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Rare variant analyses validate known ALS genes in a multi-ethnic population and identifies ANTXR2 as a candidate in PLS
BMC Genomics volume 25, Article number: 651 (2024)
Abstract
Background
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation.
Methods
Building on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS.
Results
A gene based collapsing model showed significant associations with SOD1, TARDBP, and TBK1 (OR = 19.18, p = 3.67 × 10–39; OR = 4.73, p = 2 × 10–10; OR = 2.3, p = 7.49 × 10–9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 (p = 4.88 × 10–7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p = 3.62 × 10–16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 (p = 8.38 × 10–6).
Conclusions
In a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3. A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2.
Background
Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease characterized by the progressive loss of upper motor neurons in the cortex and lower motor neurons of the brainstem and spinal cord. Even with FDA-approved disease modifying medication and palliation by artificial nutrition and ventilation, the prognosis is poor and death from accumulating paralysis occurs a median of 32 months after symptoms first manifest [1]. Over the last 30 years, genetic study of the 5–10% of ALS patients with family history [2, 3] have securely implicated ~ 20 monogenic causes and showed possible association to a similar number of genes (https://clinicalgenome.org/affiliation/40096/). Causative mutations in the most prevalent ALS genes (C9ORF72, SOD1, TARDBP, and FUS) explain ~ 70% of familial ALS [4, 5]. Due in part to incomplete penetrance, 10% of simplex ALS cases, which show no known family history, also carry mutations in these same genes [6].
A paucity of unsolved ALS pedigrees for family studies has intersected with falling sequencing costs for large-scale sequencing to allow gene discovery studies based on rare variant burden or collapsing methods on cohorts using predominantly simplex patients. Since our group first used this approach to implicate TBK1 and NEK1, others have also identified DNAJC7, TUBA4A and several candidates [6,7,8,9]. These analyses utilized the entire gene or recognizable functional domains as the regions for burden testing [6, 7] and were restricted to cohorts with European ancestry, or with less than 5% non-European ALS cases. Recognizing that power for discovery could be improved by a) increasing case and control numbers, b) diversifying the ancestries of participants, and c) collapsing on domains known to be intolerant of variation, we conducted both standard gene and intolerant domain-based collapsing analyses on 6,970 multi-ethnic ALS cases and ancestry-matched controls.
Primary lateral sclerosis (PLS) is also a neurodegenerative disease of motor neurons with clinical features, neuropathology, and some genetics that overlaps with ALS [10,11,12]. PLS is nearly always simplex and 20 times rarer than ALS [13]. Because large-scale sequencing studies of ALS often include PLS patients, we were able to conduct a gene-based collapsing analysis in 166 PLS multi-ethnic cases and ancestry matched controls.
Methods
Whole exome and genome sequencing
DNA sequencing was performed at Columbia University, the New York Genome Center, Duke University, McGill University, Stanford University, HudsonAlpha, and University of Massachusetts, Worcester. Kits used to conduct whole-exome capture are as follows: Agilent All Exon kits (50 MB, 65 MB, and CRE), Nimblegen SeqCap EZ Exome Enrichment kits (V2.0, V3.0, VCRome, and MedExome), and IDT Exome Enrichment panel. There were 2,185 participants with ALS who were sequenced using Nimblegen SeqCap EZ Exome Enrichment kits and 51 who were sequenced using the IDT Exome Enrichment panel (Supplemental Table 1). While 1,272 controls were evaluated using the Aligent All Exon kits, 8,498 with the IDT Exome Enrichment panel, and 11,201 with the Nimblegen SeqCap EZ Exome Enrichment kits. Sequencing was performed using Illumina GAIIx, HiSeq 2000, HiSeq 2500, and NovaSeq 6000 sequencers according to standard protocols. Whole genome sequencing was conducted at the New York Genome Center and in-house at the IGM. Sample-level BAM files were transferred from the New York Genome Center to the IGM (n = 3,418). An additional 1,316 genomes were processed by the IGM. There were 1,553 genomes in our control cohort (Supplemental Table 1). Data were aligned to the human reference genome (NCBI Build 37) using DRAGEN (Edico Genome, San Diego, CA, USA). Picard (http://picard.sourceforge.net) was used to remove duplicate reads and to process lane-level BAM files to create a sample-level BAM file. GATK was used to recalibrate base quality scores, realign around indels, and call variants utilizing the Best Practices recommendations v3.6 [14]. Variants were annotated using ClinEff and the Analysis Tool for Annotated Variants (ATAV), an in-house IGM annotation tool [15]. Variants were annotated with the Genome Aggregation Database (gnomAD) v2.1 frequencies, regional-intolerance metrics, and the clinical annotations by the Human Gene Mutation Database (HGMD), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Exonic regions were retained for downstream statistical analyses.
Sample and variant quality control
Samples reporting > 2% contamination according to verifyBamID [16] and those with consensus coding sequence (CCDS release 20) < 90% were excluded from these analyses. KING [17] was used to test for relatedness. Only unrelated (up to second-degree) individuals were included in these analyses. For related pairs, samples were chosen to prefer cases. Samples where X:Y coverage ratios did not match expected sex were excluded.
Only variants within the CCDS or the 2 bp canonical sites were included in these analyses. These variants were also required to have a quality score of at least 50, a quality by depth score of at least 5, genotype quality score of at least 20, read position rank sum of at least − 3, mapping quality score of at least 40, mapping quality rank sum greater than − 10, and a minimum coverage of at least 10. SNVs had a maximum Fisher’s strand bias of 60, while indels had a maximum of 200. For heterozygous genotypes, the alternative allele ratio was required to be greater than or equal to 30%. Only variants with the GATK Variant Quality Score Recalibration filter “PASS”, “VQSRTrancheSNP90.00to99.00”, or “VQSRTrancheSNP99.00to99.90” were included. Variants were excluded if they were marked by EVS, ExAC, or gnomAD as being failures (http://evs.gs.washington.edu/EVS/).
