Abstract
Background and Objectives Amyotrophic lateral sclerosis (ALS) is a degenerative condition of the brain and spinal cord in which protein-coding variants in known ALS disease genes explain a minority of sporadic cases. There is a growing interest in the role of noncoding structural variants (SVs) as ALS risk variants or genetic modifiers of ALS phenotype. In small European samples, specific short SV alleles in noncoding regulatory regions of SCAF4, SQSTM1, and STMN2 have been reported to be associated with ALS, and several groups have investigated the possible role of SMN1/SMN2 gene copy numbers in ALS susceptibility and clinical severity.
Methods Using short-read whole genome sequencing (WGS) data, we investigated putative ALS-susceptibility SCAF4 (3′UTR poly-T repeat), SQSTM1 (intron 5 AAAC insertion), and STMN2 (intron 3 CA repeat) alleles in African ancestry patients with ALS and described the architecture of the SMN1/SMN2 gene region. South African cases with ALS (n = 114) were compared with ancestry-matched controls (n = 150), 1000 Genomes Project samples (n = 2,336), and H3Africa Genotyping Chip Project samples (n = 347).
Results There was no association with previously reported SCAF4 poly-T repeat, SQSTM1 AAAC insertion, and long STMN2 CA alleles with ALS risk in South Africans (p > 0.2). Similarly, SMN1 and SMN2 gene copy numbers did not differ between South Africans with ALS and matched population controls (p > 0.9). Notably, 20% of the African samples in this study had no SMN2 gene copies, which is a higher frequency than that reported in Europeans (approximately 7%).
Discussion We did not replicate the reported association of SCAF4, SQSTM1, and STMN2 short SVs with ALS in a small South African sample. In addition, we found no link between SMN1 and SMN2 copy numbers and susceptibility to ALS in this South African sample, which is similar to the conclusion of a recent meta-analysis of European studies. However, the SMN gene region findings in Africans replicate previous results from East and West Africa and highlight the importance of including diverse population groups in disease gene discovery efforts. The clinically relevant differences in the SMN gene architecture between African and non-African populations may affect the effectiveness of targeted SMN2 gene therapy for related diseases such as spinal muscular atrophy.
Glossary
- ALS=
- amyotrophic lateral sclerosis;
- C9orf72=
- Chromosome 9 Open Reading Frame 72 gene;
- CNVs=
- copy number variants;
- EGA=
- European Genome-Phenome Archive;
- GATK=
- Genome Analysis Toolkit;
- GWAS=
- genome-wide association study;
- IQR=
- interquartile range;
- LMN=
- lower motor neuron;
- MLPA=
- multiplex ligation-dependent probe amplification;
- NEK1=
- NIMA-Related Kinase 1 gene;
- PCA=
- principal component analysis;
- PCR=
- polymerase chain reaction;
- SAB=
- South African Black;
- SAC=
- South African Coloured;
- SCAF4=
- SR-Related CTD-Associated Factor 4 gene;
- SMA=
- spinal muscular atrophy;
- SMN1=
- Survival of Motor Neuron 1 gene;
- SMN2=
- Survival of Motor Neuron 2 gene;
- SNV=
- single-nucleotide variant;
- SOD1=
- Superoxide Dismutase 1 gene;
- SQSTM1=
- Sequestosome 1 gene;
- SSVs=
- short structural variants;
- STMN2=
- Stathmin 2 gene;
- SVs=
- structural variants;
- VCF=
- variant call format;
- WGS=
- whole-genome sequencing
Amyotrophic lateral sclerosis (ALS) is a progressive degenerative disease of motor neurons and typically results in death within 2–5 years of symptom onset. Multiple evidence sources converge on a multistep process underlying ALS pathogenesis, involving genetic, environmental, and aging factors, which has been replicated across different population groups.1,2 While a growing number of disease genes harboring pathogenic variants appear to be key drivers of this multistep model, such genetic factors do not explain all cases with ALS nor are they sufficient to cause ALS in all cases. Furthermore, ALS genes can also cause other diseases (pleiotropy) while patients with ALS harboring the same pathogenic gene variant can have clinically heterogeneous presentations ranging from ALS to the frontotemporal dementia phenotype in different individuals within the same family.3 Genetic modifier variants, particularly structural variants in noncoding regulatory genomic regions, may contribute to ALS risk or modify ALS phenotype expression.4,5
