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. Author manuscript; available in PMC: 2026 Oct 17.
Published before final editing as: Neuromuscul Disord. 2026 Sep 12;68:107424. doi: 10.1016/j.nmd.2026.107424

Chronological Patterns of Pathogenic Variant Distributions in Muscular Dystrophy Genes

Trek D Stenger 1,2, Christine C Bruels 1, Peter B Kang 1,2,3
PMCID: PMC13674580  NIHMSID: NIHMS2212790  PMID: 42833167

Abstract

Background.

Newly discovered genetic subtypes of muscular dystrophy tend to be rarer than older ones, as occurs for other genetically heterogeneous classes of inherited disorders. Epidemiologically, there is a higher likelihood of ascertaining more prevalent genetic subtypes, but it is unclear why pathogenic variants are more abundant in certain genetic subtypes than others. We hypothesized that distributions of pathogenic variant types and molecular consequences of those variants for each genetic subtype vary based on the year of discovery of the muscular dystrophy gene.

Methods.

We compiled a list of genes associated with muscular dystrophy with pathogenic or likely pathogenic variants listed in ClinVar, then extracted those variants for analysis. To explore the relationship between the year of gene discovery and distribution of variant types, we performed Kendall’s τ-b test for each variant type.

Results.

Sixty-four muscular dystrophy genes had pathogenic or likely pathogenic variants listed in ClinVar. We found a significant negative correlation with year of gene discovery in the proportion of frameshift, splice acceptor, and splice donor variants and a corresponding significant positive correlation in missense variants for muscular dystrophy genes. There was a decrease in the observed / expected missense score and an increase in missense z-score by year of discovery, indicating intolerance to missense changes in more recently discovered genes. Other trends included fewer insertions and deletions, and increased single nucleotide variants (SNVs) by year of gene discovery.

Discussion.

Our findings suggest that recently discovered muscular dystrophy genes are less tolerant of variation, potentially explaining why variants and cases are less abundant in these genes.

Keywords: muscular dystrophy, tolerance to variation, long-read sequencing, ClinVar

1. Introduction

Muscular dystrophy is a category of genetic diseases that cause progressive weakness and loss of muscle mass. Since the discovery in 1986 of DMD, the gene associated with Duchenne muscular dystrophy (DMD) [1], investigators have identified over 60 additional genes with variants that are pathogenic for muscular dystrophies [2]. The more recently discovered genetic subtypes of muscular dystrophy tend to be rarer than those discovered earlier (Figure 1). For instance, a 2017 study involving more than 3,000 patients diagnosed with Limb-Girdle Muscular Dystrophies (LGMD) revealed that 40% of cases were linked to just five genes—CAPN3, DYSF, ANO5, FKRP, and COL6A1—all of which were discovered in 2010 or earlier [3]. Decades ago, before next generation sequencing became available, knowledge of the relative frequencies of various muscular dystrophy genes had an immediate diagnostic impact, as genetic testing was typically restricted to individual genes that were tested sequentially using Sanger sequencing techniques and/or multiplex ligation-dependent probe amplification (MLPA), with multi-gene panels being costly and limited in scope. Even today, relative frequencies of muscular dystrophy genes inform interpretation of next generation sequencing findings on panel, exome, and genome sequencing.

Figure 1.

Figure 1.

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a) Box plots show the log-scaled unique number of P/LP variants per gene over decades. b) Box plots show the unique number of P/LP variants per gene over decades, normalized by protein size by dividing the number of P/LP variants in each gene by the number of amino acids in the corresponding protein. c) Timeline shows the year each gene analyzed in the current study was originally reported to be associated with muscular dystrophy.

From an epidemiologic perspective, more common genetic subtypes of muscular dystrophy would be expected to be discovered earlier. We hypothesize that distributions of pathogenic variant types and molecular consequences of those variants vary among muscular dystrophy genes, and that those distributions show temporal trends based on the year of discovery for each gene. If confirmed, knowledge of the temporal patterns of variant distributions for individual genes will help clinicians interpret genetic test reports, particularly those that include variants of unknown significance (VUS).

