within and outside the ALS GWAS loci especially DHRS11 , ZNHIT3 and GGNBP2 on 17q12. All these three genes were previously identified as ALS risk genes by multi omics integrative analysis [ 42 , 67 , 84 – 87 ]. Here, we identified DHRS11 , ZNHIT3 and GGNBP2 as the 20 th, 21th and 23th significant genes, respectively. It is noted that previous gene set enrichment analysis of ALS GWAS using MAGMA did not identify any statistically significant pathways [ 7 ]. Our current gene set enrichment analysis identified 11 statistically significant pathways, all of which are GO biological processes. We highlighted the involvement of synaptic vesicle exocytosis including regulation of synaptic vesicle exocytosis (GO:2000300), synaptic vesicle exocytosis (GO:0016079) and neurotransmitter including regulation of neurotransmitter transport (GO:0051588), regulation of neurotransmitter secretion (GO:0046928), neurotransmitter secretion (GO:0007269) and neurotransmitter transport (GO:0006836) (STable 5). The presynaptic terminal is populated by synaptic vesicles containing neurotransmitters [ 88 ]. Synaptic vesicle exocytosis mediates neurotransmitter release from presynaptic terminals and modulate postsynaptic function [ 88 ]. The presynaptic synaptopathy resulting from physiological dysfunction of synapses is an early and convergent event in FTD and ALS [ 88 ]. 3-T proton magnetic resonance spectroscopy showed an imbalance in excitatory and inhibitory neurotransmitters in ALS and healthy controls [ 89 ]. Our multi omics integrative analysis including TWAS and SMR identified genes within the ALS GWAS loci and highlighting novel genes outside the ALS GWAS loci. Specifically, we identified RESP18 and TPP1 as ALS risk genes using gene-based association test, TWAS and SMR. Interestingly, a recent sex-stratified GWAS identified RESP18 as a male-specific genome-wide significant locus [ 81 ]. RESP18 deficiency protected dopaminergic neurons in a Parkinson’s disease mouse model [ 90 ]. Mendelian randomization supported the causal association between TPP1 protein level and ALS [ 91 ]. A high-content screen identified TPP1 as a regulator of axonal mitochondrial transport [ 92 ]. TPP1 inhibition or knockdown enhanced axonal mitochondria transport in rat hippocampal neurons and iPSC-derived human cortical neurons, iPSC-derived motor neurons from ALS patient with one copy of SOD1 A4V mutation [ 92 ]. Our tissue enrichment analysis showed the highest enrichment of ALS heritability in 10 GTEx v8 brain tissues, but not skeletal muscle. SMR using brain single-nucleus eQTLs datasets further identified the brain cell types where ALS risk genes may function, especially the excitatory neuron. Using spinal cord RNA-seq and snRNA-seq from ALS patients and controls, we further demonstrated the differential expression of ALS risk genes, especially the GWAS loci. Drug-gene interaction analysis highlighted some FDA-approved drugs that may be associated with ALS. C9orf72 showed evidence of interaction with ustekinumab (interaction score = 5.25), which is typically used to treat moderate to severe plaque psoriasis, psoriatic arthritis, moderate to severe Crohn disease, or moderate to severe ulcerative colitis [ 51 ]. SLC9A8 showed evidence of interactions with infliximab (interaction score = 1.684) and adalimumab (interaction score = 1.582), which were approved to treat autoimmune diseases [ 52 ]. A disproportionality analysis of the FDA Adverse Event Reporting System showed that ustekinumab, infliximab and adalimumab were associated with an increased risk of ALS [ 52 ]. SOD1 indicated a strong interaction with tofersen (interaction score = 26.10), which is used to treat ALS patients with a mutation in the SOD1 gene [ 53 ]. TPP1 from gene-based association test and TWAS indicated the strongest interaction (interaction score = 156.6) with cerliponase alfa, which was approved to treat neuronal ceroid lipofuscinosis type 2 and slow motor and language function decline [ 54 , 55 ]. BAG6 (interaction score = 2.13) and PRRC2A (interaction score = 0.71) showed evidence of interactions with carbamazepine, which had been approved for the treatment of epilepsy, neuropathic pain and bipolar disorder [ 56 ]. Carbamazepine has shown potential as a therapeutic agent for ALS in animal models by delaying disease onset, extending lifespan, protecting motor neurons and muscles by activating autophagy to reduce mutant SOD1 aggregation [ 93 ]. Meanwhile, carbamazepine rescued the motor dysfunction of FTD mice with TDP-43 pathology [ 94 ]. Therefore, cerliponase alfa and carbamazepine may be potential therapeutic agents for ALS, and clinical trials are encouraged to further validate their therapeutic benefits for patients with ALS. In summary, our current findings demonstrated that the use of both common and rare genetic variants in large-scale ALS GWAS could (1) enhance the ability to identify new loci, (2) identify rare variants of large effects, and (3) increase the proportion of ALS variance explained by known GWAS loci ( h 2 GWAS ). These genetic findings provide valuable insights into the potential underlying mechanisms of ALS, and broaden the potential therapeutic scope of the available drugs, which may contribute to future drug development targeting ALS. Declarations Data availability GWAS summary statistics (Cohort1):https://www.ebi.ac.uk/gwas/(accession IDs GCST90027164). GWAS summary statistics (Cohort2 and Cohort4): These GWAS summary statistics were created as part of the Million Veteran Program (MVP) genomewide PheWAS project, please see https://phenomics.va.ornl.gov/web/cipher/pheweb. GWAS summary statistics (Cohort3): https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_R12_G6_ALS.gz. GWAS summary statistics (Cohort5): http://cnsgenomics.com/data/benyamin_et_al_2017_nc/BenyaminEtAl_NatComm_Data.zip. GWAS summary statistics (Cohort6): The summary statistics are available at the Human Genetic Variation Database (Accession ID: HGV0000013). https://www.hgvd.genome.med.kyoto-u.ac.jp/repository/HGV0000013.html. Code availability The following software were used for data analyses: METAL (released on 2011-03-25): https://csg.sph.umich.edu/abecasis/Metal/download/, CrossMap (v0.6.5):https://github.com/liguowang/CrossMap, FUMA (v1.5.2): https://fuma.ctglab.nl/, GWASLab (v3.4.40): https://cloufield.github.io/gwaslab/ , LocusZoom (v1.4): https://github.com/statgen/locuszoom-standalone, MAGMA (v1.10): https://cncr.nl/research/magma/, FUSION: https://github.com/gusevlab/fusion_twas, COLOC (version 5): https://github.com/chr1swallace/coloc, SMR (v1.3.1) https://yanglab.westlake.edu.cn/software/smr/#Overview, SMR(online)https://yanglab.westlake.edu.cn/smr-portal/, LDSC (v1.0.1): https://github.com/bulik/ldsc, DGIdb (v5.0): https://beta.dgidb.org/. Ethical approval and consent to participate. The datasets covered in this paper provided informed consent in all corresponding original surveys. Our analyses are based on publicly available large-scale datasets rather than individual-level data. Therefore, we did not seek ethical approval. Competing interests The authors declare that they have no competing interests. Funding This work was supported by funding from the National Key R&D Program of China (Grant No. 2023YFC3605200, 2023YFC3605202), National Natural Science Foundation of China (Grant No. 82471449 and 82071212), Beijing Natural Science Foundation (Grant No. JQ21022). Authors’ contributions The authors declare no competing interests. G.Y.L. conceived and initiated the project. G.Y.L. and F.Z.L analyzed the data and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. References Chia R, Chio A, Traynor BJ. Novel genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications. 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Proc Natl Acad Sci U S A. 2012;109:15024–9. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8993465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609081624,"identity":"6656ee53-e538-4e39-a9b5-11a2cd48aeaa","order_by":0,"name":"Fengzhen Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengzhen","middleName":"","lastName":"Liu","suffix":""},{"id":609081625,"identity":"a6ca51c3-1daf-4769-83e7-91b73a463992","order_by":1,"name":"Shan Gao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Gao","suffix":""},{"id":609081626,"identity":"2190cb22-38d9-4f90-a451-02a09bf8876c","order_by":2,"name":"Ping Zhu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhu","suffix":""},{"id":609081627,"identity":"3e40291e-e255-4bf4-bd44-44a42c58518b","order_by":3,"name":"Shiyang Wu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shiyang","middleName":"","lastName":"Wu","suffix":""},{"id":609081628,"identity":"b96ff7d6-91bb-4f30-a778-2e37749fed1e","order_by":4,"name":"Yijie He","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijie","middleName":"","lastName":"He","suffix":""},{"id":609081629,"identity":"eba33afe-c30b-4c6b-a675-c55b6d66feec","order_by":5,"name":"Shuyuan Hu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuyuan","middleName":"","lastName":"Hu","suffix":""},{"id":609081630,"identity":"5cdc96eb-d33a-456a-8795-900ea4ad3c01","order_by":6,"name":"Kun Wang","email":"","orcid":"","institution":"Taishan Vocational College of Nursing","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":609081631,"identity":"829fd27b-a925-44b3-b8c9-f2680f1ec701","order_by":7,"name":"Xunming Ji","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xunming","middleName":"","lastName":"Ji","suffix":""},{"id":609081632,"identity":"2c0eb28d-364d-46a6-b393-d2316291ec68","order_by":8,"name":"Guiyou Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACPmYQaQDEzMwHHyRUSMjJE9LCBtfCzpZs8OCMhbFhAyEtcBY/j5nkw7aKRIYDhLSw8xi/5ik4LGdwmMfYIHGeRAJjA/PDRzfwOozHzJrH4LCxwWG2wgeJ2yTy2BnYjI1zCGgxBmpJ3HCYebMBUEsxYwMPmzSRWhjMJBLnSCQ2HCCsxfgxRAsLUEsDUVrYyhjnGKQbSx4GBnLCMQljw2YCfuHnP7z5w5s/1nJ85w8ffPijpk5Onr354WN8WkAWSTAwNCPxmfErByv5wMBQR1jZKBgFo2AUjFwAAHHOQ/ibpUT2AAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guiyou","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-02-28 08:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8993465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8993465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105314614,"identity":"9ad08ae8-fd28-4618-928f-31d9a91ac2bc","added_by":"auto","created_at":"2026-03-24 15:59:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":923928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a two-stage GWAS meta-analysis of ALS in 740,868 participants. In Stage 1, meta-analysis was performed using three independent ALS GWAS datasets in 671,181 participants of European ancestry including 28,620 ALS and 642,561 controls. In Stage 2, meta-analysis was performed using five independent GWAS datasets including 740,868 individuals (31,254 ALS and 709,614 controls). We further systematically characterized the genetic architecture of ALS by integrating the large-scale GWAS with multi-omics data. (a) ALS GWAS summary datasets. (b) Identification of ALS loci (Stage 1 European ancestry, Stage 2 Multi-ancestry). (c) Genomic analysis of ALS GWAS. (d) Multi-omics analysis of ALS GWAS. (e) Identification of ALS genes using gene prioritization. (f) Differential gene expression analysis. (g) Drug-gene interaction analysis. (h) Genetic correlation analysis. \u003cem\u003eN\u003c/em\u003e, the total number of individuals. eQTLs, expression quantitative trait loci; TWAS, Transcriptome-wide association study; SMR, Summary-based Mendelian Randomization.