Clustering, ancestry, and coverage harmonization
A neural network pre-trained on samples of known ancestry was used to calculate probability estimates for six ancestry groups (African, East Asian, European, Hispanic, Middle Eastern, and South Asian). Methods for characterizing samples into clusters has been previously described [18].
To ensure balanced sequencing coverage of evaluated sites between cases and controls, we imposed a statistical test of independence between the case/control status and coverage as previously described [19]. Sites were removed where the absolute difference in percentages of cases and controls with at least 10 × coverage was greater than 7%. Samples were then pruned using this method on a cluster-by-cluster basis. Through this approach, approximately 7- 11% were removed. Clusters with less than 5 participants were not included in these analyses, thereby removing 6 participants with PLS but none with ALS.
Variant-level and gene-level statistical analysis
The models that were used to test for associations of nonsynonymous coding or canonical splice variants with outcome included variants with MAF < 0.1% for each population represented in gnomad and internal AF of < 0.1%. Models tested were a standard gene-unit collapsing analysis, and a domain-unit analysis. The models used for these analyses were previously described [7]. A domain-based approach utilizing sub-region Residual Variation Intolerance Score (subRVIS) domain percentage [7] with a threshold of 25 was also used to evaluate case enrichment of rare variants. The full list of 18,653 CCDS genes was analyzed for each model. Genes with at least one qualifying variant were included for analyses. As we are meta analyzing across clusters an exact 2-sided Cochran-Mantel–Haenszel test was used (using the statistical package in R v3.6). Study-wide significance was determined by accounting for 6 nonsynonymous models- multiplicity-adjusted significance threshold α = 4.9 × 10–7 (Supplemental Table 2). Model inflation was calculated using empirical (permutation-based) expected probability distributions as described by Povysil and colleagues [18].
ALS and PLS rare variant burden testing
We conducted both standard gene and intolerant domain-based collapsing analyses on 6,970 multi-ethnic ALS cases (87% European) and 22,534 ancestry-matched controls. Standard gene collapsing analyses identified case enrichment of rare variants (minor allele frequency of 0.001) in an ALS cohort with 12 sub-population groups (Supplemental Fig. 1A) that correspond to ancestry-based clusters (Supplemental Fig. 1B; Supplemental Table 3). Analyses were conducted on clusters with at least 3 cases. Controls were drawn from individuals sequenced for phenotypes/diseases with no known association with ALS (Supplemental Table 4). As expected, a negative control analysis for rare synonymous variants found no case-enrichment (Supplemental Fig. 2). Because gene-based collapsing considers variation across the entire gene, regions that are tolerant of variation could swamp case-enrichment signals originating from regions that are intolerant to variation [7]. To overcome this limitation, we conducted rare variant collapsing on domains that are intolerant to variation as defined as a subRVIS domain score threshold of 25, a cutoff based on threshold testing.
As large-scale sequencing studies of ALS often include PLS patients, we were able to conduct a gene-based collapsing analysis in 166 PLS multi-ethnic cases (88% European) and 17,695 ancestry matched controls (Supplemental Fig. 3; Supplemental Tables 5). We expected the study would be underpowered for securely implicating causative genes but used this as an opportunity to generate candidates for future study.
ALS gene set enrichment analyses
An ALS gene set enrichment analysis was conducted using the gene strength association list outlined in Table 1. We utilized the qualifying variants that were associated with ALS in each gene set category and used the exact two-sided CMH test to analyze burden of ALS genes defined by gene set. These lists were curated using data published by Gregory and colleagues [20] and the Clinical Genome Resource (ClinGen). As outlined, “ALS Definite” genes were found to have ample published replication evidence, while ‘ALS Plus’ genes had some replication data and/or functional evidence for an association with ALS. However, ‘ALS Moderate genes, required additional replication analyses and/or functional data, and ‘ALS Limited’ genes were genes that overlapped with ALS phenotypically.
Results
Rare variant burden testing
Collapsing analysis of all rare functional variants (missense and protein truncating variants) (Supplemental Table 2) found genome-wide and study-wide significant (p < 4.9 × 10–7) case-enrichment for known ALS genes SOD1, TARDBP, TBK1 (OR = 19.18, p = 3.67 × 10–39; OR = 4.73, p = 2 × 10–10; OR = 2.3, p = 7.49 × 10–9, respectively) and control-enrichment for ALKBH3 (OR = 0.26, p = 4.88 × 10–7) (Fig. 1A; Supplemental Data). Although SOD1, TBK1 and TARDBP are definitive ALS genes, we were intrigued by the identification of control-enriched ALKBH3. Control-enrichment was not explained by sequencing methodology, ancestry cluster, or specific phenotype/disease population within the control cohort. Because ALKBH3 plays a role in DNA repair [21], a mechanism increasingly implicated in ALS pathogenesis [22], we attempted to replicate this novel association by analyzing summary statistics from the Project MinE cohort, which is similar in size to ours [23]. None of the available models focused on variation as rare as in our analyses, but at a higher minor allele frequency (MAF) for qualifying variants (0.005), a minor degree of control-enrichment was in fact observed (OR = 0.56, p = 3.96 × 10–4). This raises the possibility that rare missense and protein truncating variants (PTVs) in ALKBH3 could protect from ALS, a finding that requires validation in large cohorts.