Copy number variants (CNVs), as well as short structural variants (SSVs <50bp), such as insertions, deletions, or short tandem repeats, are much more common than single-nucleotide variants (SNV), and they are enriched on haplotypes identified by genome-wide association studies (GWAS), suggesting that they play an important role in complex diseases.6 For SSVs, bioinformatics algorithms have been used to identify promising candidates for future study by annotating the genome-wide SSV catalog with available GWAS data.7 This strategy has been applied to ALS by focusing on candidate SSVs in the putative regulatory regions of 2 known ALS disease genes (SOD1 and SQSTM1),8,9 as well as STMN2, a candidate disease gene with altered expression in ALS.10 The association of various SSVs in these genes identified in European case control studies are summarized in Table 1. Only 1 independent study has investigated the reported ALS associated STMN2 SSV and did not replicate an association with ALS, which highlights the need for replication studies in independent cohorts.11
Previously Reported ALS-Associated Short Structural Variants (SSVs) Identified in European Cohorts
A CNV that has been speculated to play a role in ALS, and for which a specialized bioinformatics tool makes it possible to study using whole-genome sequencing data, is the copy number of the SMN (survival of motor neuron) genes. These genes have been studied extensively in a mostly pediatric motor neuron disease called spinal muscular atrophy (SMA) where the homozygous deletion of SMN1 causes SMA and the copy number of its paralog, SMN2, correlates with disease severity.12 Due to the fact that lower motor neuron weakness is an essential clinical feature of both SMA and ALS and that SMN2 gene therapy to increase SMN protein levels has entered clinical trials, the copy number state of SMN1 and SMN2 has been investigated as a possible risk factor and/or genetic modifier in ALS. Despite multiple studies reporting conflicting results regarding the association of SMN1/SMN2 gene copy numbers and ALS,13,-,16 a recent well-powered population-based meta-analysis pooling the results from various non-African ALS cohorts concluded that the SMN1/SMN2 copy number state does not contribute to ALS susceptibility or severity.17
To strengthen the credibility of association studies, replicating the analysis of genotype-phenotype links in independent and diverse cohorts is necessary. In this study, we therefore sought to investigate putative European ALS-associated SSVs (in SCAF4, SQSTM1, and STMN2) and SMN1/SMN2 copy numbers in African ancestry patients with ALS.
Methods
Patients With ALS
We included 114 African ancestry patients with ALS attending the ALS clinic at Groote Schuur Hospital, Cape Town, South Africa; 30 self-identified as South African Black (SAB) and 84 self-identified as South African Coloured (SAC) genetic ancestry, which was confirmed by ancestry principal component analysis (PCA), as previously described18 (Table 2). Patients were diagnosed by a neurologist (JMH) as clinically probable or definite ALS according to the revised El Escorial criteria.19 Thirty-four individuals in this ALS patient sample were enrolled in the CReATe Consortium's Phenotype-Genotype and Biomarker Study (PGB1).
Characteristics of South African Patients With ALS Grouped According to Ancestry
Standard Protocol Approvals, Registrations, and Patient Consents
This study was approved by the University of Cape Town Faculty of Health Sciences Human Research Ethics Committee, and all patients provided informed written consent to participate.
Population Control Data Sets
Whole-genome sequencing (WGS) data of 127 SAB and 23 SAC individuals were used as ancestry-matched controls for the ALS association analysis.20,-,22 We further analyzed control samples of various ancestries from the phase 3 call set of the 1000 Genomes Project (n = 2,336)6,23 and the H3Africa Genotyping Chip Project data set (n = 347).24
Whole-Genome Sequencing
DNA extraction was performed as previously described,25 and sequencing libraries with read lengths of 100–150bp were generated using both PCR and PCR-free kits. Libraries were sequenced to a coverage of ≥30X on the BGI MGISEQ-2000 instrument or various Illumina sequencing platforms (see eMethods, links.lww.com/NXG/A612).