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/f60adf4f96796dd4de660b25.jpg"},{"id":105564665,"identity":"8f05a53d-c18d-4659-a5a9-a2cabf25e065","added_by":"auto","created_at":"2026-03-27 12:50:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":890310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan and locus plot of meta-analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Manhattan Plot of ALS GWAS loci. Each point represents a genetic variant. The x-axis denotes the variant’s genomic position and the y-axis denotes the meta-analysis \u003cem\u003ep\u003c/em\u003e-value in the Stage1 (up) and Stage2 (down). The red dashed line denotes the genome-wide significance threshold 5×10-8. The closest gene in which the lead significant variant is located is annotated. The plot was created using the GWASLab python package. (b) Locus plot of rs34711187. (c) Locus plot of rs6831487. (d) Locus plot of rs2077492. (e) Locus plot of rs739439. The purple highlight, the most significantly associated variant, different colors represent linkage disequilibrium relationships to the index variant. Overlaid line graph, recombination rate within the locus. The x-axis denotes the name and location of genes and the y-axis denotes the -log10(\u003cem\u003ep\u003c/em\u003e-value) converted by meta-analysis \u003cem\u003ep\u003c/em\u003e-value. Genes in the region are indicated below the association plot. The plot was created using LocusZoom.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/660a5a4ec3d974f88cf4a3c0.jpg"},{"id":105314612,"identity":"0fd7ea74-8150-49f4-8412-843e81d81ef0","added_by":"auto","created_at":"2026-03-24 15:59:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":954754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBubble plot and single tissue Manhattan plot of TWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Bubble plot of 15 tissues in TWAS. Each point represents a gene. The x-axis denotes the tissue type and the y-axis denotes the genes which were significant at least in three tissues with Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. (b) Manhattan-style Z-score plot of GTEx v8 muscle skeletal. (c) Manhattan-style Z-score plot of GTEx v8 cerebellum. (d) Manhattan-style Z-score plot of GTEx v8 cerebellar hemisphere. (e) Manhattan-style Z-score plot of GTEx v8 whole blood. The x-axis denotes the gene start position and chromosome, and the y-axis denotes the gene Z-score of TWAS. Each point represents a gene. The red dashed line denotes the Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value 0.05. For genes on the top part of the graph, increased expression was associated with increased risk of ALS, while expression of the genes on the bottom part of the plot showed an inverse association.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/067f2e14f1b0f89c385649b0.jpg"},{"id":105314610,"identity":"0d63e0a3-3a33-4194-b793-26970277461a","added_by":"auto","created_at":"2026-03-24 15:59:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":673787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBubble plot and single eQTLs Manhattan plot of SMR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Bubble plot of 15 eQTLsin SMR. Each point represents a gene. The x-axis denotes the eQTLs type and the y-axis denotes the genes which were significant at least in two eQTLs with Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. (b) Manhattan-style plot of SMR using BrainMeta eQTLs. (c) Manhattan-style plot of SMR using GTEx v8 whole blood eQTLs. (d) Manhattan-style plot of SMR using eQTLGen whole blood eQTLs. (e) Manhattan-style plot of SMR using GTEx v8 muscle skeletal eQTLs. The x-axis denotes the gene start position and chromosome and the y-axis denotes the gene -log10 (Benjamini-Hochberg p-value) converted by SMR \u003cem\u003ep\u003c/em\u003e-value. Each point represents a gene. The red dashed line denotes the Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value 0.05. For genes on the top part of the graph, b_SMR\u0026gt;0, while b_SMR\u0026lt;0.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/36c62da2121fbda55ba999c6.jpg"},{"id":105314608,"identity":"48f174c2-e70c-4218-b10b-090cbe58f696","added_by":"auto","created_at":"2026-03-24 15:59:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":705937,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Prioritization gene volcano plot of cervical spinal cord (8 ALS and 4 controls). (b) Prioritization gene volcano plot of cervical spinal cord (139 ALS and 35 controls). (c) Prioritization gene volcano plot of lumbar spinal cord (122 ALS and controls). (d) Prioritization gene volcano plot of motor cortex (112 ALS and 59 controls). The x-axis represents log (fold change), the y-axis represents -log\u003csub\u003e10 \u003c/sub\u003e(Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value) converted by differential gene expression analysis \u003cem\u003ep\u003c/em\u003e-value. The |Log2 (fold change)| \u0026gt;0.1 and Benjamini-Hochberg \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. (e) Bubble plot of cervical spinal cord (8 ALS and 4 controls). (f) Bubble plot of cervical spinal cord (139 ALS and 35 controls). (g) Bubble plot of lumbar spinal cord (122 ALS and controls). (h) Bubble plot of motor cortex (112 ALS and 59 controls). The x-axis denotes log (fold change), the y-axis denotes genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVolcano plot and bubble plot of ALS\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/e122e9bb8bf62ebd704a295f.jpg"},{"id":105314611,"identity":"5b4d88bb-e5e6-4591-9d7a-24cabc7401b1","added_by":"auto","created_at":"2026-03-24 15:59:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":622552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUMAP plot and box plot of ALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) UMAP plot depicting identified cell types from snRNA-seq analysis of ALS and control spinal cords. (b) Box plot genes of different cell types from snRNA-seq analysis of ALS and control spinal cords. The x-axis denotes the sample groups (ALS vs control), the y-axis denotes gene expression levels in each cell type.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/ab460653f7070a23c585d79b.jpg"},{"id":106994076,"identity":"57025e0b-d4ec-48e3-8e7a-cb6d0d3d98e7","added_by":"auto","created_at":"2026-04-15 15:03:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6531001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/a8b564c9-6cd8-49aa-838a-8fbbce880509.pdf"},{"id":105314613,"identity":"14aea6f7-27bc-4e75-b659-5dd16cd4dddc","added_by":"auto","created_at":"2026-03-24 15:59:52","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13889858,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8993465/v1/a2b9b5a27fa4693a017b4c80.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association study highlights novel loci and hiding heritability for amyotrophic lateral sclerosis in 740,868 individuals ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS), commonly referred to as motor neuron disease, is a progressive and fatal neurodegenerative condition affecting adults of all age groups, resulting in muscle weakness and atrophy due to damage in the motor neuron [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Genetic factors significantly contribute to the etiology of ALS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 10% of ALS cases exhibit a clear familial history, referred to as familial ALS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 90\u0026ndash;95% of ALS cases are sporadic, known as sporadic ALS, which is a complex disease caused by a combination of genetic, environmental, and lifestyle factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The heritability of sporadic ALS from pedigree-based studies (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003ePED\u003c/sub\u003e) was estimated as 61% using twin data [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and 50% using a prospective population-based study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUntil now, genetic association studies particularly genome-wide association studies (GWAS) have identified several ALS risk loci, including \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e, \u003cem\u003eSARM1\u003c/em\u003e, \u003cem\u003eC21orf2\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eACSL5\u003c/em\u003e/\u003cem\u003eZDHHC6\u003c/em\u003e, and \u003cem\u003eGPX3/TNIP1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The largest GWAS meta-analysis including 29,612 ALS and 122,656 controls identified 15 genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), and \u003cem\u003eSOD1\u003c/em\u003e was the only locus tagged by a rare genetic variant in individuals of European ancestry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These ALS GWAS estimated the single nucleotide polymorphism (SNP)-based heritability on liability scale (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e), which is the overall proportion of phenotypic variance explained by the additive genetic effects of common SNPs, ranging from 2.8% to 35% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However the amount of phenotypic variance explained by the GWAS significant SNPs (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e) remains substantially lower than \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e, ranging from 0.1% to 0.3% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe gap between \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e and \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e (using common variants) was known as \u0026lsquo;hiding heritability\u0026rsquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Two major factors may explain the hiding heritability including GWAS sample sizes and rare variants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The gap is expected to vanish with the increase in GWAS sample sizes, which may contribute to discover more ALS GWAS loci [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is known that GWAS was designed to broadly capture the common genetic variants with a minor allele frequency (MAF) greater than 1% [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Rare variants were not tagged by common genetic variants from genotyping arrays and imputation [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In fact, evidence shows that rare variants around ALS GWAS loci such as \u003cem\u003eC9orf72\u003c/em\u003e and \u003cem\u003eTBK1\u003c/em\u003e had large effects in familial ALS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the role of rare variants remains largely unknown in sporadic ALS.\u003c/p\u003e \u003cp\u003eUntil recently, publicly available biobanks using whole-genome sequence offered an unprecedented opportunity to assess the effects of both common and rare genetic variants on human traits and diseases, highlighted the missing heritability (the gap between \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003ePED\u003c/sub\u003e and \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e using common variants), large effects and significant contribution of rare variants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Here, we hypothesized that ALS GWAS using both common and rare variants may contribute to (1) increase the number of novel susceptibility loci, (2) identify the rare variants of large effects, and (3) increase the proportion of ALS variance explained by GWAS loci (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e). We collected six publicly available biobanks/cohorts, and conducted the largest multi-ancestry ALS GWAS meta-analysis in 740,868 participants (31,254 ALS and 709,614 controls) from three ancestral populations: European, East Asian, and African using genetic variants with the MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01%. We further systematically characterized the genetic architecture of ALS by integrating the large-scale GWAS with multi-omics data. An overview of the workflow is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eALS GWAS datasets\u003c/h2\u003e \u003cp\u003eWe obtained six ALS GWAS datasets in individuals of European, African and East Asian ancestries from previous ALS GWAS dataset (cohort 1, European, 27,205 ALS and 110,881 controls) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], Million Veteran Program (MVP) (cohort 2, European, 748 ALS and 314,920 controls) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], FinnGen