Q-Q plots of gene- and domain-level collapsing of ALL functional coding variants in ALS cohort. A The results for a standard gene-level collapsing of 6,970 ALS cases and 22,524 controls. P-values were generated using an exact two-sided Cochran-Mantel–Haenszel (CMH) by gene by cluster. The genes with the top associations that achieved study-wide significance of p < 4.9 × 10–7 (SOD1 (OR = 19.18), TARDBP (OR = 4.73), TBK1 (OR = 2.3), and ALKBH3 (OR = 0.26)) are labeled. SOD1, TARDBP, TBK1 have been previously implicated in rare variant association studies of ALS. Yellow and green lines indicate the 2.5th and 97.5.th percentile of expected p-values, respectively. B The results for the domain-based collapsing restricting qualifying variants to those with subRVIS domain percentage score < 25 of 6,970 cases and 22,524 controls. P-values were generated using an exact two-sided Cochran-Mantel–Haenszel (CMH) by gene by cluster. The genes with the top associations (SOD1 (OR = 20.63), TARDBP (OR = 10.08), and TBK1 (OR = 3.15)) are labeled. C Standard gene-level collapsing model showed 44 qualifying variants in cases (red circles) and 31 in controls (blue circles) for TARDBP (D) subRVIS domain collapsing improved association by removing control variants (cases = 43; controls = 15). Regions with subRVIS domain percentage below 25 are highlighted in orange while those above this threshold are highlighted in blue. A one tailed z-score showed that there were significantly less controls in the intolerant domain as indicated by subRVIS domain percentage score < 25 (p = 0.031)
Intolerant domain analyses implicated the same three known ALS genes (SOD1, TARDBP, and TBK1 at OR = 20.63, p = 1.68 × 10–38; OR = 10.08, p = 3.62 × 10–16; and OR = 3.15, p = 8.38 × 10–11, respectively) (Fig. 1B; Supplemental Data). The intolerant domain analysis did not improve over the gene-based analysis for SOD1 or TBK1 (Fig. 2; Fig. 3) but doubled the odds ratio and significantly lowered the p-value obtained for TARDBP. The improvement of the intolerant domain model (Fig. 1C, 1D) stemmed from a significant drop (one-tailed z-score p = 0.031) in the number of qualifying variants found in controls dispersed across tolerant regions, while highlighting qualifying variants in ALS cases predominantly in the intolerant C-terminal region.
Plot of gene- and domain-level collapsing of ALL SOD1 functional coding variants. Standard gene-level collapsing model showed 93 qualifying variants in cases (red circles) and 18 in controls (blue circles) for SOD1. subRVIS domain collapsing improved association by removing control variants (cases = 90; controls = 16). Regions with subRVIS domain percentage below 25 are highlighted in orange while those above this threshold are highlighted in blue. However, a one tailed z-score showed that the differences in the number of controls in the intolerant domain was not significantly lower than those in the entire gene as indicated by subRVIS domain percentage score < 25 (p = 0.4)
Plot of gene- and domain-level collapsing of ALL TBK1 functional coding variants. Standard gene-level collapsing model showed 73 qualifying variants in cases (red circles) and 143 in controls (blue circles) for TBK1. subRVIS domain collapsing improved association by removing control variants (cases = 47; controls = 72). Regions with subRVIS domain percentage below 25 are highlighted in orange while those above this threshold are highlighted in blue. However, a one tailed z-score showed that the differences in the number of controls in the intolerant domain was not significantly lower than those in the entire gene as indicated by subRVIS domain percentage score < 25 (p = 0.3)
Although most models showed no significant genes, the dominant PTV model showed significant case enrichment for ANTXR2 (OR = 174.57, p = 8.38 × 10–6) (Fig. 4; Supplemental Table 6; Supplemental Data), a gene associated with brain connectivity changes and Alzheimer’s disease [24]. Currently, there are no additional large sequencing studies of PLS in which we could attempt replication.
Q-Q plot of gene-level collapsing of protein truncating variants (PTV) in PLS cohort. The results for a standard gene-level collapsing of 166 PLS cases and 17,695 controls. P-values were generated using an exact two-sided Cochran-Mantel–Haenszel (CMH) by gene by cluster. The gene with the top associations that achieved genome-wide significance of p < 8.38 × 10–6 (ANTXR2 (OR = 174.57)) is labeled. ANTXR2 has not been previously implicated in rare variant association studies of PLS. Yellow and green lines indicate the 2.5th and 97.5th percentile of expected p-values, respectively
ALS gene set enrichment analyses
A gene set enrichment analysis of genes that were defined as ‘ALS Definite’ were significantly associated with ALS for all dominant models, including PTV only (p < 1.49 × 10–72), PTV & Missense (p < 7.93 × 10–24), and Missense only (p < 8.27 × 10–28) (Fig. 5). The synonymous model, which served as a control, showed no association (p = 0.5) between these genes and ALS. Genes that are moderately associated with ALS, ‘ALS Moderate, showed significant enrichment of rare variants for the PTV & Missense (p < 7.9 × 10–6), as well as the Missense only (p < 8.18 × 10–5) models. The group of genes that were described as having limited evidence, ‘ALS Limited’, showed a significant association with rare variants and ALS for the PTV only model (p = 0.032). For all other models, rare variants in these genes were not significantly associated with ALS. An analysis of genes that are characterized as ‘ALS Plus’ showed no significant association of rare variants with ALS for the 4 models that were analyzed.