Read Alignment and Variant Calling
WGS FASTQ files were aligned to the NCBI GRCH38 reference genome with alt contigs using alt aware alignment, followed by joint variant calling according to the Genome Analysis Toolkit (GATK) best-practice guidelines26 documented in github.com/grbot/varcall. SCAF4 poly-T repeat length (region NC_000021.9:g. 31671109_31671125) and SQSTM1 insAAAC (NC_000005.10:g.179830139_179830142dup) high-quality genotypes were extracted from the joint called VCF file after applying the following quality control filters: genotype quality ≥20, read depth ≥10, and an allele balance for the alternative allele of ≥0.2. A subset of these high-quality genotypes were visually inspected using the Integrative Genomics Viewer (IGV) to verify the accuracy of variant calling (see eMethods, links.lww.com/NXG/A612).
Determination of STMN2 CA Repeat Alleles
ExpansionHunter v527 was used to determine STMN2 CA repeat length (NC_000008.11:g.79641629_79641672) using a custom STMN2 variant catalog.11 STMN2 CA repeat length genotypes with a 95% confidence interval for either allele spanning a range of repeat length sizes were excluded. A subset of these high-quality repeat length genotypes were visually inspected using the Illumina REViewer tool (github.com/Illumina/REViewer) (see eMethods, links.lww.com/NXG/A612).
Determination of SMN Copy Numbers
The Illumina SMN Copy Number Caller tool was used to determine the copy number states of SMN1 and SMN2.28 Only samples that passed the quality control metric (given by: PASS:Majority or PASS:AgreeWithSome labels) were included in the analysis. Samples that did not pass the quality control metric (given by: Ambiguous or FLCNnoCall labels) included those where 5 of 8 sites in the intron 6 to exon 8 region of both SMN1 and SMN2 were not found on the haplotype sequences required to make a call. SMN copy number calls were not validated by molecular assays such as multiplex ligation-dependent probe amplification (MLPA).
Statistical Analysis
Statistical analysis was performed using GraphPad Prism v9.4.1 (GraphPad software, San Diego, CA). The Fisher exact test was used to compare 2 categorical variables, and the χ2 test was used to compare more than 2 categorical variables between groups.
Availability of Data and Materials
Anonymized whole-genome sequencing data from patients with ALS in this study form part of larger ongoing collaborative studies. Data generated by UCT Neurology will be released to bone fide researchers via the European Genome-Phenome Archive (EGA, ega-archive.org/) subject to data access committee approval after the completion of aggregate data analysis and the publication embargo period in accordance with H3Africa policy guidelines.29 Whole-genome sequencing data from the 1000 Genomes Project Phase 3 set used in this study are available through internationalgenome.org/data-portal/datacollection/30x-grch38. The H3Africa Genotyping Chip Project data sets are available by request under the following EGA accession numbers: EGAD00001004220, EGAD00001004448, EGAD00001004393, EGAD00001004316, EGAD00001004533, EGAD00001004505, EGAD00001004334, EGAD00001004557, and EGAD00001005076. The AWI-Gen Phase 1 WGS data from 100 South Africans22 (EGAD00001006418) and the Southern African Human Genome Programme data set20 (EGAD00001003791) have been deposited in the EGA.
Results
Study Population
A summary of the demographic and clinical characteristics of the South African patients with ALS in this study is provided in Table 2. Ancestry principal component analysis of South African patients with ALS using genetic markers has been previously performed.18 SAB patients cluster separately to the East and West African samples from the 1000 Genomes Project, while SAC patients are admixed with genetic contributions from Khoisan, Black African, European, and Asian individuals.18 This highlights the necessity of analyzing SAB and SAC patients with ALS from South Africa as separate ancestry groups because variant allele frequencies differ between SAB and SAC groups, which could introduce a bias if these groups are analyzed as a combined African ancestry sample for association testing.
SCAF4, SQSTM1, and STMN2 Short Structural Variants in South Africans With ALS
The SCAF4 poly-T repeat genotypes for 21 patients with ALS and 15 ancestry-matched control samples did not pass the defined quality control filters and were excluded from the analysis (see methods). Six different alleles (14T–19T) were reported at this genomic location in this study. The SCAF4 18T allele, previously reported to be associated with ALS in a largely SOD1 variant–positive cohort (Table 1), was not associated with ALS in South Africans (p ≥ 0.7) (Table 3). No SOD1 variants were found among the 10 SAC ALS 18T allele carriers, while 1 patient had a pathogenic C9orf72 expansion.