To test our hypothesis, we compiled a list of established muscular dystrophy genes and evaluated patterns of known pathogenic (P) and likely pathogenic (LP) variants in those genes based on the year of gene discovery.

2. Methods

2.1. Identification of genes associated with muscular dystrophy.

We compiled a comprehensive list of muscular dystrophy genes using the 2025 edition of the widely used gene tables of muscular dystrophies and congenital muscular dystrophies (Figure 1) [2]. For each of these genes, we documented the year of the first published study that associated it with muscular dystrophy, as well as the inheritance pattern, phenotype, and PubMed ID of the publication.

2.2. Variant Extraction from ClinVar.

We used publicly available genomic variant data from ClinVar to analyze pathogenic variant distribution patterns among muscular dystrophy genes [4]. Using a custom Python script, we automated the process of extracting variant data from ClinVar. The gene names from the previous step were read in using the Python data manipulation package pandas [5]. We then collected the counts of all unique pathogenic and likely pathogenic variants by using the requests package in Python to query the NCBI E-utilities API to retrieve ClinVar variant data in XML format [6]. The data were downloaded on June 10th, 2026 using the June 8th, 2026 weekly release. We pulled all variants in these genes that had an aggregate classification of likely pathogenic or pathogenic. We did not filter by review status. We analyzed all such variants in these genes, including those that were associated with other conditions. We filtered the variants to include only those that were located in the gene of interest or in overlapping genes to avoid including variants that spanned hundreds or thousands of genes, which were typically large copy number variants (CNVs). However, large insertions, deletions, duplications, and CNVs were included if they were located within the gene of interest or in overlapping genes. Data extraction was conducted using a gene-centric, rather than transcript-centric, approach. For completeness, we captured all molecular consequence terms associated with the variant across all transcripts reported within the ClinVar aggregate record. For genes such as DYSF, where the clinically relevant transcript (MANE Select Plus Clinical) differs from the canonical transcript (MANE Select), this approach reduces the risk of missing clinically relevant annotations compared to a single-transcript approach, provided those annotations are present in the ClinVar aggregate record. However, transcript coverage within ClinVar is submitter-dependent and may not be complete for all variants. By including only P/LP variants, and by selecting only the most severe molecular consequence per variant using our standardized severity hierarchy, as discussed below, we excluded clinically irrelevant molecular consequences associated with non-canonical transcripts. The XML was parsed using Python’s xml package to extract the numbers of unique pathogenic and likely pathogenic variants of each variant type and molecular consequence for each gene. Of note, variant classification criteria in ClinVar have changed over time; for simplicity we use variant classifications as they were defined in April 2026. We also classified each variant by size: structural variants (≥50 nucleotides, abbreviated SVs) and small variants (<50 nucleotides). Variants in ClinVar often have multiple associated molecular consequences. To avoid duplicate counting of such variants, we selected a single consequence per variant with the highest expected severity based on a standardized functional hierarchy as detailed in Table 1.

Table 1.

Hierarchy of expected variant molecular consequence severity.

Rank Molecular Consequence Impact Tier
1 Frameshift High
2 Nonsense High
3 Stop lost High
4 Splice acceptor variant High
5 Splice donor variant High
6 Inframe insertions and deletions Moderate
7 Missense Moderate
8 Initiator codon variant Moderate
9 Synonymous Low
10 5′ UTR variant Modifier
11 3′ UTR variant Modifier
12 Intron variant Modifier
13 Non-coding transcript variant Modifier
14 Genic upstream transcript variant Modifier
15 Genic downstream transcript variant Modifier
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This order was based on the order of severity estimated by Ensembl Variant Effect Predictor found at https://www.ensembl.org/info/genome/variation/prediction/predicted_data.html and modified to incorporate clinical observations [7]. Specifically, we considered frameshift variants to be more consistently severe than nonsense variants, and stop lost variants to be more consistently severe than splice acceptor and splice donor variants, based on our clinical experience.