R12 (cohort 3, European, 667 ALS and 216,760 controls) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], MVP (cohort 4, European and African, 975 ALS and 370,198 controls) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], China (cohort 5, East Asian, 1,234 ALS and 2,850 controls) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and Japan (cohort 6, East Asian, 1,173 ALS and 8,925 controls) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, STable 1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality control\u003c/h3\u003e\n\u003cp\u003eBefore meta-analysis, rigorous quality control was applied to each GWAS dataset. All GWAS datasets were processed to obtain a harmonized format. We converted all GWAS datasets corresponding build 37 (hg19) coordinates using CrossMap [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We performed a genetic variant quality control to exclude (1) SNPs with non-standard alleles (other than A, T, C, G); (2) monomorphic SNPs; (3) SNPs absent from the reference and SNPs located on the X or Y chromosome [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], 10,439,566, 18,568,388, 17,804,658, 16,459,111, 6,077,307, and 4,026,065 SNPs are available for subsequent analysis in cohort 1, cohort 2, cohort 3, cohort 4, cohort 5, cohort 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. b), respectively. (4) SNPs with a MAF below 0.01%. Consequently, only biallelic SNPs with \u0026gt;\u0026thinsp;0.01% were included [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eALS GWAS meta-analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted two-stage GWAS meta-analyses using an IVW fixed-effects method implemented in METAL (March 25, 2011 release), weighted by effect size and stander error (SE) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In Stage 1, GWAS meta-analysis was performed in participants of European ancestry from three ALS GWAS summary statistics including 671,181 participants (28,620 ALS and 642,561 controls) (cohort 1, cohort 2 and cohort 3; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In stage 2, a GWAS meta-analysis was performed in participants of European, African and East Asian ancestries from five ALS GWAS summary statistics including 740,868 individuals (31,254 ALS and 709,614 controls) (cohort 1, cohort 3, cohort 4, cohort 5 and cohort 6; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Finally, 12,875,607 and 12,679,307 SNPs were available for Stage1 and Stage2. Genetic variants were considered to be significant if reaching the genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). To assess the potential genomic inflation and residual confounding due to population stratification in meta-analysis, we calculated the λGC and LDSC intercept using LDSC (v1.0.1) and LD scores are from the 1000 Genomes Project phase 3 reference population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eIdentification of genetic risk loci\u003c/h3\u003e\n\u003cp\u003eWe identified genomic risk loci using Functional Mapping and Annotation (FUMA v1.5.2) from the GWAS meta-analysis by selecting the LD scores \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e from 1000 Genomes Project phase 3 reference panel [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Independent significant SNPs are identified by first clumping all significant variants with \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and the LD threshold \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Lead SNPs are identified by second clumping all independent significant SNPs with the \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The genomic locus is defined by merging LD blocks of all independent significant SNPs within 250 kb of each other, with each locus was represented by the lead SNP with the most significant \u003cem\u003eP\u003c/em\u003e value [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. All genetic risk loci were compared to the previously known ALS risk loci [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and are defined to be novel if they were not within 1Mb. Manhattan plots were generated using GWASLab (v3.4.40) and Locus plots were generated using LocusZoom (v1.4) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGene mapping\u003c/h3\u003e\n\u003cp\u003eTo map the genome-wide significant ALS loci to specific genes, we selected the independent significant SNPs and SNPs in LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.6) with the independent significant SNPs, and mapped them to specific protein-coding genes using positional mapping (within 10 kb from the locus) and eQTLs mapping (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) using eQTLs datasets from GTEx v8 (13 brain tissues, skeletal muscle and whole blood) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and eQTLGen (whole blood) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (STable 2 and STable 3).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHeritability analysis\u003c/h2\u003e \u003cp\u003eUsing LDSC (v1.0.1), we estimated the \u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e of ALS in each stage ALS GWAS meta-analysis: the proportion of variation that could be explained by the aggregated effect of common genetic variants mapped to HapMap3. ALS population prevalence estimate of 0.26% was used to convert \u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e from the observation scale to the liability scale [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Meanwhile, we calculated \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e, the proportion of ALS variance explained by the GWAS significant loci [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{h}_{GWAS}^{2}\\text{=}\\sum_{\\text{j}\\text{=1}}^{\\text{k}}\\text{2*EAF*}\\left(\\text{1-EAF}\\right)\\text{*}{\\text{\u0026beta;}}_{\\text{j}}^{\\text{2}}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e is the number of lead genetic variants representing genomic risk loci (as defined by FUMA), EAF is the effect allele frequency of \u003cem\u003eSNP\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the effect size of \u003cem\u003eSNP\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene-based association test, gene set and tissue enrichment analyses\u003c/h3\u003e\n\u003cp\u003eMulti-marker Analysis of Genomic Annotation (MAGMA v1.10) was used to perform the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of ALS GWAS meta-analysis summary data [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. MAGMA mapped all SNPs from ALS GWAS meta-analysis to 17,903 protein-coding genes using the SNP-wise mean model, genomic location and boundary information from human genome build 37, and the ancestry-matched LD information from the 1000 Genomes Project phase 3 reference panel[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. MAGMA calculated a gene-based association score based on the aggregate of all SNPs inside each gene [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMAGMA performed a gene set enrichment analysis through competitive analysis to identify the genes in a gene set that are more strongly associated with the phenotype of interest than other genes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Here, we focused on 16,228 GO terms including biological processes, cellular components and molecular functions from the Molecular Signatures Database (MSigDB) (v7.0, version 2025.1.Hs) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMAGMA determined whether ALS heritability is enriched in specific tissues by integrating the ALS GWAS meta-analysis summary data with gene expression data from 54 GTEx v8 tissues including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]86. BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for gene-based association test gene set enrichment analysis and tissue enrichment analysis.\u003c/p\u003e\n\u003ch3\u003eTranscriptome-wide association study\u003c/h3\u003e\n\u003cp\u003eTWAS was performed using FUSION several predictive models (ENET, LASSO, SUSIE, and TOP1) to identify genes whose expression levels are associated with ALS by integrating the ALS GWAS meta-analysis summary data and eQTLs datasets in 15 relevant tissues including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood from GTEx v8. In each tissue, BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eWe conducted a colocalization analysis of the TWAS significant signals with BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using COLOC implemented by FUSION to investigate whether ALS and gene expression are likely influenced by the same underlying genetic variant within a specific region [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Basically, five configurations are calculated including H0: neither trait has a genetic association in the region, H1: only trait 1 has a genetic association in the region, H2: only trait 2 has a genetic association in the region, H3: both traits are associated, but with different causal variants, H4: both traits are associated and share a single causal variant. A posterior probability for H4 (PP4)\u0026thinsp;\u0026ge;\u0026thinsp;0.75 indicated strong evidence that two traits share a same causal variant [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSummary-based Mendelian randomization\u003c/h2\u003e \u003cp\u003eTWAS aims to identify genes whose expression levels are associated with a trait, and SMR tests whether that correlation is likely causal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Here, we conducted a SMR using online SMR-Portal [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] to integrate the ALS GWAS meta-analysis summary data with eQTLs datasets in relevant tissues from GTEx v8 including 13 brain tissues (amygdala, anterior cingulate cortex (BA24), caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex (BA9), hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord (cervical c-1) and substantia nigra), skeletal muscle and whole blood [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], BrainMeta (brain) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and eQTLGen (whole blood) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Meanwhile, we conducted a SMR (v1.3.1) analysis with default settings by integrating the ALS GWAS meta-analysis summary data with brain single-nucleus eQTLs datasets from eight brain cell types including excitatory neurons, oligodendrocytes, astrocytes, inhibitory neurons, oligodendrocyte precursor cells/committed oligodendrocyte precursors (OPCs/COPs), microglia, endothelial cells, pericytes derived from prefrontal cortex, temporal cortex and white matter of 192 participants [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and seven brain cell types including astrocyte, endothelial cells, excitatory neurons, inhibitory neurons, microglia, oligodendrocytes, and oligodendrocyte progenitor cells derived from the dorsolateral prefrontal cortex of 424 older participants [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. SMR statistically significant genes were defined using BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eFirstly, we performed a differential gene expression analysis of ALS risk genes using five RNA-seq datasets from ALS and control bulk tissues including cervical spinal cords (8 ALS including 6 sporadic and 2 fALS, and 4 age-and sex-matched non-neurological controls, GEO accession: GSE287256) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], cervical spinal cords (139 ALS and 35 non-neurological controls) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], thoracic spinal cords (42 ALS and 10 non-neurological controls) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], lumbar spinal cords (122 ALS and 21 non-neurological controls) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] from the New York Genome Center ALS Consortium, and motor cortex (112 ALS and 59 controls) from the King\u0026rsquo;s College London BrainBank [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].Here, we performed differential gene expression analysis using Limma R package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], or searched the ALS risk genes of interest in the corresponding supplementary materials provided by the original studies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecondly, we conducted a differential gene expression analysis of ALS risk genes using single-nucleus RNA-seq data from 8 ALS (6 sporadic and 2 familial ALS) and 4 age-and sex-matched non-neurological controls (GEO accession: GSE287257) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. SnRNA-seq data was analyzed using R package Seurat 5.3.0 with R version 4.4.3. In brief, we filter out the cells using three criteria: fewer than 200 detected genes, or more than 9,000 detected genes, or more than 20% mitochondrial genes. After the quality controls, all the raw reads were normalized using the LogNormalize method from the NormalizeData function. The most 3000 variable features were identified using the. FindVariableFeatures function with the \u0026ldquo;vst\u0026rdquo; method. ScaleData converts normalized gene expression to Z-score (values centered at 0 and with variance of 1). RunPCA was used to run principal component analysis and reduce dimensionality. Cell clustering is performed using FindNeighbors and FindClusters. Cell types were assigned by identifying genes unique to each cluster and by cross-referencing known markers of each cell type from existing published datasets [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and CellMarker2.0 database [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. RunUMAP was used to visualize the two conditions side-by-side. Differential expression analysis was carried out using FindMarkers. The differentially expressed genes were identified with the following parameters and thresholds: two-tailed unpaired Wilcoxon rank sum test \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, min.pct\u0026thinsp;\u0026gt;\u0026thinsp;0.1, |log2(fold change)| \u0026gt; 0.1. Here, we defined the statistically significant differential expression using |log2(fold change)| \u0026gt; 0.10 and the BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDrug-gene interaction analysis\u003c/h2\u003e \u003cp\u003eThe Drug Gene Interaction Database (DGIdb v5.0) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] is an online database that integrates information from drug\u0026ndash;gene interaction databases (accessed December 2024). DGIdb contains over 10,000 genes and 20,000 drugs involved in nearly 70,000 drug-gene interactions or belonging to one of 43 potentially druggable gene categories [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].A interaction score is used to rank results in an interaction search result set [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlation across neurodegenerative diseases\u003c/h2\u003e \u003cp\u003eWe evaluated the genetic association of ALS with other 7 neurodegenerative diseases including Dementia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], Lewy body dementia [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], Vascular dementia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Frontotemporal dementia [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and Multiple sclerosis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] using LDSC (v1.0.1) default parameters [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and 1000 genomes phase 3 European reference panel (STable 19). All GWAS summary statistics were filtered according to HapMap3. We defined the statistically significant genetic association using the Bonferroni-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/7.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEuropean-specific GWAS meta-analysis\u003c/h2\u003e \u003cp\u003eWe observed the genomic inflation factor (λGC)\u0026thinsp;=\u0026thinsp;1.0988 and linkage disequilibrium (LD) score regression (LDSC) intercept 1.0261 (s.e.= 0.0068), which showed little evidence of genetic inflation. We observed the SNP-based heritability on liability scale \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e=1.91% (s.e.= 0.0021) assuming a population prevalence of 0.0026 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Using a fixed-effects inverse variance weighted (IVW) meta-analysis method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], we revealed 28 independent genome-wide significant loci (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) by confirming all 12 previously known loci including \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eHLA\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e, \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eC21orf2\u003c/em\u003e from European-specific GWAS meta-analysis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and highlighting 16 novel loci including 4 loci \u003cem\u003eDHX30\u003c/em\u003e, \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eBAG6\u003c/em\u003e, and \u003cem\u003eSARM1\u003c/em\u003e tagged by common variants, and 12 loci tagged by rare variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These 28 GWAS loci explained 26.98% of ALS variance including \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=11.86% from loci tagged by common variants and \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=15.12% from loci tagged by rare variants. Using positional mapping and eQTLs mapping, we identified 183 ALS risk genes using gene mapping (STable 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome-wide significant ALS loci from European-specific GWAS meta-analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5E-8, Stage1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ersID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllele1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllele2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStdErr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNearestGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld/Novel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers535959354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102297068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.3039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eOLFM3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers187245629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162105498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.609e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNOS1AP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers631312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39508968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.194e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eMOBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers34711187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47835889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.327e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eDHX30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers536126574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148066370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.9288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.317e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCPB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers73020386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159216860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.196e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eIQCJ-SCHIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers553661670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139775493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.9382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.454e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNOCT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6831487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170331972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.678e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNEK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers181900403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119560991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.948e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ePRR16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10463311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150410835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.042e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eTNIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers517339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172354731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.935e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eERGIC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers183247446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179941250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.277e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCNOT6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2077492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31606392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.076e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eBAG6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9275477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32672641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.498e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eHLA-DQA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers527769781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7470838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.4431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.313e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eKDM4C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers139185008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27491942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.6173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.46e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eMOB3B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers145112002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116856846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.837e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eKIF12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers374866773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1777036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.4691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.017e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCTSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers113247976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57975700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.863e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eKIF5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers61931525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64749141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.707e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eC12orf56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7154847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31059969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.576e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eG2E3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers74008540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7091457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.314e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eRBFOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers739439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26723822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSARM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers79307092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57586914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.519e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ePMAIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12973192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17753239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.638e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eUNC13A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17785991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48438761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.076e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSLC9A8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers80265967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33039603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.2291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.887e-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers75087725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45753117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.439e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eC21orf2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: rsID, rsID of the SNP; Chr, Chromosome; Pos, SNP position in base pairs (GR37 Human Genome Build/hg19 coordinates); Allele1, effect allele; Allele2, non-effect allele; MAF, minor allele frequency; Effect, effect size; StdErr,standard error of Effect.