Abstract
Skeletal dysplasias are a clinically and genetically heterogeneous group of rare disorders. Studies from large cohorts are essential to provide insights into the disease epidemiology, phenotypic spectrum, and mutational profiles. Here we enumerate additional 248 Indians from 197 families with a skeletal dysplasia, following a similar study earlier. We achieved a clinical-molecular diagnosis in 145 families by targeted analysis in 37 and next generation sequencing (exomes and genomes) in 108 families that resulted in a diagnostic yield of 73.6% (145 of 197 families). We identified 149 causal variants, of which 85 were novel, across 73 genes. Eighty-one distinct monogenic forms of skeletal dysplasia were observed with a high proportion of autosomal recessive skeletal dysplasias (60%, 84 families). We observed consanguinity in 35% of the families. Lysosomal storage diseases with skeletal involvement, FGFR3-related skeletal dysplasia and disorders of bone mineralisation were most frequent in this cohort. We expand the phenotypic and genotypic spectrum of rarely reported conditions (RAB33B, TRIP11, NEPRO, RPL13, COL27A1, PTHR1, EXOC6B, PRKACA, FUZ and RSPRY1) and noted novel gene-disease relationships for PISD, BNIP1, TONSL, CCN2 and SCUBE3 related skeletal dysplasia. We successfully implemented genomic testing for skeletal dysplasia in clinical and research settings. Our study provides valuable information on the spectrum of skeletal dysplasia and disease-causing variants for Asian Indians.
Introduction
Skeletal dysplasias (SKD) are a heterogenous group of genetic conditions resulting from perturbations in skeletal development, growth and maintenance. Traditionally, specific radiographic features were used by the physicians to diagnose them. Eventually, additional approaches utilising biochemical, functional and molecular assays were introduced to further distinguish these disorders. Skeletal dysplasia as a group has evolved both clinically and genetically [1, 2]. The recent nosology of genetic skeletal disorders, has categorised 771 skeletal disorders into 41 groups, associated with 552 causative genes [1]. However, the genetic cause for many disorders is still unknown. Additionally, there are several phenotypes that are yet to be fully characterised. The diagnosis of this group of disorders remains challenging owing to their phenotypic and genotypic heterogeneity, as highlighted in various studies [3, 4]. A precise molecular diagnosis for individuals affected with skeletal diseases significantly contributes to resolving the diagnostic odyssey faced by them and support appropriate care and genetic counselling.
Skeletal dysplasias have an estimated prevalence rate of 1 per 5000 live births [5]. India’s distinct and large population structure, coupled with cultural practices supporting consanguineous marriage has provided a niche for accumulation of deleterious genetic variations [6, 7]. There is limited information on the burden of skeletal dysplasias in our country. According to a study, the incidence of this rare group of diseases is 19.6/10,000 births in the Southern part of India [8].
Clinico-molecular profiles of SKD are documented earlier from India in large cohorts [3, 4, 9]. Several new skeletal dysplasias have been delineated in Indian patients [10–13]. Studying large cohorts and documenting the clinical and molecular findings is paramount for several reasons, including the ability to offer precise molecular diagnosis, implement effective genetic counselling, and improve the medical management of the disease. This also offers an opportunity to train the clinicians and scientists. These studies on underrepresented populations are likely to play a pivotal role in addressing global disparities in healthcare as well. Considering these, we hereby present our experience of evaluating 248 affected individuals from 197 Indian families with a skeletal dysplasia from 21 centres from various states of India (Supplementary Fig. 1).
Methods
Patient recruitment
Individuals diagnosed with or suspected to have a skeletal dysplasia and their families were enrolled in this study during 2015−2024. We had reported our first cohort earlier [3]. The clinical evaluation of the enrolled individuals was performed at Kasturba Medical College, Manipal which received referrals by the local paediatricians and orthopaedic surgeons along with other 20 collaborating centres that provide clinical genetics services (Supplementary Fig. 1). All tests were performed as part of research supported by various funding agencies at Manipal. Detailed medical history, clinical photographs and radiographs of the affected individuals were obtained after receiving written informed consents. This study has the approval from the Institutional Ethics Committee at the Kasturba Medical College and Kasturba Hospital, Manipal (IEC:921/2018; IEC:363/2020, IEC: 430/2013, IEC: 570/2015, IEC: 302/2013).
DNA extraction and Sanger sequencing
The peripheral blood (2−5 ml) was collected from the proband, parents and other family members. The genomic DNA was extracted using QIAmp DNA Blood Mini kit (QIAGEN, Hilden, Germany) following the standard protocols. Targeted genetic testing was performed for the exons and flanking intronic sequences of genes and/or for the specific variants known to cause the phenotype/suspected diagnosis. Sanger sequencing was also performed to validate and segregate candidate variants revealed by next generation sequencing in the proband and family members. Primers were designed using Primer3 software. The resulting PCR products were purified and labelled with BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA). Following sequencing on an 8-capillary ABI 3500 Genetic Analyzer, the obtained results were analyzed.