2.3. Extraction of Other Gene-Level Data.

We collected the encoded protein length for each gene by number of amino acids, and gene intolerance estimation values, specifically, the probability of loss-of-function intolerance (pLI), loss-of-function observed/expected upper bound fraction (LOEUF), gene percentile, o/e missense, missense z-score, o/e synonymous, and synonymous z-score. The protein length was collected from UniProt release 2026_01, using the isoform identified as the canonical sequence [8]. The gene intolerance scores for each gene were collected from gnomAD v4.1.1 based on genome build GRCh38 by searching for each gene name and documenting the scores manually [9–11].

2.4. Variant Analysis.

We determined the proportions of variant types for each gene and plotted the proportions against the year of gene discovery. The variant analysis was conducted using a custom Python script. First, using the pandas package, the raw counts for each variant type were normalized by dividing them by the total number of unique likely pathogenic or pathogenic variants in each gene, allowing for comparisons based on proportions rather than raw counts [5]. To explore the relationship between the year of gene discovery and distribution of variant types, we performed Kendall’s τ-b test for each variant type, with the year of discovery as X and the proportion of a given variant type in each gene as Y [12]. Similar to Spearman’s ρ, Kendall’s τ-b tests for monotonic correlation without the assumption of a specific distribution, a linear trend, or constant variance [13]. However, unlike Spearman’s ρ, Kendall’s τ-b is robust to ties, which are prevalent in our dataset due to multiple genes being discovered in the same year. We performed the tests using the kendalltau function from the scipy.stats package [14]. Significance was determined with a 0.05 threshold for the corresponding p-value.

3. Results

3.1. Muscular dystrophy genes.

We identified 64 established muscular dystrophy genes (Figure 1 and Table 2) [2]. While now listed as a hereditary cardiomyopathy in the gene table, we included DTNA in analyses as this was included in previous versions of the gene table, and a recent publication [15] links DTNA with muscular dystrophy. Also, while ACTA1, CACNA1S, CAVIN1, GAA, GOLGA2, KBTBD13, RYR1, TRAPPC2L, TRIP4, and VCP are included in the 2025 gene table, we excluded them from analyses as these genes are not primarily associated with muscular dystrophy. We excluded DUX4 because facioscapulohumeral muscular dystrophy (FSHD) is caused by inappropriate expression of DUX4 as a secondary phenomenon rather than by variants within DUX4 itself [16]. All genes on our final list represent primary mutational drivers where disease is directly initiated by pathogenic variants within the gene itself, including the FSHD upstream epigenetic regulators SMCHD1 and LRIF1, as well as genes encoding enzymes involved in the glycosylation of α-dystroglycan. From a clinical perspective, the final list of genes represents the ones of greatest clinical relevance for individuals with muscular dystrophy.

Table 2.

List of all muscular dystrophy genes analyzed along with that gene's year of association with MD, inheritance pattern, phenotype, count of likely pathogenic or pathogenic (LP/P) variants located within the gene and overlapping genes, and PubMed ID.