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCross-ancestry GWAS meta-analysis\u003c/h2\u003e \u003cp\u003eThe λGC\u0026thinsp;=\u0026thinsp;1.1019 and LDSC intercept of 1.027 (s.e.= 0.0069) showed little evidence of genetic inflation. We identified the SNP-based heritability on liability scale \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eSNP\u003c/sub\u003e=1.85% (s.e.=0.0021) with the population prevalence of 0.0026 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Using the fixed-effects IVW meta-analysis method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], we revealed 28 independent genome-wide significant loci by confirming 14 previously known loci including \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eHLA\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eCOG3\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e, \u003cem\u003eSLC9A8 SOD1\u003c/em\u003e and \u003cem\u003eCFAP410\u003c/em\u003e from cross-ancestry GWAS meta-analysis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and highlighting 14 novel loci including 3 loci \u003cem\u003eDHX30\u003c/em\u003e, \u003cem\u003eBAG6\u003c/em\u003e, \u003cem\u003eSARM1\u003c/em\u003e tagged by common variants, and 11 loci tagged by rare variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These 28 GWAS loci explained 26.20% of ALS variance including \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=11.77% from loci tagged by common variants and \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=14.43% from loci tagged by rare variants. Using positional mapping and eQTLs mapping, we identified 167 ALS risk genes (STable 3). It is noted all subsequent analyses were performed using the Stage 2 GWAS summary statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome-wide significant ALS loci from Cross-ancestry GWAS meta-analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5E-8, Stage2)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ersID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllele1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllele2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStdErr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNearestGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld/Novel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers187245629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162105498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.21e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNOS1AP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers631312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39508968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.846e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eMOBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers34711187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47835889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.69e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eDHX30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers182172717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78033854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.2306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.978e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eROBO2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6831487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170331972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.689e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNEK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers116038694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119581355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.0321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.499e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ePRR16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10463311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150410835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.215e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eTNIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2431213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172356957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.549e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eERGIC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2077492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31606392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.852e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eBAG6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9275477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32672641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.602e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eHLA-DQA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers145947991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123704220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.9109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.058e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eZHX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers139185008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27491942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.6142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.402e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eMOB3B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers374866773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1777036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.4122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.06e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCTSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers570212709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24606549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.1341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.107e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eLUZP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers577231108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59140723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.7479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.273e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eOR5AN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers113247976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57975700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.911e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eKIF5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers61933200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64873122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.509e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eTBK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2985994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46113984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.187e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eFAM194B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers229176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31027267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.728e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eG2E3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers148781797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34154275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.1062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.125e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNPAS3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers138395815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56240482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.9453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eKTN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers739439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26723822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.937e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSARM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers79307092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57586914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.295e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ePMAIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12608932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17752689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.314e-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eUNC13A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2869935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48493927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.496e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSLC9A8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers150723682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32601335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.319e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eTIAM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNovel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers80265967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33039603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.2263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.49e-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers75087725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45753117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.706e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eC21orf2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: rsID, rsID of the SNP; Chr, Chromosome; Pos, SNP position in base pairs (GR37 Human Genome Build/hg19 coordinates); Allele1, effect allele; Allele2, non-effect allele; MAF, minor allele frequency; Effect, effect size; StdErr,standard error of Effect;.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGene-based association test, gene set and tissue enrichment analyses\u003c/h2\u003e \u003cp\u003eGene-based association test identified 114 statistically significant genes with the Benjamini-Hochberg (BH)-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, including 13 of 28 GWAS significant loci tagged by common genetic variants including \u003cem\u003eDHX30\u003c/em\u003e, \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eBAG6\u003c/em\u003e, \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eG2E3\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e, and \u003cem\u003eSOD1\u003c/em\u003e (STable 4). Meanwhile, gene-based association test confirmed 33 of 167 genes from gene mapping. The top 10 significant genes include \u003cem\u003eIFNK\u003c/em\u003e, \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eG2E3\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eBAG6\u003c/em\u003e, \u003cem\u003eTSPAN31\u003c/em\u003e, and \u003cem\u003eSOD1\u003c/em\u003e around the \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eG2E3\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eBAG6\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, and \u003cem\u003eSOD1\u003c/em\u003e loci.