Forest plot of ALS genes by model. Rare variants in “ALS Definite” genes were significantly associated with ALS in gene-based collapsing models except the control synonymous model. Rare variants in “ALS Moderate” genes were associated with ALS in “PTV & Missense” and “Missense” gene-based collapsing model. There was no association with ALS of rare variants in “ALS Plus” and a weak association in the PTV “ALS Limited” genes. Pooled odds ratio, 95% confidence intervals, and p-values were generated from exact two-sided Cochran-Mantel–Haenszel (CMH) tests
Discussion
Burden testing
Conducting genic and intolerant domain based collapsing analyses in a large multi-ethnic population provides insight into novel and established biological mechanisms in disease manifestations. Additionally, analyzing specific disease subtypes can capture critical disease pathways that could be targets for clinical intervention. Here we show, that performing collapsing analyses in multi-ethnic populations and in disease subtypes found novel genetic associations in individuals diagnosed with ALS and PLS. These analyses implicated ALS genes that have previously been identified (SOD1, TARDBP, and TBK1). We also identified ALKBH3 as a potentially protective gene that warrants further study in additional cohorts. In addition, we conducted the first rare variant collapsing analysis in PLS, identifying PTVs in ANTXR2. This gene will need to be investigated further in larger PLS cohorts or in targeted functional analyses. Lastly, gene set enrichment analyses provide evidence that genes known to be associated with ALS show strong evidence to have a rare variant burden especially for protein truncating variants.
ALKBH3 associates with ALS
We found that genic collapsing analyses of individuals diagnosed with ALS identified known risk genes (SOD1, TARDBP, and TBK1) and a novel protective gene (ALKBH3). ALKBH3 encodes for AlkB homolog 3, Alpha-Ketoglutarate Dependent Dioxygenase which protects against the cytotoxicity of methylating agents by repair of the specific DNA lesions [25,26,27]. ALKBH3 potentially acts as a putative hyperactive promotor to suppress transcription associated DNA damage of highly expressed genes [28]. Genes that play a role in DNA repair and DNA damage response such as TARDBP, FUS, and NEK1 [29,30,31,32] are known to play a role in ALS potentially through neuronal death pathways.
ANTXR2 associated with PLS
Genic collapsing analyses of protein truncating variants on individuals with PLS identified a suggestive gene (ANTXR2). ANTXR2 encodes a receptor for anthrax toxin that may be involved in extracellular matrix adhesion. Variants in this gene have been associated with hyaline fibromatosis [33, 34], and has been shown to play a role in angiogenesis [35]. This finding adds to the number angiogenic genes that have been implicated in ALS including VEGF and ANG [36].
While we identified a potentially important gene that is associated with PLS, we were limited in our sample size and will therefore need additional cohorts or functional studies to further investigate this finding. Additionally, there are potentially more ALS subtypes that could be investigated to better understand this heterogeneous disease. Lastly, unknown confounders could be contributing to the signal that are found in these association analyses.
Conclusions
In summary, we performed the largest rare variant analyses of a multi-ethnic population of patients with ALS to date. Our analysis did not identify new ALS risk genes but demonstrated that collapsing models informed by regions of intolerance can be useful for identifying genes where disease-associated variation is limited to regions with low background variation. This analysis also confirmed the association of the C-terminal domain of TARDBP. We also identified ALKBH3 as a potentially protective gene that warrants further study in additional and larger cohorts. Finally, we conducted the first rare variant collapsing analysis in PLS, identifying PTVs in ANTXR2 as a candidate disease gene. This association and potential mechanisms for PTVs in this gene will need to be investigated further in larger PLS cohorts.
It is important to note that this analysis doubled the number of ALS cases and quadrupled the number of controls from our first study [6] but remained underpowered for the identification of new ALS genes. A recently published rare variant burden analysis with a similar number of ALS cases did not identify new genes [23] either, emphasizing the need for increasingly large genomically characterized ALS cohorts, especially in non-European populations.
Availablity of data and materials
All summary data generated during this study are included in this published article and its supplementary information files.
References
Dorst J, Ludolph AC, Huebers A. Disease-modifying and symptomatic treatment of amyotrophic lateral sclerosis. Ther Adv Neurol Disord. 2018;11:1756285617734734.
Chen S, Sayana P, Zhang X, Le W. Genetics of amyotrophic lateral sclerosis: an update. Mol Neurodegener. 2013;8:28.
Gibson SB, Figueroa KP, Bromberg MB, Pulst SM, Cannon-Albright L. Familial clustering of ALS in a population-based resource. Neurology. 2014;82(1):17–22.
Chia R, Chiò A, Traynor BJ. el genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications. Lancet Neurol. 2018;17(1):94–102.
Boylan K. Familial Amyotrophic Lateral Sclerosis. Neurol Clin. 2015;33(4):807–30.
Cirulli ET, Lasseigne BN, Petrovski S, Sapp PC, Dion PA, Leblond CS, et al. Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Science. 2015;347(6229):1436–41.
Gelfman S, de Araujo Moreno C Martins, Dugger S, Ren Z, Wolock CJ, Shneider NA, et al. A new approach for rare variation collapsing on functional protein domains implicates specific genic regions in ALS. Genome Res. 2019;29(5):809–18.
Jih KY, Tsai PC, Tsai YS, Liao YC, Lee YC. Rapid progressive ALS in a patient with a DNAJC7 loss-of-function mutation. Neurol Genet. 2020;6(5): e503.
Smith BN, Ticozzi N, Fallini C, Gkazi AS, Topp S, Kenna KP, et al. Exome-wide rare variant analysis identifies TUBA4A mutations associated with familial ALS. Neuron. 2014;84(2):324–31.
Tartaglia MC, Rowe A, Findlater K, Orange JB, Grace G, Strong MJ. Differentiation between primary lateral sclerosis and amyotrophic lateral sclerosis: examination of symptoms and signs at disease onset and during follow-up. Arch Neurol. 2007;64(2):232–6.
Statland JM, Barohn RJ, Dimachkie MM, Floeter MK, Mitsumoto H. Primary Lateral Sclerosis. Neurol Clin. 2015;33(4):749–60.