In this study, we employed a sequential genetic testing approach to optimise resource utilisation and diagnostic yield. Targeted testing was initially performed for mutational hotspots, recurrent mutations, and small genes (FGFR3, IDS, GALNS, COL10A1, CHST3, EBP, DYM, IFITM5, TRPV4, WISP3 and FLNB). Next generation sequencing was undertaken subsequently in these patients as necessary and when the genes were large or clinically evaluation pointed to a genetically heterogeneous condition.
Next generation sequencing
We performed whole exome sequencing (ES) and whole genome sequencing (WGS). The details of capture kits and sequencing platforms used for ES are mentioned in Supplementary Table 1. Exome sequencing mostly achieved an average coverage of 100X, ensuring that 95% of bases covered at a minimum of 20X with a sensitivity of 97%. The raw data were retrieved in fastq format and underwent quality checking through the FastQC (v0.12.1) tool. Subsequently, adaptor removal and quality trimming were performed using the Adaptor Removal (v2.3.2) tool. The reads were mapped to the GRCh38 version of human reference genome using bwamem2 (v0.7.15), followed by sorting and indexing the aligned output using Samtools (v1.6). Secondary analysis was conducted employing an established in-house pipeline based on GATK toolkit v4.2.6.1 adhering to the GATK best practices guidelines [14]. The vcf files generated after variant calling were annotated using ANNOVAR, along with in-house utility scripts. We applied a minor allele frequency cut-off of 0.01%. Copy number variant (CNV) analysis was performed for all the individuals using ExomeDepth and cn.mops.
Whole genome sequencing (WGS) was performed in selected patients when different approaches or exome sequencing (Supplementary Table 2) failed to identify a genetic cause. Genomic DNA was fragmented, followed by library preparation through a shotgun cloning approach to generate templates. These templates were then subjected to DNA sequencing, encompassing the entire genome without predefined intervals as in exome sequencing. Subsequently, the raw sequencing reads were mapped and aligned to the human reference genome. Quality assessment of FASTQ files was conducted via an in-silico pipeline, followed by variant calling and annotation using appropriate databases and software.
Variant prioritisation and pathogenicity assessment
Variant prioritisation and pathogenicity assessment were carried out considering parameters such as variant type, genomic location, impact on protein function, inheritance pattern, allele frequency in control population databases like Genome Aggregation Database (gnomAD), in-house database of approximately 3000 samples and prediction scores of multiple in-silico tools (such as Mutation Taster, CADD_phred, REVEL, M_CAP, SIFT Indel, Splice AI). In whole genome sequencing data analysis, intronic or copy number variants (CNVs) in the genes specific to the clinical diagnosis were prioritised. The allele frequency of the prioritised variants was verified in the gnomAD database, and in-silico prediction tools including Human Splice Finder (HSF), Regulation Spotter, Regulome DB, and ENCODE were used for analyzing the putative effects of the variants on gene and protein function. Furthermore, a clinical analysis was performed for the potential variants and phenotype in consideration (described in HPO terms).
The validation and segregation analysis were performed for prioritised/candidate variants in all families except when parental samples were not available (Supplementary Table 3). The guidelines and criteria issued by the American College of Medical Genetics and Genomics and Association of Molecular Pathologists (ACMG-AMP) were followed for classifying the disease-causing variants [15]. The variants were described according to HGVS nomenclature, using MANE select reference transcript sequence of GRCh38 human genome assembly. The identified variants in this study were submitted to the ClinVar database or Leiden Open Variation Database (LOVD) database (Supplementary Table 3).
Results
Participants
We ascertained 248 affected individuals from 197 unrelated families with a variety of skeletal dysplasia. Among them, 151 families underwent evaluation at the primary study centre at Manipal whereas 46 families were assessed at other centres across India. Of the affected individuals, 148 (59.6%) individuals were males, 100 (40.4%) were females. Consanguinity was noted in 35% (69/197) of the families. The age distribution of the affected individuals in the cohort ranged from foetus to 78 years (Supplementary Table 4).