Gene Year Described Inheritance Phenotypes Count of LP/P Variants PubMed ID Key Reference Citation
DMD 1986 XR DMD, BMD 3340 3773991 [1]
EMD 1994 XR EDMD 114 7894480 [14]
SGCA 1994 AR LGMD 192 8069911 [15]
CAPN3 1995 AR, AD LGMD 537 7720071 [16]
LAMA2 1995 AR CMD, LGMD 1048 7550355 [17]
SGCB 1995 AR LGMD 137 7581448, 7581449 [19,20]
SGCG 1995 AR LGMD 123 7481775 [18]
COL6A1 1996 AR, AD CMD, LGMD 202 8782832 [21]
COL6A2 1996 AR, AD CMD, LGMD 279 8782832 [21]
PLEC 1996 AR LGMD, MD (other) 157 8698233, 8696340, 8894687 [23–25]
SGCD 1996 AR LGMD 61 8841194 [22]
CAV3 1998 AD LGMD, DD 28 9537420 [26]
COL6A3 1998 AR, AD CMD, LGMD 208 9536084 [27]
DYSF 1998 AR LGMD, DD 949 9731526, 9731527 [30,31]
FKTN 1998 AR CMD, LGMD 185 9690476 [28]
ITGA7 1998 AR CMD 64 9590299 [29]
LMNA 1999 AD, AR CMD, EDMD 559 10080180 [32]
TCAP 2000 AR LGMD 35 10655062 [33]
FKRP 2001 AR CMD, LGMD 229 11741828 [34]
POMGNT1 2001 AR CMD, LGMD 267 11709191 [35]
SELENON 2001 AR RSMD 115 11528383 [36]
POMT1 2002 AR CMD, LGMD 201 12369018 [37]
TRIM32 2002 AR LGMD 74 11822024 [38]
TTN 2002 AR LGMD 5844 12145747 [39]
LARGE1 2003 AR CMD 78 12966029 [40]
DNM2 2005 AD CMD 29 16227997 [41]
POMT2 2005 AR CMD, LGMD 157 15894594 [42]
SYNE1 2007 AD EDMD 389 17267447 [43]
SYNE2 2007 AD EDMD 10 17267447 [43]
DPM3 2009 AR LGMD 14 19576565 [44]
FHL1 2009 XR EDMD 104 19716112 [45]
ANO5 2010 AR LGMD, DD 204 20096397 [46]
CHKB 2011 AR CMD 44 21665002 [47]
DAG1 2011 AR CMD, LGMD 22 21388311 [48]
TMEM43 2011 AD EDMD 3 21391237 [49]
DNAJB6 2012 AD LGMD 14 22334415 [50]
POMGNT2 2012 AR CMD, LGMD 30 22958903 [51]
RXYLT1 2012 AR CMD 56 23217329 [52]
SMCHD1 2012 AD FSHD 144 23143600 [53]
B3GALNT2 2013 AR CMD 69 23453667 [54]
B4GAT1 2013 AR CMD 6 23359570 [55]
CRPPA 2013 AR LGMD, CMD 87 22522420, 22522421 [56,57]
GMPPB 2013 AR LGMD, CMD 60 23768512 [58]
POMK 2013 AR CMD, LGMD 27 23519211 [60]
TNPO3 2013 AD LGMD 9 23543484, 23667635 [61,62]
TRAPPC11 2013 AR LGMD, CMD 76 23830518 [59]
COL12A1 2014 AR, AD CMD 137 24334604 [63]
HNRNPDL 2014 AD LGMD 3 24647604 [79]
TOR1AIP1 2014 AR LGMD 30 24856141 [64]
LIMS2 2015 AR LGMD 3 25589244 [66]
POPDC1 2015 AR LGMD 6 26642364 [65]
POGLUT1 2016 AR LGMD 24 27807076 [67]
INPP5K 2017 AR CMD 12 28190456, 28190459 [69,70]
MSTO1 2017 AR CMD 21 28544275 [68]
GOSR2 2018 AR CMD 28 29855340 [71]
PYROXD1 2018 AR LGMD, CMD 30 30515627 [72]
POPDC3 2019 AR LGMD 6 31610034 [73]
GGPS1 2020 AR MD 4 32403198 [74]
LRIF1 2020 AR FSHD 1 32467133 [75]
JAG2 2021 AR CMD, LGMD 5 33861953 [76]
DTNA 2023 AD LGMD 1 36799992 [77]
HMGCR 2023 AR LGMD 7 36745799 [78]
HMGCS1 2024 AR RSMD 9 39531736 [81]
SNUPN 2024 AR LGMD 4 38366623 [82]
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There were 17,068 pathogenic or likely pathogenic variants in muscular dystrophy genes or in overlapping genes in ClinVar with an annotated variant type. Among those variants, a definitive variant size could not be programmatically derived for 55 variants. This primarily occurred when breakpoints for large deletions were uploaded as uncertain intervals rather than exact single-base coordinates, or when size estimates were documented solely within unstructured free-text comments. Because the entries in question lacked standard coordinate boundaries required for automated size calculation, those variants were excluded from analyses, leaving a final dataset of 17,013 variants. Among the 17,013 variants, molecular consequences were annotated for 15,294. The remaining 1,719 variants were annotated as an “Unknown” molecular consequence and were not included in the molecular consequence analyses, though they were included for the variant type analyses.