Mitsumoto H, Nagy PL, Gennings C, Murphy J, Andrews H, Goetz R, et al. Phenotypic and molecular analyses of primary lateral sclerosis. Neurol Genet. 2015;1(1): e3.
Turner MR, Barohn RJ, Corcia P, Fink JK, Harms MB, Kiernan MC, et al. Primary lateral sclerosis: consensus diagnostic criteria. J Neurol Neurosurg Psychiatry. 2020;91(4):373–7.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.
Ren Z, Povysil G, Hostyk JA, Cui H, Bhardwaj N, Goldstein DB. ATAV: a comprehensive platform for population-scale genomic analyses. BMC Bioinformatics. 2021;22(1):149.
Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, Abecasis GR, et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet. 2012;91(5):839–48.
Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–73.
Povysil G, Chazara O, Carss KJ, Deevi SVV, Wang Q, Armisen J, et al. Assessing the Role of Rare Genetic Variation in Patients With Heart Failure. JAMA Cardiol. 2021;6(4):379–86.
Petrovski S, Todd JL, Durheim MT, Wang Q, Chien JW, Kelly FL, et al. An Exome Sequencing Study to Assess the Role of Rare Genetic Variation in Pulmonary Fibrosis. Am J Respir Crit Care Med. 2017;196(1):82–93.
Gregory JM, Fagegaltier D, Phatnani H, Harms MB. Genetics of Amyotrophic Lateral Sclerosis. Current Genetic Medicine Reports. 2020;8(4):121–31.
Kuang W, Jin H, Yang F, Chen X, Liu J, Li T, et al. ALKBH3-dependent m(1)A demethylation of Aurora A mRNA inhibits ciliogenesis. Cell Discov. 2022;8(1):25.
Konopka A, Atkin JD. DNA Damage, Defective DNA Repair, and Neurodegeneration in Amyotrophic Lateral Sclerosis. Front Aging Neurosci. 2022;14: 786420.
van Rheenen W, van der Spek RAA, Bakker MK, van Vugt J, Hop PJ, Zwamborn RAJ, et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat Genet. 2021;53(12):1636–48.
Elsheikh SSM, Chimusa ER, Mulder NJ, Crimi A. Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease. Sci Rep. 2020;10(1):1433.
Duncan T, Trewick SC, Koivisto P, Bates PA, Lindahl T, Sedgwick B. Reversal of DNA alkylation damage by two human dioxygenases. Proc Natl Acad Sci U S A. 2002;99(26):16660–5.
Bian K, Lenz SAP, Tang Q, Chen F, Qi R, Jost M, et al. DNA repair enzymes ALKBH2, ALKBH3, and AlkB oxidize 5-methylcytosine to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine in vitro. Nucleic Acids Res. 2019;47(11):5522–9.
Jiang F, Wang L, Dong Y, Nie W, Zhou H, Gao J, et al. DPPA5A suppresses the mutagenic TLS and MMEJ pathways by modulating the cryptic splicing of Rev1 and Polq in mouse embryonic stem cells. Proc Natl Acad Sci U S A. 2023;120(30): e2305187120.
Liefke R, Windhof-Jaidhauser IM, Gaedcke J, Salinas-Riester G, Wu F, Ghadimi M, et al. The oxidative demethylase ALKBH3 marks hyperactive gene promoters in human cancer cells. Genome Med. 2015;7(1):66.
Sreedharan J, Blair IP, Tripathi VB, Hu X, Vance C, Rogelj B, et al. TDP-43 mutations in familial and sporadic amyotrophic lateral sclerosis. Science. 2008;319(5870):1668–72.
Kwiatkowski TJ Jr, Bosco DA, Leclerc AL, Tamrazian E, Vanderburg CR, Russ C, et al. Mutations in the FUS/TLS gene on chromosome 16 cause familial amyotrophic lateral sclerosis. Science. 2009;323(5918):1205–8.
Vance C, Rogelj B, Hortobágyi T, De Vos KJ, Nishimura AL, Sreedharan J, et al. Mutations in FUS, an RNA processing protein, cause familial amyotrophic lateral sclerosis type 6. Science. 2009;323(5918):1208–11.
Kenna KP, van Doormaal PT, Dekker AM, Ticozzi N, Kenna BJ, Diekstra FP, et al. NEK1 variants confer susceptibility to amyotrophic lateral sclerosis. Nat Genet. 2016;48(9):1037–42.
Denadai R, Raposo-Amaral CE, Bertola D, Kim C, Alonso N, Hart T, et al. Identification of 2 novel ANTXR2 mutations in patients with hyaline fibromatosis syndrome and proposal of a modified grading system. Am J Med Genet A. 2012;158A(4):732–42.
Hanks S, Adams S, Douglas J, Arbour L, Atherton DJ, Balci S, et al. Mutations in the gene encoding capillary morphogenesis protein 2 cause juvenile hyaline fibromatosis and infantile systemic hyalinosis. Am J Hum Genet. 2003;73(4):791–800.
Reeves CV, Dufraine J, Young JA, Kitajewski J. Anthrax toxin receptor 2 is expressed in murine and tumor vasculature and functions in endothelial proliferation and morphogenesis. Oncogene. 2010;29(6):789–801.
Lambrechts D, Lafuste P, Carmeliet P, Conway EM. Another angiogenic gene linked to amyotrophic lateral sclerosis. Trends Mol Med. 2006;12(8):345–7.
Acknowledgements
We thank the following groups for contributing ALS/PLS samples, sequencing, or clinical data:
New York Genome Center AL S Consortium : J. Kwan, D. Sareen, J.R. Broach, Z. Simmons, X. Arcila-Londono, E.B. Lee, V.M. Van Deerlin, E. Fraenkel, L.W. Ostrow, F. Baas, N. Zaitlen, J.D. Berry, A. Malaspina, P. Fratta, G.A. Cox, L.M. Thompson, S. Finkbeiner, E. Dardiotis, T.M. Miller, S. Chandran, S. Pal, E. Hornstein, D.J. MacGowan, T. Heiman-Patterson, M.G. Hammell, N.A. Patsopoulos, J. Dubnau, and A. Nath.