Targeted analysis based on clinical and/or radiological suspicion
Affected individuals with a specific clinical or radiological diagnosis of skeletal dysplasia underwent targeted genetic testing by Sanger sequencing. In total, 59 individuals from 50 families were tested, resulting in a diagnosis for 37 (74%) families. Targeted testing was performed for disorders such as FGFR3-related skeletal dysplasia; Mucopolysaccharidosis type 2, IDS-related; Mucopolysaccharidosis type 4, GALNS-related (type 4 A) (GALNS); Metaphyseal dysplasia Schmid (MCS), COL10A1-related (COL10A1); CHST3-related spondyloepiphyseal dysplasia (CHST3); Chondrodysplasia punctata, EBP-related; Dyggve-Melchior-Clausen dysplasia, DYM-related (DYM); Osteogenesis imperfecta, progressively deforming (Sillence type 3), IFITM5-related (IFITM5); Metatropic dysplasia, TRPV4-related (TRPV4); Progressive pseudorheumatoid dysplasia (PPRD), WISP3-related (WISP3) and Larsen syndrome, FLNB- related (FLNB). Thirteen families that remained undiagnosed with this approach and five of them underwent singleton exome sequencing subsequently (Fig. 1).
Fig. 1. Flow chart outlining our study design for evaluation of skeletal dysplasia.
The numbers refer to the families that underwent the specific diagnostic test.
Next generation sequencing approach
We used next generation sequencing in 152 families and achieved a diagnosis in 108 of them (71%). Exome sequencing (ES) was performed in 145 families, with singleton in 110, duo (siblings) in 11, and trio (parents-child) in 24 families (Fig. 1). ES yielded a molecular diagnosis in 104 out of 145 families (71.7%), with 92 out of 110 (83.6%) families diagnosed solely through singleton ES. Seven families were diagnosed by duo ES and five families by trio ES. When the ES was non-diagnostic, whole genome sequencing (WGS) was performed in six families while one family underwent WGS after an abnormal karyotype result (Supplementary Table 2) leading to a possible molecular diagnosis in four of them (Fig. 1). The detailed clinical summaries of patients who underwent whole genome sequencing are provided in supplementary information. Intronic variants identified in three of these families (Supplementary Table 5). A total of 149 potential causal variants were observed in the cohort in 145 diagnosed families with a skeletal dysplasia (Supplementary Table 3).
Among the identified variants, 147 out of 149 (98.6%) were single nucleotide variants (SNVs), one was a copy number variant (CNV) and one was an indel. Ninety-six were missense SNVs, 16 splicing alterations, 16 frameshift deletions, eight frameshift insertions, six nonsense, three in-frame-deletions and two 5’ UTR variants, and one stop-loss. We observed a homozygous copy number variant (deletion of ∼120 kb) in EXOC6B [16]. Seventy-five of these variants were classified as pathogenic, and 41 as likely pathogenic, 30 as variants of uncertain significance (VUS) and three variants are in genes of uncertain significance (GUS) as per the ACMG-AMP criteria (our submission records can be accessed from ClinVar and LOVD numbers provided in the Supplementary Table 3 [15]). 85 out of 149 (57%) variants were novel in the study cohort (Supplementary Table 3).
Spectrum of skeletal dysplasia
We observed 81 different monogenic forms of skeletal dysplasia resulting from disease-causing variants in 73 genes in 145 families (Supplementary Table 3). We noted 49 autosomal recessive disorders in 84 families, followed by 29 autosomal dominant disorders in 51 families. Additionally, an X-linked dominant condition (hypophosphatemic rickets, PHEX-related) was observed in six families and two X-linked recessive disorders (mucopoly-saccharidosis type 2, IDS-related, and spondyloepiphyseal dysplasia, TRAPPC2 related) were observed in six and four families respectively. Three families were diagnosed with atypical/unreported Mendelian inheritance patterns: COL2A1-related spondyloepimetaphyseal dysplasia (autosomal recessive inheritance) [17], and IDH1-related enchondromatosis (due to somatic mosaicism), and two brothers affected with pseudoachondroplasia (due to gonadal mosaicism) (Supplementary Fig. 2). Moreover, we observed biotinidase deficiency in two siblings (one brother and one sister) with pseudoachondroplasia [co-occurrence of a known heterozygous variant c.950 A > G in COMP (NM_000095.3) and homozygous variant c.641 C > T (p.Thr214Ile) in exon 4 of BTD gene (NM_001370658.1)].
We identified five novel gene-disease associations during this study period: PISD-related spondylo-epi-metaphyseal dysplasia [12], BNIP1-related spondylo-epiphyseal dysplasia [10], and SCUBE3-related short stature, facial dysmorphism, and skeletal anomalies with or without cardiac anomalies [11]. The genetic aetiology of SPONASTRIME dysplasia was previously unknown. We partnered in identifying hypomorphic variants resulting in reduced function of TONSL in five families with SPONASTRIME dysplasia, thereby establishing its molecular basis [18]. Kyphomelic dysplasia, characterised by severe bowing of the limbs, primarily of the femora, for which the molecular basis remained elusive for a long time, was found to result from biallelic loss-of-function variants in the CCN2 gene in one family with two affected siblings during this study [19]. These diagnoses are grouped and listed in Supplementary Table 3 as per the 2023th revision of the Nosology of Genetic Skeletal Disorders.