3.2. Variant type distributions.

We analyzed the significance of the relationship between gene year of discovery and variant types. We excluded variant types that appeared less than 10 times each across all the muscular dystrophy genes in ClinVar: complex, compound heterozygous, diplotype, distinct chromosomes, fusion, inversion, protein only, tandem duplication, translocation, and variation. Thus, the variant types that we included in the analysis were copy number gains, copy number losses, deletions, indels, insertions, duplications; microsatellites; and single nucleotide variants. We combined the variants classified as insertions, deletions, and indels into a single category, then split it based on variant size, leaving two categories: Small indels, containing all insertions and deletions less than 50bp, and structural insertions and deletions, containing all insertions and deletions 50bp or greater. Additionally, it is important to note that ClinVar does not equate microsatellites with tandem repeat expansions. Instead, it defines a microsatellite variant simply as a variant within a microsatellite region. The vast majority of these microsatellite variants in MD genes are single repeat unit deletions that occur where the reference genome contains only two to three trinucleotide or tetranucleotide repeats. Among the variant types, copy number gains, copy number losses, duplications, microsatellites, and structural insertions and deletions had a significant negative correlation with year of gene discovery, while SNVs had a positive significant correlation when looking at all of the muscular dystrophy genes (Figure 2). Among genes associated with exclusively autosomal recessive inheritance, copy number losses, duplications, microsatellites, small indels, and structural insertions and deletions had a significant negative correlation with year of gene discovery while SNVs had a positive significant correlation. Among exclusively autosomal dominant genes and bimodal genes (genes that are associated with both dominant and recessive inheritance), copy number gains were absent with no significant changes in any proportions of variant types over year of discovery. Of note, SNVs encompass nonsense variants, missense variants, and splice variants, thus the trend for SNVs cannot be used to assess changes in patterns of truncating versus non-truncating variants. The significance by gene inheritance category is summarized in Table 3. Our variant distribution findings indicate that to detect the broadest range of pathogenic variants possible, it is important to scan for the full range of variant sizes, from SNVs to large SVs.

Figure 2.

Figure 2.

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Distributions of variant types across all muscular dystrophy-associated genes (n = 64). Each dot represents a gene; darker dots indicate overlapping points where multiple genes share the same coordinates. The x-axis shows the year each gene was first linked to muscular dystrophy, while the y-axis shows the proportion of all pathogenic or likely pathogenic variants in that gene that fall into the specified variant type.

Table 3.

Comparison of p-values and significance by gene inheritance category. “Autosomal Bimodal” includes genes described as having both recessive and dominant inheritance patterns.

Autosomal Dominant (n = 10) Autosomal Recessive (n = 45) Autosomal Bimodal (n = 6) All (n = 63)
P Value Significance P Value Significance P Value Significance P Value Significance
Variant Types Copy number gain N/A — No variation 0.160 N/A — No variation 0.037 *
Copy number loss 0.726 0.037 * 0.846 0.009 **
Duplication 0.401 0.002 ** 0.444 0.002 **
Microsatellite 0.682 0.027 * 0.702 0.025 *
Small indel 0.513 0.013 * 0.251 0.063
Structural insertion and deletion 0.179 0.001 ** 0.251 1.27E-05 ****
Single nucleotide variant 0.928 0.001 *** 1.000 0.002 **
Molecular Consequences Frameshift 0.926 2.62E-04 *** 0.251 0.006 **
Intron variant 0.823 0.047 * 0.126 0.003 **
Missense variant 0.360 0.002 ** 0.702 0.006 **
Nonsense 0.287 0.410 0.702 0.443
Small inframe indels 0.575 0.119 0.702 0.014 *
Splice acceptor variant 0.654 0.261 0.251 0.037 *
Splice donor variant 0.923 0.012 * 0.702 0.008 **
Synonymous variant 0.726 0.006 ** 0.846 4.76E-04 ***
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The “All” category encompasses all genes, regardless of inheritance mode. Pvalues are color-coded based on the direction of the correlation from the Kendall’s Tau-b test: green indicates a positive correlation with year of discovery, while red indicates a negative correlation. Asterisks denote statistical significance as follows:

*

p < 0.05

**

p < 0.01

***

p < 0.001

****

p < 0.0001

and no asterisk indicates p ≥ 0.05 (not significant).

3.3. Molecular consequence distributions.

We analyzed the relationship between year of gene discovery and various molecular consequences. We excluded the molecular consequences annotated as 3’ UTR, 5’ UTR, genic downstream transcript variant, genic upstream transcript variant, initiator codon variants, no sequence alteration, non-coding transcript, and stop loss from analyses, as there were less than 10 of each of these combined across all the muscular dystrophy genes. We thus analyzed the following categories: frameshift variants; inframe deletions; inframe insertions; intronic variants; missense, nonsense, splice acceptor, splice donor, and synonymous variants. We again combined all variants classified with a molecular consequence of inframe indel, inframe insertion, and inframe deletion into one group, then split by variant size to have small inframe indels of less than 50bp and structural inframe insertions and deletions 50bp or greater. However, there were less than 10 structural inframe insertions and deletions in the dataset, so these were excluded from analyses. Frameshift, intronic, small inframe indels, splice acceptor, splice donor, and synonymous variants had a significant negative correlation with year of gene discovery, whereas missense variants had a significant positive correlation (Figure 3). We performed our analysis on the exclusively recessive genes and found that frameshift, intronic, splice acceptor, splice donor, and synonymous variants had a significant negative correlation with year of gene discovery while missense variants had a significant positive correlation. There were no significant trends among the exclusively dominant or bimodal genes. There were no significant changes in the proportions of nonsense variants for any subsets of genes. (Figures S1). The findings by gene inheritance category are summarized in Table 3. Our molecular consequence findings indicate that for broad detection of pathogenic variants, it is important to sequence both coding and non-coding regions.

Figure 3.

Figure 3.

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Distributions of molecular consequences across all analyzed muscular dystrophy-associated genes (n = 64). Each dot represents a gene; darker dots indicate overlapping points where multiple genes share the same coordinates. The x-axis shows the year each gene was first linked to muscular dystrophy, while the y-axis shows the proportion of all pathogenic or likely pathogenic variants in that gene that fall into the specified molecular consequence.

3.4. Distributions of other gene-level metrics.

We analyzed the relationship between gene year of discovery versus variant length, protein length, count of splice variants per splice site and gene intolerance scores. The proportion of small variants had a significant positive correlation with year of gene discovery. Both the proportion of structural variants and the average variant size had a significant negative correlation with year of gene discovery (Figure 4). There was a sharp negative correlation in the number of splice site variants per splice site over year of gene discovery (Figure 5a). After performing Bonferroni test correction, the o/e missense scores had a significant negative correlation with year of gene discovery and the missense z-score had a positive correlation with year of gene discovery (Figure 5b and 5c). We found no statistically significant changes in the protein size over year of discovery. (Figure S2).

Figure 4.

Figure 4.

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Distributions of average variant size in base pairs among genes and the proportions of small and structural variants. The x-axis shows the year each gene was first linked to muscular dystrophy, while the y-axis shows the proportion of all pathogenic or likely pathogenic variants in that gene that fall into the specified variant size. For panel c, the y-axis shows the average variant size.

Figure 5.

Figure 5.