Comparison of SCAF4 Poly-T Allele Frequencies in South African Patients With ALS and Ancestry-Matched South African Controls
The SQSTM1 insAAAC genotypes for 1 patient with ALS and 4 ancestry-matched control samples did not pass the defined quality control filters and were excluded from the analysis (see methods). In this study, we detected 3 alleles: reference (-), insAAAC (approximately 50% of all samples), and insACAAAAAAC. We did not replicate the previously reported SQSTM1 insAAAC/insAAAC genotype association with ALS in our South African sample (p ≥ 0.4) (Table 4).
Comparison of SQSTM1 AAAC Insertion Frequencies in South African Patients With ALS and Ancestry-Matched South African Controls
The STMN2 CA repeat genotypes for 4 patients with ALS and 7 ancestry-matched control samples did not pass the defined quality control filters and were excluded from the analysis (see methods). The STMN2 alleles detected in this study ranged from 13-26 CA repeats. Long STMN2 CA repeats were common in both cases with ALS and controls (approximately 50%) and were not associated with ALS (p ≥ 0.2) (Table 5).
Comparison of STMN2 CA Repeat Length Frequencies in South African Patients With ALS and Ancestry-Matched South African Controls
SMN1 and SMN2 Copy Numbers in South Africans With ALS
The carrier frequency (1 copy of SMN1) for the autosomal recessive disorder, spinal muscular atrophy (SMA), was approximately 2% in African ancestry individuals in this study (cases with ALS and matched African ancestry controls) (Table 6). Approximately 50% of SAB individuals have ≥3 copies of SMN1, and the overall frequency distribution of SMN1 copy number states does not differ between cases with ALS and controls (p ≥ 0.5) (Table 6). By contrast, SMN1 copy number frequencies differed between SAC cases with ALS and ancestry-matched controls where cases with ALS had a higher frequency of SMN1 copy number 2 (75%, p = 0.03) and a lower frequency of SMN1 copy number 3 (17%, p = 0.003). Although the sample is too small for any conclusion, 2 out of 8 successfully genotyped SAC ALS patients with predominant lower motor neuron (LMN) involvement (LMN-ALS, Table 2) had ≥3 copies of SMN1 (25%). Although the overall frequency distribution of SMN2 copy number states did not differ between ALS cases and controls in both SAB and SAC groups (p ≥ 0.05), individuals lacking the SMN2 gene (12%–32% respectively) were not infrequent in this South African sample (Table 6). Total SMN protein levels were estimated using the Veldink formula (SMN1 copy number + 0.2* SMN2 copy number)30 and were similar in patients with ALS and controls for both SAB and SAC groups. SMN1 and SMN2 copy number calls from the SMN Caller tool were absent or ambiguous in 22/264 (8%) of the combined African ancestry cases with ALS and matched ancestry controls in this study.
Comparison of SMN1 and SMN2 Copy Number States in South African Patients With ALS and Ancestry-Matched South African Controls
The SMN1/SMN2 gene architecture was further explored in samples from the 1000 Genomes as well as H3Africa Genotyping Chip Projects (Figure). Samples of African ancestry had higher SMN1 copy numbers (approximately 40% have ≥3 copies) compared with non-African ancestry samples where most have 2 copies of SMN1 (p <1 × 10−4, Figure, A). The findings for the SMN2 gene were reciprocal, where African ancestry samples had lower SMN2 copy numbers (approximately 20% have no SMN2 gene copies) compared with non-African ancestry samples (approximately 9%, p <1 × 10−4, Figure, B).
SMN1 (A) and SMN2 (B) gene copy numbers in different population groups grouped according to continental ancestry (African or non-African). UK = United Kingdom, USA = United States of America, ASW = African Ancestry in Southwest US, CEU = Utah residents with Northern and Western European ancestry, GBR = British in England and Scotland, STU = Sri Lankan Tamil in the UK, ITU = Indian Telugu in the UK. The overall frequency distribution of SMN1 and SMN2 copy numbers between Afr = can (n = 994) and non-Afr = can (n = 1,669) populations was compared using a χ2 test.