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis identified 11 statistically significant gene ontology (GO) biological processes with the BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 especially the regulation of synaptic vesicle exocytosis (GO:2000300, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.304\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), synaptic vesicle exocytosis (GO:0016079, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.339 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), regulation of neurotransmitter transport (GO:0051588, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.371\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), regulation of neurotransmitter secretion (GO:0046928, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.760 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), neurotransmitter transport (GO:0006836, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.990\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and neurotransmitter secretion (GO:0007269, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.906 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (STable 5).\u003c/p\u003e \u003cp\u003eTissue enrichment analysis revealed the highest enrichment in ten GTEx v8 brain tissues including cerebellum, cerebellar hemisphere, cortex, frontal cortex, nucleus accumbens, anterior cingulate cortex, putamen, hippocampus, caudate, and hypothalamus with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, In contrast, skeletal muscle did not show significant enrichment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71942). Importantly, cerebellum and cerebellar hemisphere passed the threshold of statistical significance with BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (STable 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome-wide association study\u003c/h2\u003e \u003cp\u003eWe identified 118 TWAS significant genes including 88 genes in brain tissues, 21 genes in skeletal muscle and 31 genes in whole blood with BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (STable 7 and STable 8). TWAS confirmed 5 ALS loci including \u003cem\u003eBAG6\u003c/em\u003e (skeletal muscle, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), \u003cem\u003eG2E3\u003c/em\u003e (skeletal muscle, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), \u003cem\u003eMOBP\u003c/em\u003e (cortex), \u003cem\u003eSLC9A8\u003c/em\u003e (cerebellum in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, cerebellar hemisphere in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, hypothalamus and spinal cord), \u003cem\u003eTNIP1\u003c/em\u003e (whole blood, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee), 20 of 167 genes from gene mapping, and 46 of 114 genes from gene-based association test. Specifically, \u003cem\u003eC9orf72\u003c/em\u003e around the \u003cem\u003eMOB3B\u003c/em\u003e locus is identified in 13 tissues including 11 brain tissues, skeletal muscle and whole blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). \u003cem\u003eSCFD1\u003c/em\u003e around the \u003cem\u003eG2E3\u003c/em\u003e locus is identified in 8 tissues including 6 brain tissues, skeletal muscle and whole blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Importantly, TWAS highlighted some novel genes outside the ALS GWAS loci, including \u003cem\u003eDHRS11\u003c/em\u003e (10 tissues, chr 17), \u003cem\u003eMYO19\u003c/em\u003e (10 tissues, chr 17), \u003cem\u003eRESP18\u003c/em\u003e (9 tissues, chr 2), \u003cem\u003eGGNBP2\u003c/em\u003e (8 tissues, chr 17), \u003cem\u003eAP3B2\u003c/em\u003e (8 tissues, chr 5), \u003cem\u003eRANBP10\u003c/em\u003e (7 tissues, chr 16), and \u003cem\u003eTPP1\u003c/em\u003e (skeletal muscle and cerebellum, chr 11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSummary-based Mendelian randomization\u003c/h2\u003e \u003cp\u003eUsing bulk tissue eQTLs datasets, we identified 24 statistically significant genes with BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, STable 7 and STable 9). We confirmed 6 ALS GWAS loci including \u003cem\u003eG2E3\u003c/em\u003e (BrainMeta, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), \u003cem\u003eMOBP\u003c/em\u003e (BrainMeta, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), \u003cem\u003eSARM1\u003c/em\u003e (BrainMeta, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), \u003cem\u003eSLC9A8\u003c/em\u003e (BrainMeta cerebellum and cerebellar hemisphere, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), \u003cem\u003eTNIP1\u003c/em\u003e (whole blood in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and eQTLGen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), \u003cem\u003eTBK1\u003c/em\u003e (whole blood in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and eQTLGen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), 10 of 167 genes from gene mapping, 16 of 114 genes from gene-based association test, and 16 of 118 TWAS significant genes. Specifically, \u003cem\u003eC9orf72\u003c/em\u003e around the \u003cem\u003eMOB3B\u003c/em\u003e locus is identified in 11 GTEx v8 tissues (9 brain tissues, skeletal muscle, whole blood), BrainMeta and eQTLGen. \u003cem\u003eSCFD1\u003c/em\u003e around the \u003cem\u003eG2E3\u003c/em\u003e locus is identified in 5 GTEx v8 tissues (3 brain tissues, skeletal muscle and whole blood), BrainMeta and eQTLGen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Importantly, SMR confirmed the TWAS significant genes outside the ALS GWAS loci, including \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eMYO19\u003c/em\u003e, \u003cem\u003eRESP18\u003c/em\u003e, and \u003cem\u003eGGNBP2\u003c/em\u003e. It is noted that \u003cem\u003eRESP18\u003c/em\u003e is identified in 9 GTEx v8 brain tissues and BrainMeta (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSMR using single-nucleus RNA sequencing eQTLs datasets highlighted 14 statistically significant genes with BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 especially in excitatory neuron including \u003cem\u003eC9orf72\u003c/em\u003e (excitatory neuron, inhibitory neuron, astrocyte and microglia), \u003cem\u003eSCFD1\u003c/em\u003e (excitatory neuron, inhibitory neuron, and microglia), \u003cem\u003eSLC9A8\u003c/em\u003e (excitatory neuron, inhibitory neuron, and oligodendrocyte), \u003cem\u003eMOB3B\u003c/em\u003e (excitatory neuron), \u003cem\u003eMYO19\u003c/em\u003e (excitatory neuron), \u003cem\u003ePRRC2A\u003c/em\u003e (excitatory neuron), \u003cem\u003eMOBP\u003c/em\u003e (oligodendrocyte), \u003cem\u003eEIF2AK3\u003c/em\u003e (excitatory neuron), \u003cem\u003eTAF11\u003c/em\u003e (excitatory neuron), \u003cem\u003eDHRS11\u003c/em\u003e (excitatory neuron), \u003cem\u003eDNAJB1\u003c/em\u003e (excitatory neuron), \u003cem\u003ePDZD7\u003c/em\u003e (excitatory neuron), \u003cem\u003eCAMLG\u003c/em\u003e (excitatory neuron), \u003cem\u003eMINK1\u003c/em\u003e (excitatory neuron) and \u003cem\u003eLY6G5C\u003c/em\u003e (excitatory neuron and inhibitory neuron) (STable 7, STable 10 and STable 11). Interestingly, these findings from snRNA-seq eQTLs confirmed 6 genes from gene mapping (\u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003ePRRC2A\u003c/em\u003e, \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e), 7 genes from gene-based association test (\u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e, \u003cem\u003ePRRC2A\u003c/em\u003e, \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eMYO19\u003c/em\u003e), 9 TWAS significant genes (\u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eCAMLG\u003c/em\u003e, \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eMYO19\u003c/em\u003e, \u003cem\u003ePDZD7\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e, \u003cem\u003eTAF11\u003c/em\u003e), and 7 SMR significant genes using tissue eQTLs (\u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eEIF2AK3\u003c/em\u003e, \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eMYO19\u003c/em\u003e, \u003cem\u003eSCFD1\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eWe identified 321 risk genes using GWAS, gene mapping, gene-based association test, TWAS and SMR (STable 12). We performed a differential expression analysis of these 321 risk genes using RNA-seq from ALS and control cervical spinal cords [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], cervical spinal cords [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], thoracic spinal cords [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], lumbar spinal cords [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and motor cortex [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. 14, 109, 2, 70, and 8 genes showed statistically significant differential expression with |log2(fold change)|\u0026gt;0.10 and BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively (STable 13, STable 14, STable 15 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe found evidence of differential expression of ALS GWAS loci including 9 loci in cervical spinal cords including \u003cem\u003eDHX30\u003c/em\u003e (novel locus), \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eZHX2\u003c/em\u003e (novel locus), \u003cem\u003eCTSD\u003c/em\u003e (novel locus), \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e (STable 13 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]; 11 loci in cervical spinal cords including \u003cem\u003eNOS1AP\u003c/em\u003e (novel locus), \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eDHX30\u003c/em\u003e (novel locus), \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eZHX2\u003c/em\u003e (novel locus), \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eCTSD\u003c/em\u003e (novel locus), \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eSARM1\u003c/em\u003e (novel locus) and \u003cem\u003eUNC13A\u003c/em\u003e (STable 14 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; 6 loci in lumbar spinal cords including \u003cem\u003eNOS1AP\u003c/em\u003e (novel locus), \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eCTSD\u003c/em\u003e (novel locus) and \u003cem\u003eSARM1\u003c/em\u003e (novel locus) (STable 14 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; 4 loci in motor cortex including \u003cem\u003eLUZP2\u003c/em\u003e (novel locus), \u003cem\u003eG2E3\u003c/em\u003e, \u003cem\u003eSARM1\u003c/em\u003e (novel locus), \u003cem\u003eSOD1\u003c/em\u003e (STable 15 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Meanwhile, genes from gene mapping, gene-based association test, TWAS and SMR also showed evidence of differential expression including \u003cem\u003eLY6G5C\u003c/em\u003e (cervical, and lumbar), \u003cem\u003ePRRC2A\u003c/em\u003e (cervical and motor cortex), \u003cem\u003eDHRS11\u003c/em\u003e (cervical and motor cortex), \u003cem\u003eRESP18\u003c/em\u003e (spinal cords), \u003cem\u003eGGNBP2\u003c/em\u003e (cervical and lumbar), \u003cem\u003eAP3B2\u003c/em\u003e (cervical and cervical), \u003cem\u003eRANBP10\u003c/em\u003e (cervical, lumbar and motor cortex), \u003cem\u003eTPP1\u003c/em\u003e (cervical), \u003cem\u003eC9orf72\u003c/em\u003e (cervical) and \u003cem\u003eSCFD1\u003c/em\u003e (cervical).