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Distributions of additional gene-level metrics. a) the number of splice site variants per splice site among all muscular dystrophy-associated genes (n = 64). Each dot represents a gene; darker dots indicate overlapping points where multiple genes share the same coordinates. The x-axis shows the year each gene was first linked to muscular dystrophy, while the y-axis shows the average count of splice site variants per splice site. b) Distributions of observed/expected missense scores in all muscular dystrophy-associated genes (n = 64). c) Distributions of missense z-scores in all muscular dystrophy-associated genes (n = 64). Each dot represents a gene; darker dots indicate overlapping points where multiple genes share the same coordinates. The x-axis shows the year each gene was first linked to muscular dystrophy, while the y-axis shows the LOEUF score of that gene.

4. Discussion

Our study reveals chronological shifts in the types of pathogenic and likely pathogenic variants associated with muscular dystrophies by year of discovery, marked by a significant decline in frameshift splice donor variants, corresponding with a significant rise in missense variants. To provide further support for our findings, we collected gene intolerance scores of each gene to assess whether the observed changes in variant types were associated with lower tolerance to variation [11]. The o/e missense scores had a significant negative correlation with year of gene discovery and the missense z-scores had a significant positive correlation with year of gene discovery, indicating that more recently discovered muscular dystrophy genes are less tolerant of missense variants. Combined with the finding of a significant increase in the proportion of pathogenic missense variants over year of gene discovery, it appears that missense changes are generally depleted in healthy individuals for the newer genes, and that missense variants are more likely to be sufficient to cause disease when they occur. Similar findings that show missense-depleted regions in controls have high concentrations of ClinVar pathogenic variants and de novo missense variants [17]. These trends suggest that pathogenic variants in the more recently identified muscular dystrophy-associated genes may be rarer because these genes are less tolerant to missense variants than earlier-discovered genes. In genes that are less tolerant of missense changes, missense variants are sufficient to cause disease, and truncating variants may lead to embryonic or fetal lethality and thus are rarely observed [18]. As a result, the pathogenic variants observed in more recently discovered, rarer muscular dystrophy genes are more likely to be missense changes, which can alter protein function without completely eliminating its biological productivity. This hypothesis is also supported by the finding of a decrease in the number of pathogenic splice site variants per splice site as the decrease in the proportion of splice site variants over year of discovery cannot simply be attributed to newly discovered genes having fewer splice sites.

A limitation of our study is that we counted unique variants, without accounting for the frequency of specific variants in the relevant subtypes of muscular dystrophy. Based on currently available data, there is not a rigorous means of taking this factor into account, given the broad range of available data on variant frequencies in different muscular dystrophy populations. Founder variants tend to be specific for certain communities or countries, and are thus not generalizable across the globe. Examples of such founder variants include the retrotransposal insertion of FKTN in Japan [19] and Korea [20], the FKRP c.826C>A, p.L276I variant in Hutterite and other northern European populations [21], the SGCG c.848G>A, p.C283Y variant found in the Roma community [22], and the SGCG c.787G>A, p.E263K variant in Puerto Rico [23]. Given the large number of muscular dystrophy genes and the population-specific nature of all the founder variants identified to date, even the most common founder variants are only responsible for small proportions of muscular dystrophy cases globally. Thus, we believe that repeating our analyses while incorporating estimates of variant frequencies would yield similar results. Additionally, there are numerous other factors that likely influence the abundance of pathogenic variants that were not analyzed in our study, including those affecting spontaneous mutation rates, population frequency, and mechanisms of disease. Further, the intricacies of ClinVar itself likely play a role in reported abundance. Certain genes are included on more genetic tests and are therefore evaluated more often than others. Other factors, such as the type of genetic test and the voluntary nature of variant submissions to ClinVar influence the count of pathogenic variants, but were not assessed due to lack of high-quality data relating to these factors. It is also important to recognize that because ClinVar is a submission-driven database, certain genes, variant types, and molecular consequences may exhibit differing rates of classification accuracy.