AL S Exome Sequencing Consortium : S.H. Appel, R.H. Baloh, R.S. Bedlack, R. Brown, W.K. Chung, S. Gibson, J.D. Glass, A. Gitler, D.B. Goldstein, T.M. Miller, R.M. Myers, S.M. Pulst, J.M. Ravits, G. Rouleau, E. Greene, N. Shneider, and W.W. Xin;
Genomic Translation for AL S Care (GTAC) study : S.H. Appel, R.H. Baloh, R.S. Bedlack, S. Chandran, L. Foster, S. Goutman, E. Green, C. Karam, D. Lacomis, G. Manousakis, T.M. Miller, S. Pal, D. Sareen, A. Sherman, Z. Simmons, L. Wang.
AL S COSMO S Study Sites Group : Columbia University Coordinating Center, NY, NY: Hiroshi Mitsumoto, MD, DSc, Pam Factor-Litvak, PhD, Regina Santella, PhD, Howard Andrews, PHD; Texas Neurology, P.A., Dallas, TX: Daragh Heitzman, MD; Duke University, Durham, NC: Richard S. Bedlack, MD, PhD; California Pacific Medical Center, San Francisco, CA: Jonathan S. Katz, MD, Robert Miller, MD, Dallas Forshew; University of Kansas, Kansas City, KS: Richard J. Barohn, MD, PhD; Mayo Clinic, Rochester, MN; Dr. Eric J. Sorenson, MD; University of California—Davis, Sacramento, CA: Bjorn E. Oskarsson, MD, PhD; University of Kentucky, Lexington, KY: Edward J. Kasarskis, MD, PhD; University of California—San Francisco, San Francisco, CA: Catherine Lomen-Hoerth, MD, PhD, Jennifer Murphy, PhD; University of Colorado, Aurora, CO: Yvonne D. Rollins, MD, PhD; University of California – Irvine, Orange, CA: Tahseen Mozaffar, MD; University of Nebraska, Omaha, NE; J. Americo M. Fernandes, MD; University of Iowa, Iowa City, IA: Andrea J. Swenson, MD; University of Texas—Southwestern, Dallas: Sharon P. Nations, MD; SUNY—Upstate Medical University, Syracuse, NY: Jeremy M. Shefner, MD, PhD; and Hospital for Special Care, New Britain, CT: Jinsy A. Andrews, MD, MS, Dr. Agnes Koczon-Jaremko.
PL S COSMO S Study Group : Columbia University Irving Medical Center, NY, NY: Hiroshi Mitsumoto, MD, DSc, Peter L. Nagy, MD, PhD, Pam Factor-Litvak, PhD, PhD, Rejina Santella, PhD, Howard Andrews, PhD, Raymond Goetz, PhD; Icahn School of Medicine—Mount Sinai, NY, NY: Chris Gennings, PhD; University of California—San Francisco, San Fransisco, CA: Jennifer Murphy, PhD; National Institute of Neurological Disorders and Stroke, Bethesda, MD: Mary Kay Floeter, MD, PhD; University of Kansan Medical Center, Kansas City, KS: Richard J. Barohn, MD; University of Texas, Dallus, TX: Sharon Nations, MD; Western University, London, Ontario: Christen Shoesmith, MD; and University of Kentucky, Louisville, KT: Edward Kasarskis, MD, PhD.
We thank The Washington Heights–Inwood Columbia Aging Project (WHICAP) for the contribution of control samples. We also thank the WHICAP study participants and the WHICAP research and support staff for their contributions to this study: K. Welsh-Bomer, C. Hulette, and J. Burke; D. Valle, J. Hoover-Fong, and N. Sobriera; A. Poduri; S. Palmer; R. Buckley; K. Newby; The Murdock Study Community Registry and Biorepository Pro00011196; National Institute of Allergy and Infectious Diseases Center for HIV/AIDS Vaccine Immunology (CHAVI) (U19-AI067854); National Institute of Allergy and Infectious Diseases Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (UM1-AI100645); CHAVI Funding; R. Ottman; V. Shashi; S. Berkovic, I. Scheffer, and B. Grinton; The Epi4K Consortium and Epilepsy Phenome/Genome Project; C. Depondt, S. Sisodiya, G. Cavalleri, and N. Delanty; S. Hirose; C. Woods, C. Village, K. Schmader, S. McDonald, M. Yanamadala, and H. White; G. Nestadt, J. Samuels, and Y. Wang; D. Levy; E. Pras, D. Lancet, and Z. Farfel; S. Chen; R. Wapner; C. Moylan, A. Mae Diehl, and M. Abdelmalek; DUHS (Duke University Health System) Nonalcoholic Fatty Liver Disease Research Database and Specimen Repository; M. Winn and R. Gbadegesin; M. Hauser; S. Delaney; A. Need and J. McEvoy; M. Walker; M. Sum; Undiagnosed Diseases Network; National Institute on Aging (R01AG037212, P01AG007232).
CONSORTIA
New York Genome Center ALS Consortium: Matthew B Harms1,5,6, Justin Kwan10, Dhruv Sareen11, James R Broach12, Zachary Simmons13, Ximena Arcila-Londono14, Edward B Lee15, Vivianna M Van Deerlin15, Ernest Fraenkel16, Lyle W Ostrow17, Frank Baas18, Noah Zaitlen19, James D Berry20, Andrea Malaspina21, Pietro Fratta22, Gregory A Cox23, Leslie M Thompson24, Steve Finkbeiner25, Efthimios Dardiotis26, Timothy M Miller27, Siddharthan Chandran28, Suvankar Pal28, Eran Hornstein29, Daniel J MacGowan30, Terry Heiman-Patterson31, Molly G Hammell32, Nikolaos A Patsopoulos33, Joshua Dubnau34, and Avindra Nath35.