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe further performed a differential expression analysis of these 321 risk genes using single-nucleus RNA-seq from ALS and control cervical spinal cords [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. We found evidence of differential expression of 12 previously known ALS GWAS loci including \u003cem\u003eMOBP\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e, \u003cem\u003eERGIC1\u003c/em\u003e, \u003cem\u003eCNOT6\u003c/em\u003e, \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eMOB3B\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eG2E3\u003c/em\u003e, \u003cem\u003eUNC13A\u003c/em\u003e, \u003cem\u003eSLC9A8\u003c/em\u003e, \u003cem\u003eSOD1 and TBK1\u003c/em\u003e and 14 novel loci including \u003cem\u003eNOS1AP\u003c/em\u003e (astrocyte, oligodendrocyte, OPC, neuron and microglia, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), \u003cem\u003eIQCJ-SCHIP1\u003c/em\u003e (astrocyte and oligodendrocyte), \u003cem\u003eNOCT\u003c/em\u003e (oligodendrocyte), \u003cem\u003eNEK1\u003c/em\u003e (astrocyte, oligodendrocyte, OPC and neuron, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), \u003cem\u003ePRR16\u003c/em\u003e, (astrocyte, OPC and neuron), \u003cem\u003eKDM4C\u003c/em\u003e (astrocyte, oligodendrocyte and neuron), \u003cem\u003eCTSD\u003c/em\u003e (astrocyte, oligodendrocyte, OPC and neuron), \u003cem\u003eRBFOX1\u003c/em\u003e (oligodendrocyte, neuron, OPC and fibroblast), \u003cem\u003eSARM1\u003c/em\u003e (oligodendrocyte and OPC), \u003cem\u003eROBO2\u003c/em\u003e (astrocytes, OPC and neuron), \u003cem\u003eZHX2\u003c/em\u003e (astrocyte, neuron and OPC, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), \u003cem\u003eLUZP2\u003c/em\u003e (astrocyte, microglia, oligodendrocyte and OPC), \u003cem\u003eNPAS3\u003c/em\u003e (OPC, neuron, microglia and fibroblast, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), \u003cem\u003eKTN1\u003c/em\u003e (astrocyte and oligodendrocyte), \u003cem\u003eTIAM1\u003c/em\u003e (astrocyte, oligodendrocyte, neuron, microglia and fibroblast). Meanwhile, some genes from gene mapping, gene-based association test, TWAS and SMR also showed evidence of differential expression including \u003cem\u003eC9orf72\u003c/em\u003e (astrocyte and microglia), \u003cem\u003eSCFD1\u003c/em\u003e (OPC, neuron and microglia, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), \u003cem\u003eGGNBP2\u003c/em\u003e (OPC), \u003cem\u003eAP3B2\u003c/em\u003e (OPC), \u003cem\u003eRANBP10\u003c/em\u003e (neuron), \u003cem\u003eTPP1\u003c/em\u003e (OPC, oligodendrocyte and neuron), \u003cem\u003ePRRC2A\u003c/em\u003e (oligodendrocyte), \u003cem\u003eEIF2AK3\u003c/em\u003e (astrocyte, microglia and pericyte/SMC), \u003cem\u003eTAF11\u003c/em\u003e (astrocyte), \u003cem\u003eDNAJB1\u003c/em\u003e (OPC and neuron), \u003cem\u003eMINK1\u003c/em\u003e (astrocyte, OPC and microglia) (STable 16).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDrug-gene interaction analysis\u003c/h2\u003e \u003cp\u003eALS risk genes showed evidence of interactions with Food and Drug Administration (FDA)-approved drugs (STable 17). \u003cem\u003eC9orf72\u003c/em\u003e showed evidence of interaction with ustekinumab (interaction score\u0026thinsp;=\u0026thinsp;5.25) that was approved to treat inflammatory bowel disease [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. \u003cem\u003eSLC9A8\u003c/em\u003e showed evidence of interactions with infliximab (interaction score\u0026thinsp;=\u0026thinsp;1.684) and adalimumab (interaction score\u0026thinsp;=\u0026thinsp;1.582), which were approved to treat autoimmune diseases [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. \u003cem\u003eSOD1\u003c/em\u003e indicated a strong interaction with tofersen (interaction score\u0026thinsp;=\u0026thinsp;26.10) that was approved to treat ALS patients with a \u003cem\u003eSOD1\u003c/em\u003e mutation [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eTPP1\u003c/em\u003e from gene-based association test and TWAS indicated the strongest interaction (interaction score\u0026thinsp;=\u0026thinsp;156.6) with cerliponase alfa, which was approved to treat neuronal ceroid lipofuscinosis type 2 and slow motor and language function decline [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cem\u003eBAG6\u003c/em\u003e (interaction score\u0026thinsp;=\u0026thinsp;2.13) and \u003cem\u003ePRRC2A\u003c/em\u003e (interaction score\u0026thinsp;=\u0026thinsp;0.71) showed evidence of interactions with carbamazepine, which was approved for the treatment of epilepsy, neuropathic pain and bipolar disorder [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlation across neurodegenerative diseases\u003c/h2\u003e \u003cp\u003eWe evaluated the genetic association of ALS with other 7 neurodegenerative diseases including dementia, Alzheimer\u0026rsquo;s disease, Lewy body dementia, vascular dementia, frontotemporal dementia, Parkinson\u0026rsquo;s disease, and multiple sclerosis using LDSC [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] (STable 18). We found that ALS showed statistically significant positive genetic correlation with Parkinson\u0026rsquo;s disease (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=0.1843, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012), Alzheimer\u0026rsquo;s disease (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=0.2952, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0068) using a Bonferroni-corrected threshold \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;7.14\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (0.05/7), and suggestive positive genetic correlation with dementia (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=0.2378, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01669), and lewy body dementia (\u003cem\u003er\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e=0.4221, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) (STable 19).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we performed the largest cross-ancestry ALS GWAS meta-analysis to date in 740,868 participants including 31,254 ALS and 709,614 controls from three ancestral populations including European, East Asian, and African using both common and rare genetic variants. Stage 1 European meta-analysis identified 28 independent genome-wide significant loci with \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=26.98% by confirming 12 previously known loci [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and highlighting 16 novel loci. Stage 2 cross-ancestry GWAS meta-analysis revealed 28 independent genome-wide significant loci with \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e=26.20% by confirming 14 previously known loci [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and highlighting 14 novel loci. Collectively, we identified 36 unique independent genome-wide significant loci, and highlighted the hiding heritability of ALS, especially the contribution from rare variants.\u003c/p\u003e \u003cp\u003eOf these novel loci, \u003cem\u003eDHX30\u003c/em\u003e encodes the mitochondrial nucleoid protein and plays an important role in the development of the brain [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. \u003cem\u003eDHX30\u003c/em\u003e missense variants caused neurodevelopmental disorder with severe motor impairment and absent language [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. \u003cem\u003eDHX30\u003c/em\u003e was a key molecule underlying mitochondrial dysfunction in ALS [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. \u003cem\u003eNEK1\u003c/em\u003e is a risk gene for ALS, and its mutations caused 2\u0026ndash;3% of all ALS cases by disrupting microtubule homeostasis and nuclear import [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In ALS, the RNA-binding protein TDP-43 is depleted from the nucleus of neurons in the brain and spinal cord [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. ALS is a synaptopathy accompanied by the presence of cytoplasmic aggregates containing TDP-43, linked to about 97% of ALS cases [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. \u003cem\u003eBAG6\u003c/em\u003e prevents the aggregation of TDP-43 fragments [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. \u003cem\u003eSARM1\u003c/em\u003e was identified as a genome-wide significant locus for ALS in two previous GWAS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, this locus was not successfully replicated in subsequent GWAS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mendelian randomization revealed the causal association between \u003cem\u003eSARM1\u003c/em\u003e protein level and the risk of ALS [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. \u003cem\u003eNOS1AP\u003c/em\u003e is a novel molecular target and critical factor in TDP-43 pathology [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eKDM4C\u003c/em\u003e was a susceptibility gene for schizophrenia and autism spectrum disorder [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. \u003cem\u003eKDM4C\u003c/em\u003e overexpression significantly upregulates \u003cem\u003eApoE\u003c/em\u003e expression, ultimately promoting proliferation in mouse hippocampal neural stem cells [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Until now, the role of \u003cem\u003eKIF12\u003c/em\u003e remains unclear. \u003cem\u003eKIF5A\u003c/em\u003e and \u003cem\u003eKIF12\u003c/em\u003e are both kinesin motor proteins that move cargo along microtubules. \u003cem\u003eKIF5A\u003c/em\u003e was a genome-wide significant ALS locus in our current study and previous studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eKIF5A\u003c/em\u003e variant impaired motor unit recovery and maintenance in a knock-in mouse model [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], caused locomotor deficits, alterations of neuromuscular junctions, and motor neuron loss in Drosophila model [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], and abolished autoinhibition resulting in a toxic gain of function [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth cathepsin D (\u003cem\u003eCTSD\u003c/em\u003e) and cathepsin B (\u003cem\u003eCTSB\u003c/em\u003e) are family of cathepsin proteins. \u003cem\u003eCTSB\u003c/em\u003e was involved in the motor neuron degeneration in ALS [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Loss of \u003cem\u003eCTSD\u003c/em\u003e activity caused lysosomal dysfunction and promoteed cell-to-cell transmission of α-synuclein aggregates [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Lack of \u003cem\u003eCTSD\u003c/em\u003e in the central nervous system caused microglia and astrocyte activation and the accumulation of proteinopathy-related proteins [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Lnc-HIBADH-4 regulates autophagy-lysosome pathway in ALS by targeting \u003cem\u003eCTSD\u003c/em\u003e [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Mendelian randomization showed that genetically determined CTSD circulating protein level was associated with a higher risk of ALS (OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.00-1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. \u003cem\u003eRBFOX1\u003c/em\u003e was associated with brain amyloidosis and involved in the pathogenesis of Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. \u003cem\u003ePMAIP1\u003c/em\u003e expression was significantly downregulated in stage 2\u0026ndash;3 ALS motor cortex compared to stage 4 ALS patients [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. A recent sex-stratified GWAS identified \u003cem\u003eLUZP2\u003c/em\u003e as a male-specific genome-wide significant locus [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. \u003cem\u003eTIAM1\u003c/em\u003e was identified as a genome-wide significant locus for ALS [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. However, this locus was not successfully replicated in subsequent GWAS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Loss-of-function variants in \u003cem\u003eTIAM1\u003c/em\u003e are associated with developmental delay, intellectual disability, and seizures [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur current gene-based association test identified 114 statistically significant genes including genes within and outside the ALS GWAS loci especially \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eZNHIT3\u003c/em\u003e and \u003cem\u003eGGNBP2\u003c/em\u003e on 17q12. All these three genes were previously identified as ALS risk genes by multi omics integrative analysis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan additionalcitationids=\"CR85 CR86\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Here, we identified \u003cem\u003eDHRS11\u003c/em\u003e, \u003cem\u003eZNHIT3\u003c/em\u003e and \u003cem\u003eGGNBP2\u003c/em\u003e as the 20\u003csup\u003eth,\u003c/sup\u003e 21th and 23th significant genes, respectively. It is noted that previous gene set enrichment analysis of ALS GWAS using MAGMA did not identify any statistically significant pathways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our current gene set enrichment analysis identified 11 statistically significant pathways, all of which are GO biological processes. We highlighted the involvement of synaptic vesicle exocytosis including regulation of synaptic vesicle exocytosis (GO:2000300), synaptic vesicle exocytosis (GO:0016079) and neurotransmitter including regulation of neurotransmitter transport (GO:0051588), regulation of neurotransmitter secretion (GO:0046928), neurotransmitter secretion (GO:0007269) and neurotransmitter transport (GO:0006836) (STable 5). The presynaptic terminal is populated by synaptic vesicles containing neurotransmitters [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Synaptic vesicle exocytosis mediates neurotransmitter release from presynaptic terminals and modulate postsynaptic function [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The presynaptic synaptopathy resulting from physiological dysfunction of synapses is an early and convergent event in FTD and ALS [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. 