Discussion
Previous work has shown that the genetic drivers of ALS may differ across geographies (i.e., an ALS associated allele may be relatively frequent in 1 ALS population but rare or absent in a different ALS population).25,31 Nonetheless, new ALS gene discoveries such as the association of NEK1 loss-of-function variants with ALS, first identified in a large European ALS cohort by unbiased gene burden analysis,32 have subsequently been replicated in multiple smaller ALS cohorts of diverse ancestries.33,34 This highlights the utility of trans-ancestry replication studies in confirming putative ALS genes. While inclusion of African cohorts are important, they are at present small and their high levels of genetic diversity and substructure require careful ancestry matching of control groups in association testing.
In this study, we did not replicate the previously reported association of various SSVs (SCAF4, SQSTM1 and STMN2) with ALS in a small sample of African ancestry patients. In contrast to previous findings8,9 where the SCAF4 and SQSTM1 variant associations were reported in European familial ALS cohorts with multiple individuals from the same family and a high frequency of pathogenic SOD1 variants (approximately 65% with SOD1 A5V variant, formerly known as A4V), our study investigated a largely sporadic African ancestry ALS cohort with only 2 related individuals and a 6% frequency of pathogenic SOD1 variants (none of which were A5V). While sampling bias (population-based, family-based, and variant-based) may have contributed to the detection of SCAF4 and SQSTM1 variant associations with ALS in previous reports, the fact that we did not replicate these associations in our small African ALS sample does not provide conclusive evidence against their role in disease. Indeed, SCAF4 and SQSTM1 ALS associated alleles were more frequent in SAC patients with ALS, who may have approximately 20% European admixture,35 although their association with ALS (OR 2.5 and 1.6 respectively) was not statistically significant. For the SCAF4 analysis, our study had 96% power to detect an odds ratio of 5, while the SQSTM1 analysis was underpowered (55% power to detect an odds ratio of 2).
STMN2 long genotypes with 24 CA repeats, previously reported to be associated with ALS in Europeans (where the control carrier frequency was approximately 5%), were common in both subpopulations of African patients with ALS and controls (approximately 25%). We did not replicate the association of long STMN2 CA repeats (with or without 24 CA) with ALS in our adequately powered African ancestry sample (80% chance of detecting an odds ratio >2), which is similar to the findings reported in a recent replication study in Europeans.11
For SMN1/SMN2 copy number calling, we used the Illumina SMN Copy Number Caller, which performs similarly (>99% accuracy) to the gold standard diagnostic detection of SMN copy numbers (digital PCR and MLPA).28 Although the tool has been designed for use on WGS data from diverse ancestries, due to the incorporation of 8 SNV sites that are nearly fixed in all populations (see Methods), we were not able to perform SMN1/SMN2 copy number calling in 8% of African ancestry individuals (owing to ambiguity at these SNV sites) which is greater than the previously reported 1% of no calls in African ancestry individuals.28 This highlights the uniqueness of Southern African genetic variation, which is not represented in the large-scale publicly available population data sets, used to train computational models and tools.
Consistent with the findings of the recent meta-analysis examining SMN1/SMN2 copy number states and ALS,17 SMN1 and SMN2 copy numbers did not differ between ALS cases and ancestry-matched controls for SAB individuals. Although the SAC patients with ALS had fewer copies of SMN1 compared with ancestry-matched controls, it is worth noting that the SAC ALS copy number distribution for SMN1 is more similar to non-African SMN1 gene architecture (Figure, A), and together with few ancestry-matched controls in this admixed group, the results could merely reflect statistically different proportions of non-African ancestry between the SAC ALS case and control groups.