We observed a significant chronological shift in the types of pathogenic variants in muscular dystrophies by year of gene discovery, marked by a negative correlation in frameshift splice acceptor, and splice donor variants and a positive correlation in missense variants. This pattern supports the hypothesis that distributions of pathogenic variant types and molecular consequences of those variants for each genetic subtype vary based on the year of discovery of the muscular dystrophy gene. These patterns, combined with the finding that more recently discovered muscular dystrophy genes are more intolerant of missense variation, may indicate that more recently discovered muscular dystrophy subtypes are rarer because their associated genes are less tolerant of missense variation. While neither pLI nor LOEUF showed a significant correlation with year of gene discovery after performing Bonferroni correction, we believe these more recently discovered muscular dystrophy genes may also be highly intolerant of truncating or loss-of-function variants, which could be too severe to be compatible with life, as evidenced by their absence or rarity in observed patients. These findings underscore the importance of considering missense and other non-truncating variants in both gene discovery and variant interpretation.

Our findings have several implications for muscular dystrophy research and gene discovery. The observed shift toward missense variants in more recently identified genes suggests that the more obvious pathogenicity of truncating variants may bias discovery toward a subset of genes that are more tolerant of variation. Consequently, genes critical to muscle function but intolerant of missense or loss-of-function variation may remain undetected or under-characterized. More widespread adoption of long-read whole-genome sequencing will help detect a broader range of pathogenic variants [24].

Conclusion

Our work highlights the importance of capturing patterns of variant type distributions among muscular dystrophy subtypes. Applying similar approaches to other rare genetic disorders may help uncover broad patterns in variant distribution and gene constraint, contributing to a deeper understanding of their genetic architecture. A better understanding of those patterns may help refine the interpretation of genetic variants in both clinical and research contexts. Variant reanalysis, sequence reanalysis, and new sequence analysis should include the full range of variant sizes as well as both coding and non-coding regions for optimal detection of pathogenic variants.

Supplementary Material

1

Figure S1. Distributions of the proportion of nonsense variants across all analyzed muscular dystrophy-associated genes (n = 64).

2

Figure S2. Distributions of protein size determined by the number of amino acids in all analyzed muscular dystrophy-associated genes (n = 64).

Table S1. Definitions of variant types and molecular consequences in ClinVar.

Highlights.

  • Older genetic subtypes of muscular dystrophy are more common than newer ones

  • It is unclear why certain muscular dystrophy genes are more common than others

  • Frameshift and splice variants are more frequent in older subtypes of muscular dystrophy

  • Single nucleotide variants are more common in newer subtypes of muscular dystrophy

  • Patterns of variant type distributions change based on the year of gene discovery

Funding Statement

This work was supported by the National Institutes of Health (NIH) Minnesota Muscle Training Program [T32AR007612].

Footnotes

Declaration of Generative AI: During the preparation of this work, Google Gemini was used to aid in resolving Python errors during the retrieval of data from ClinVar, and to refine language during manuscript drafting and editing. After using this tool, the authors manually reviewed and edited all code and text and take full responsibility for the final contents of the publication.

The datasets and Python scripts used are available on GitHub at https://github.com/umn-hst-kanglab/stenger-2026-md-variant-chronology.

Conflicts of Interest

The authors have no financial conflicts of interest relevant to the contents of the current report.

Declaration of interests

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Trek Stenger reports financial support was provided by National Institutes of Health. Peter B. Kang reports a relationship with Sarepta Therapeutics Inc that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Data Availability

These data were derived from the following resources available in the public domain: ClinVar June 8th, 2026 release, https://www.ncbi.nlm.nih.gov/clinvar/. gnomAD v4.1.1, https://gnomad.broadinstitute.org/

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

Figure S1. Distributions of the proportion of nonsense variants across all analyzed muscular dystrophy-associated genes (n = 64).

NIHMS2212790-supplement-1.pdf (102.5KB, pdf)
2

Figure S2. Distributions of protein size determined by the number of amino acids in all analyzed muscular dystrophy-associated genes (n = 64).

NIHMS2212790-supplement-2.pdf (106.6KB, pdf)

Data Availability Statement

These data were derived from the following resources available in the public domain: ClinVar June 8th, 2026 release, https://www.ncbi.nlm.nih.gov/clinvar/. gnomAD v4.1.1, https://gnomad.broadinstitute.org/

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