10 Department of Neurology, University of Maryland School of Medicine, University of Maryland ALS Clinic, Baltimore, United States of America
11 Cedars-Sinai Department of Biomedical Sciences, Board of Governors Regenerative Medicine Institute and Brain Program, Cedars-Sinai Medical Center, Los Angeles, United States of America
12 Department of Biochemistry and Molecular Biology, Penn State Institute for Personalized Medicine, The Pennsylvania State University, Hershey, United States of America
13 Department of Neurology, The Pennsylvania State University, Hershey, United States of America
14 Department of Neurology, Henry Ford Health System, Detroit, United States of America
15 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States of America
16 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States of America
17 Department of Neurology, Johns Hopkins School of Medicine, Baltimore, United States of America
18 Department of Neurogenetics, Academic Medical Centre, Amsterdam and Leiden University Medical Center, Leiden, Netherlands
19 Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, United States of America
20 ALS Multidisciplinary Clinic, Neuromuscular Division, Department of Neurology, Harvard Medical School, Boston, United States of America
21 Centre for Neuroscience and Trauma, Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
22 Institute of Neurology, National Hospital for Neurology and Neurosurgery, University College London, London, United Kingdom
23 The Jackson Laboratory, Bar Harbor, United States of America
24 Department of Psychiatry and Human Behavior and Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, United States of America
25 Taube/Koret Center for Neurodegenerative Disease Research, Roddenberry Center for Stem Cell Biology and Medicine, Gladstone Institute, San Francisco, United States of America
26 Department of Neurology and Sensory Organs, University of Thessaly, Thessaly, Greece
27 Department of Neurology, Washington University in St. Louis, St. Louis, United States of America
28 Centre for Clinical Brain Sciences, Anne Rowling Regenerative Neurology Clinic, Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh, United Kingdom
29 Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
30 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, United States of America
31 Center for Neurodegenerative Disorders, Department of Neurology, the Lewis Katz School of Medicine, Temple University, Philadelphia, United States of America
32 Cold Spring Harbor Laboratory, Cold Spring Harbor, United States of America
33 Computer Science and Systems Biology Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States of America
34 Department of Anesthesiology, Stony Brook University, Stony Brook, United States of America
35 Section of Infections of the Nervous System, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States of America
Scottish Genomes Partnership: Timothy J. Aitman8, Nicola Williams36,37, Jonathan Berg38, Ruth McGowan36, Zosia Miedzybrodzka39, Mary Porteous40, Edward Tobias36
36 West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, Scotland UK
37 Genomics England, QMUL, Dawson Hall, London, EC1M 6BQ England UK
38 University of Dundee Human Genetics Unit, Level 6, Ninewells Hospital, Dundee, DD1 9SY Scotland UK
39 Institute of Medical Sciences, University of Aberdeen, North of Scotland Regional Genetic Service, NHS Grampian
40 South East Scotland Genetic Service, NHS Lothian, Western General Hospital, Edinburgh, Scotland UK
ALS COSMOS Study Group:Hiroshi Mitsumoto5,41, Pam Factor-Litvak41, Regina Santella41, Howard Andrews41, Daragh Heitzman42, Richard S. Bedlack43, Jonathan S. Katz44, Robert Miller44, Dallas Forshew44, Richard J. Barohn45, Eric J. Sorenson46,Bjorn E. Oskarsson47,Edward J. Kasarskis48,Catherine Lomen-Hoerth49, Jennifer Murphy49, Yvonne D. Rollins50, Tahseen Mozaffar51, J. Americo M. Fernandes52, Andrea J. Swenson53, Sharon P. Nations54, Jeremy M. Shefner55, Jinsy A. Andrews56, Agnes Koczon-Jaremko56
41 Columbia University Coordinating Center, New York, New York, United States of America
42 Texas Neurology, P.A., Dallas, Texas, United States of America
43 Duke University, Durham, North Carolina, United States of America
44 California Pacific Medical Center, San Francisco, California, United States of America
45 University of Kansas, Kansas City, Kansas, United States of America
46 Mayo Clinic, Rochester, Minnesota, United States of America
47 University of California - Davis, Sacramento, California, United States of America
48 University of Kentucky, Lexington, Kentucky, United States of America
49 University of California - San Francisco, San Francisco, California, United States of America
50 University of Colorado, Aurora, Colorado, United States of America
51 University of California – Irvine, Orange, California, United States of America
52 University of Nebraska, Omaha, Nebraska, United States of America
53 University of Iowa, Iowa City, Iowa, United States of America
54 University of Texas - Southwestern, Dallas, Texas, United States of America
55 SUNY - Upstate Medical University, Syracuse, New York, United States of America
56 Hospital for Special Care, New Britain, Connecticut, United States of America
PLS COSMOS Study Group: Hiroshi Mitsumoto5,41,57, Peter L. Nagy57, Pam Factor-Litvak41,57, Rejina Santella41,57, Howard Andrews41,57, Raymond Goetz57, Chris Gennings58, Jennifer Murphy49, Mary Kay Floeter59, Richard J. Barohn45, Sharon Nations54, Christen Shoesmith60, Edward Kasarskis48
57 Columbia University Irving Medical Center, New York, New York, United States of America
58 Icahn School of Medicine - Mount Sinai, New York, New York, United States of America
59 National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, United States of America