Our broader analysis of SMN1/SMN2 copy numbers in diverse ancestries confirms previous reports that the architecture of these genes differs between African and non-African populations where Africans have higher copies of SMN1 and lower copies of SMN236,37 (Figure). The overall SMA carrier frequency (2% with 1 copy of SMN1) in this study (Table 6) is similar to reported frequencies in Europeans (2%–5%).37
While most individuals of non-African ancestry have 2 copies of SMN2, in this study, we confirm that most individuals with African ancestry have ≤1 copy of SMN2. This might be clinically relevant because SMA results from complete deficiency of SMN protein, and a gene-based therapy acting on SMN2 to increase SMN protein levels for therapeutic benefit has been established.38
This study highlights the usefulness of investigating putative ALS associated variants in independent population groups, particularly where the ancestry differs from the discovery cohort and underscores the importance of including African ancestry patients in gene discovery efforts. This will ensure that gene-based therapies, developed for ALS and other disorders, will benefit Africans in future.
Study Funding
N.R. Monnakgotla received funding from the University of Cape Town (fellowships administered by the Neurology Research Group, Department of Medicine and UCT). M. Nel is the recipient of a CReATe scholar award and a Carnegie Developing Emerging Academic Leaders (DEAL) award. This publication was made possible (in part) by grants from the Carnegie Corporation of New York (M. Nel), the Joost van der Westhuizen Centre for Neurodegeneration (donated by Aspen Pharmacare) and the South African National Research Foundation (J.M. Heckmann and A.C. Mahungu). The statements made and views expressed are solely the responsibility of the authors.
Disclosure
J.P. Taylor is a consultant for Nido Biosciences; M. Benatar serves on the ALS Association Board of Trustees and holds grants from NIH (R01-NS105479, U01-NS107027, U54-NS092091) and the Muscular Dystrophy Association (645863), intellectual property from the University of Miami licensed to Biogen (IP-142A), a provisional patent related to determining the onset of amyotrophic lateral sclerosis and consults for Alector, Annexon, Arrowhead, Biogen, Denali, Novartis, Orphazyme, Roche, Sanofi and UniQure; J.M. Heckmann holds a seed grant from the ALSA (23-SGP-626), serves on the scientific advisory committee for the International ALS MND Alliance and consults for Merck. The other authors declare that they have no competing interests. Go to Neurology.org/NG for full disclosures.
Acknowledgment
The authors thank the CReATe consortium and Paul Taylor's laboratory at St Jude's funded by the Amyotrophic Lateral Sclerosis Association (ALSA) and the St Jude American Lebanese Syrian Associated Charities (ALSAC) for their support and for funding the WGS data generation on 34 ALS cases. The Clinical Research in ALS and related disorders for Therapeutic Development (CReATe) Consortium (U54NS092091) is part of Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Sciences (NCATS). This consortium is funded through collaboration between NCATS and the NINDS. They acknowledge the support of the UCT Division of Computational Biology (N Mulder) who funded the whole-genome sequencing of 25 cases with ALS (Human Genome Research Institute: U24HG006941) and provided bioinformatics support (G Botha). The SAHGP dataset was generated by the Southern African Human Genome Program, a national initiative funded by the Department of Science and Technology of South Africa. This study makes use of data generated by H3Africa. A full list of the investigators who contributed to the generation of the data is available from h3africa.org. The funding for this project comes through the Human Heredity and Health in Africa (H3Africa) Initiative, which is funded by the National Institutes of Health and the Wellcome Trust through SFA Foundation. The authors acknowledge the use of the Ilifu cloud computing facility (ilifu.ac.za), a partnership between the University of Cape Town, the University of the Western Cape, the University of Stellenbosch, Sol Plaatje University, the Cape Peninsula University of Technology, and the South African Radio Astronomy Observatory. The Ilifu facility is supported by contributions from the Inter-University Institute for Data Intensive Astronomy (IDIA—a partnership between the University of Cape Town, the University of Pretoria, and the University of the Western Cape), the Computational Biology division at UCT, and the Data Intensive Research Initiative of South Africa (DIRISA). This (publication) was made possible (in part) by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the authors.
Appendix Authors
Footnotes
Go to Neurology.org/NG for full disclosures. Funding information is provided at the end of the article.
The Article Processing Charge was funded by Carnegie Corporation of New York.
Submitted and externally peer reviewed. The handling editor was Associate Editor Raymond P. Roos, MD, FAAN.
- Received February 14, 2023.
- Accepted in final form April 3, 2023.
- Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
References
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