3-T proton magnetic resonance spectroscopy showed an imbalance in excitatory and inhibitory neurotransmitters in ALS and healthy controls [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur multi omics integrative analysis including TWAS and SMR identified genes within the ALS GWAS loci and highlighting novel genes outside the ALS GWAS loci. Specifically, we identified \u003cem\u003eRESP18\u003c/em\u003e and \u003cem\u003eTPP1\u003c/em\u003e as ALS risk genes using gene-based association test, TWAS and SMR. Interestingly, a recent sex-stratified GWAS identified \u003cem\u003eRESP18\u003c/em\u003e as a male-specific genome-wide significant locus [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. \u003cem\u003eRESP18\u003c/em\u003e deficiency protected dopaminergic neurons in a Parkinson\u0026rsquo;s disease mouse model [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Mendelian randomization supported the causal association between TPP1 protein level and ALS [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. A high-content screen identified \u003cem\u003eTPP1\u003c/em\u003e as a regulator of axonal mitochondrial transport [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. \u003cem\u003eTPP1\u003c/em\u003e inhibition or knockdown enhanced axonal mitochondria transport in rat hippocampal neurons and iPSC-derived human cortical neurons, iPSC-derived motor neurons from ALS patient with one copy of SOD1\u003csup\u003eA4V\u003c/sup\u003e mutation [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Our tissue enrichment analysis showed the highest enrichment of ALS heritability in 10 GTEx v8 brain tissues, but not skeletal muscle. SMR using brain single-nucleus eQTLs datasets further identified the brain cell types where ALS risk genes may function, especially the excitatory neuron. Using spinal cord RNA-seq and snRNA-seq from ALS patients and controls, we further demonstrated the differential expression of ALS risk genes, especially the GWAS loci.\u003c/p\u003e \u003cp\u003eDrug-gene interaction analysis highlighted some FDA-approved drugs that may be associated with ALS. \u003cem\u003eC9orf72\u003c/em\u003e showed evidence of interaction with ustekinumab (interaction score\u0026thinsp;=\u0026thinsp;5.25), which is typically used to treat moderate to severe plaque psoriasis, psoriatic arthritis, moderate to severe Crohn disease, or moderate to severe ulcerative colitis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. \u003cem\u003eSLC9A8\u003c/em\u003e showed evidence of interactions with infliximab (interaction score\u0026thinsp;=\u0026thinsp;1.684) and adalimumab (interaction score\u0026thinsp;=\u0026thinsp;1.582), which were approved to treat autoimmune diseases [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A disproportionality analysis of the FDA Adverse Event Reporting System showed that ustekinumab, infliximab and adalimumab were associated with an increased risk of ALS [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eSOD1\u003c/em\u003e indicated a strong interaction with tofersen (interaction score\u0026thinsp;=\u0026thinsp;26.10), which is used to treat ALS patients with a mutation in the \u003cem\u003eSOD1\u003c/em\u003e gene [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eTPP1\u003c/em\u003e from gene-based association test and TWAS indicated the strongest interaction (interaction score\u0026thinsp;=\u0026thinsp;156.6) with cerliponase alfa, which was approved to treat neuronal ceroid lipofuscinosis type 2 and slow motor and language function decline [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cem\u003eBAG6\u003c/em\u003e (interaction score\u0026thinsp;=\u0026thinsp;2.13) and \u003cem\u003ePRRC2A\u003c/em\u003e (interaction score\u0026thinsp;=\u0026thinsp;0.71) showed evidence of interactions with carbamazepine, which had been approved for the treatment of epilepsy, neuropathic pain and bipolar disorder [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Carbamazepine has shown potential as a therapeutic agent for ALS in animal models by delaying disease onset, extending lifespan, protecting motor neurons and muscles by activating autophagy to reduce mutant \u003cem\u003eSOD1\u003c/em\u003e aggregation [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Meanwhile, carbamazepine rescued the motor dysfunction of FTD mice with TDP-43 pathology [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Therefore, cerliponase alfa and carbamazepine may be potential therapeutic agents for ALS, and clinical trials are encouraged to further validate their therapeutic benefits for patients with ALS.\u003c/p\u003e \u003cp\u003eIn summary, our current findings demonstrated that the use of both common and rare genetic variants in large-scale ALS GWAS could (1) enhance the ability to identify new loci, (2) identify rare variants of large effects, and (3) increase the proportion of ALS variance explained by known GWAS loci (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003eGWAS\u003c/sub\u003e). These genetic findings provide valuable insights into the potential underlying mechanisms of ALS, and broaden the potential therapeutic scope of the available drugs, which may contribute to future drug development targeting ALS.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS summary statistics (Cohort1):https://www.ebi.ac.uk/gwas/(accession IDs GCST90027164). GWAS summary statistics (Cohort2 and Cohort4): These GWAS summary statistics were created as part of the Million Veteran Program (MVP) genomewide PheWAS project, please see https://phenomics.va.ornl.gov/web/cipher/pheweb. GWAS summary statistics (Cohort3): https://storage.googleapis.com/finngen-public-data-r12/summary_stats/release/finngen_R12_G6_ALS.gz. GWAS summary statistics (Cohort5): http://cnsgenomics.com/data/benyamin_et_al_2017_nc/BenyaminEtAl_NatComm_Data.zip. GWAS summary statistics (Cohort6): The summary statistics are available at the Human Genetic Variation Database (Accession ID: HGV0000013). https://www.hgvd.genome.med.kyoto-u.ac.jp/repository/HGV0000013.html. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following software were used for data analyses:\u003c/p\u003e\n\u003cp\u003eMETAL (released on 2011-03-25): https://csg.sph.umich.edu/abecasis/Metal/download/, CrossMap (v0.6.5):https://github.com/liguowang/CrossMap, FUMA (v1.5.2): https://fuma.ctglab.nl/, GWASLab (v3.4.40): https://cloufield.github.io/gwaslab/\u003c/p\u003e\n\u003cp\u003e, LocusZoom (v1.4): https://github.com/statgen/locuszoom-standalone, MAGMA (v1.10): https://cncr.nl/research/magma/, FUSION: https://github.com/gusevlab/fusion_twas, COLOC (version 5): https://github.com/chr1swallace/coloc, SMR (v1.3.1) https://yanglab.westlake.edu.cn/software/smr/#Overview, SMR(online)https://yanglab.westlake.edu.cn/smr-portal/, LDSC (v1.0.1): https://github.com/bulik/ldsc, DGIdb (v5.0): https://beta.dgidb.org/.\u003c/p\u003e\n\u003cp\u003eEthical approval and consent to participate. The datasets covered in this paper provided informed consent in all corresponding original surveys. Our analyses are based on publicly available large-scale datasets rather than individual-level data. Therefore, we did not seek ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from the National Key R\u0026amp;D Program of China (Grant No. 2023YFC3605200, 2023YFC3605202), National Natural Science Foundation of China (Grant No. 82471449 and 82071212), Beijing Natural Science Foundation (Grant No. JQ21022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. G.Y.L. conceived and initiated the project. G.Y.L. and F.Z.L analyzed the data and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChia R, Chio A, Traynor BJ. Novel genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications. Lancet Neurol. 2018;17:94\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Rheenen W, van der Spek RAA, Bakker MK, van Vugt J, Hop PJ, Zwamborn RAJ, de Klein N, Westra HJ, Bakker OB, Deelen P, et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. 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Proc Natl Acad Sci U S A. 2012;109:15024\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"amyotrophic lateral sclerosis, genome-wide association study, expression quantitative trait loci, hiding heritability, RNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-8993465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8993465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenome-wide association studies (GWAS) have identified several amyotrophic lateral sclerosis (ALS) risk loci, however only explained a small proportion of ALS variance. We consider that GWAS sample sizes and rare variants may explain the hiding heritability. Here, we collected six publicly available biobanks/cohorts, and conducted the largest multi-ancestry ALS GWAS meta-analysis in 740,868 participants (31,254 ALS and 709,614 controls) from European, East Asian, and African ancestries using genetic variants with the minor allele frequency of 0.01%.\u003c/p\u003e \u003cp\u003eWe identified 36 loci (22 new) explaining 26% of ALS variance. We integrated ALS GWAS with multi-omics data, and identified 321 risk genes. Using bulk tissue and single-nucleus RNA-seq, we demonstrated significantly differential expression of 218 genes including 21 GWAS loci. Drug-gene interaction analysis identified 4 genes as the potential therapeutic targets for ALS.\u003c/p\u003e \u003cp\u003eCollectively, our findings highlight the hiding heritability of ALS and provide valuable insights into the potential underlying mechanisms of ALS.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study highlights novel loci and hiding heritability for amyotrophic lateral sclerosis in 740,868 individuals ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 15:59:46","doi":"10.21203/rs.3.rs-8993465/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9e0ee09-d3f7-456b-86c3-aff46e14e37e","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T22:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 15:59:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8993465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8993465","identity":"rs-8993465","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}