60 Western University, London, Ontario, Canada
Genomic Translation for ALS Care (GTAC) study: Matthew B Harms1,5,6, Stanley Appel61, Robert Baloh62, Richard Bedlack63, Siddharthan Chandran64, Laura Foster65, Stephen Goutman66, Ericka Greene67, Chafic Karam68, David Lacomis69, George Manousakis70, Timothy Miller71, Suvankar Pals72, Dhruv Sareen73, Alex Sherman74, Zachary Simmons75, Leo Wang76
61 Department of Neurology, Houston Methodist, Houston, Texas, United States of America
62 Department of Neurology, Cedars Sinai, Los Angeles, California, United States of America
63 Department of Neurology, Duke University, Durham, North Carolina, United States of America
64 Department of Neurology, University of Edinburgh, Edinburgh, Scotland, UK
65 Department of Neurology, University of Colorado, Aurora, Colorado, United States of America
66 Department of Neurology, University of Michigan, Ann Arbor, Michigan, United States of America
67 Department of Neurology, Houston Methodist, Houston, Texas, United States of America
68 Department of Neurology, Oregon Health Sciences University, Portland, Oregon, United States of America
69 Department of Neurology, University of Pittsburg Medical Center, Pittsburgh, Pennsylvania, United States of America
70 Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
71 Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, United States of America
72 Department of Neurology, University of Edinburgh, Edinburgh, Scotland, UK
73 Cedars Sinai, Los Angeles, California, United States of America
74 Massachusetts General Hospital, Boston, Massachusetts, United States of America
75 Department of Neurology, The Pennsylvania State University, College of Medicine, Hershey, Pennsylvania, United States of America
76 Department of Neurology, University of Washington, Seattle, Washington, United States of America
Funding
The collection of ALS and PLS samples and data was funded in part by: The Scottish Genomes Partnership (Chief Scientist Office of the Scottish Government Health Directorates (SGP/1) and The Medical Research Council Whole Genome Sequencing for Health and Wealth Initiative (MC/PC/15080); The New York Genome Center ALS Consortium (ALS Association 15-LGCA-234, 19-SI-459, and the Tow Foundation; The GTAC study (ALS Association 16-LGCA-310 and Biogen Idec); ALS Exome Sequencing Consortium (Biogen Idec). Funding for the ALS 561 COSMOS and PLS COSMOS studies was provided by NIEHS R01ES016348, the Muscular 562 Dystrophy Association, MDA Wings Over Wall Street, Spastic Paraplegia Foundation (SPF), private 563 donations from Mr. and Mrs. Marren, the Adams Foundation, and Ride for Life.
The collection of control samples and data was funded in part by: Bryan ADRC NIA P30 AG028377; NIH RO1 HD048805; Gilead Sciences, Inc.; D. Murdock; National Institute of Allergy and Infectious Diseases Center for HIV/AIDS Vaccine Immunology (CHAVI) (U19-AI067854); National Institute of Allergy and Infectious Diseases Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (UM1-AI100645); Bill and Melinda Gates Foundation; NINDS Award# RC2NS070344; New York-Presbyterian Hospital; The Columbia University College of Physicians and Surgeons; The Columbia University Medical Center; NIH U54 NS078059; NIH P01 HD080642; The J. Willard and Alice S. Marriott Foundation; The Muscular Dystrophy Association; The Nicholas Nunno Foundation; The JDM Fund for Mitochondrial Research; The Arturo Estopinan TK2 Research Fund; UCB; Epilepsy Genetics Initiative, A Signature Program of CURE; Epi4K Gene Discovery in Epilepsy study (NINDS U01-NS077303) and The Epilepsy Genome/Phenome Project (EPGP - NINDS U01-NS053998); The Ellison Medical Foundation New Scholar award AG-NS-0441-08; National Institute Of Mental Health of the National Institutes of Health under Award Number K01MH098126; B57 SAIC-Fredrick Inc. M11-074; OCD Rare 1R01MH097971-01A1. This research was supported in part by funding from Funding from the Duke Chancellor's Discovery Program Research Fund 2014; an American Academy of Child and Adolescent Psychiatry (AACAP) Pilot Research Award; NIMH Grant RC2MH089915; Endocrine Fellows Foundation Grant; The NIH Clinical and Translational Science Award Program (UL1TR000040); NIH U01HG007672; The Washington Heights Inwood Columbia Aging Project; The Stanley Institute for Cognitive Genomics at Cold Spring Harbor Laboratory and the Utah Foundation for Biomedical Research. Data collection and sharing for the WHICAP project (used as controls in this analysis) was supported by The Washington Heights–Inwood Columbia Aging Project (WHICAP, PO1AG07232, R01AG037212, RF1AG054023) funded by the National Institute on Aging (NIA) and by The National Center for Advancing Translational Sciences, National Institutes of Health, through Grant Number UL1TR001873. This manuscript has been reviewed by WHICAP investigators for scientific content and consistency of data interpretation with previous WHICAP Study publications. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Conception and design: T.D.P., J.E.M., D.B.G., and M.B.H. Data acquisition: C.A.M.M., Z.R., H.P., T.J.A., J.S.L, H.M., D.B.G., and M.B.H. Analysis: T.D.P. and M.B.H. Interpretation, draft and review, and final approval: all authors. T.D.P. and M.B.H. had full access to the study data and take responsibility for the integrity of the data and accuracy of analyses. All authors read and approved the final manuscript.
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Pottinger, T.D., Motelow, J.E., Povysil, G. et al. Rare variant analyses validate known ALS genes in a multi-ethnic population and identifies ANTXR2 as a candidate in PLS. BMC Genomics 25, 651 (2024). https://doi.org/10.1186/s12864-024-10538-1
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DOI: https://doi.org/10.1186/s12864-024-10538-1