Several rare skeletal disorders were further characterised both phenotypically and genotypically. RSPRY1-related spondyloepime-taphyseal dysplasia in two sisters had elbow joint dislocation as a novel clinical feature [20]. We also enumerated the role of FUZ in a skeletal ciliopathy akin to orofaciodigital syndrome [21]. Four individuals from two families with spondyloepimetaphyseal dysplasia, RPL13-related were phenotypically and genetically delineated [22]. We demonstrated that individuals with spondy-loepimetaphyseal dysplasia with joint laxity, EXOC6B-reletad, might exhibit intellectual disability and structural brain malformations [23]. Cleft palate and delayed carpal bone ossification were described as novel findings in two families with Steel syndrome [24]. In two families, loss of function variants in deep intronic regions of TRIP11 led to achondrogenesis 1 A [25]. The identification of biallelic variants in NEPRO has expanded the spectrum of ribosomopathies [26]. Association of RAB33B with Smith-McCort (SMC) dysplasia has been reported in only two families previously. Describing three additional patients strengthened this genedisease relationship [27]. Two additional families with the ultrarare Eiken syndrome, previously reported in only one family caused by PTHR1, were further characterised [28, 29].
Discussion
Here we describe an Indian cohort of 197 families with a genetic skeletal disorder, where we obtained a molecular diagnosis in 145 families. We used clinical, radiographic and molecular approaches (targeted analysis and next generation sequencing) that resulted in a diagnosis in 73.6% of them. We identified 81 genetic skeletal disorders that include both rare and well-known skeletal dysplasias. These disorders are distributed in 35 (out of 41) groups of SKD listed in the recent Nosology of genetic skeletal disorders [1] (Supplementary table 3).
Most frequently observed disorders in the present study belong to the following groups: lysosomal storage diseases with skeletal involvement followed by FGFR3 chondrodysplasias and disorders of bone mineralisation, accounting for 46 families (32%) in the cohort (Supplementary Table 3). In a similar study by Li et al. on a Chinese cohort, the most prevalent categories were (osteogenesis imperfecta and bone fragility group), (disorders of bone mineralisation), and (osteosclerotic disorders), collectively constituting 71% of the total diagnosed population [30]. Scocchia et al., found type 2 collagen disorders as most prevalent, followed by FGFR3 chondrodysplasias and osteogenesis imperfecta and bone fragility group, encompassing 33% of all diagnosed cases [31]. In a Turkish cohort comprising 417 patients with skeletal dysplasia, FGFR3 chondrodysplasias (achondroplasia), and osteogenesis imperfecta and bone fragility group were the two most prominent groups [32]. Similar findings have been reported in a few studies from India earlier. Nampoothiri et al. reported that dysostoses multiplex group was the most common Nosology group, followed by FGFR3 chondrodysplasias, osteogenesis imperfecta and bone fragility group [4]. In our previous study, lysosomal storage diseases with skeletal involvement, genetic inflammatory or rheumatoid-like osteoarthropathies groups were the most common groups [3]. However, FGFR3 chondrodysplasias, lysosomal storage diseases with skeletal involvement and osteogenesis imperfecta and bone fragility group are found to be more prevalent among all the studies (Supplementary Fig. 3). It should be noted that, over-representation of certain disorders is possible due to a selection bias. For example, some centres serve as nodal points or regional centres for treatment of osteogenesis imperfecta, others provide enzyme replacement therapy for mucopolysaccharidoses and a few have skilled orthopaedists caring for skeletal dysplasia. Overall, most of the afore-mentioned diseases are severe, easily recognisable and treatable (osteogenesis imperfecta and mucopolysaccharidosis).
Appropriate clinical work-up allows targeted sequencing as a testing strategy considering its cost-effectiveness, accuracy and rapid turnaround times [3, 4, 30]. From thirteen remaining undiagnosed families, five families underwent singleton exome sequencing. In eight other families additional testing was not opted due to either the likelihood of non-genetic cause or financial constraint in resource-limited settings at that particular time.
The molecular diagnosis of skeletal dysplasias has improved significantly through the advent of next-generation sequencing techniques, which is illustrated in the present version of the Nosology (11th version of nosology) which comprises 771 entries associated with 552 genes. The 10th version of the Nosology [33] comprised 461 distinct diseases, among which 425 SKDs had a molecular aetiology (92%). This advancement has enabled the identification of causative genes in 108 families (108/145), 74.5% of the present cohort.
The diagnostic yield of this study is comparatively higher than in other studies: Scocchia et al., 2021 (42%); Lv et al., 2021 (49%); Bae et al., 2016 (52%); Zhang et al., 2015 (54%); Retterer et al., 2016 (39%) [31, 34–37]. Other studies, such as those conducted by Silveira et al. with a diagnostic yield of 77% [38], and Hammarsjö et al. reporting a remarkable 90% diagnostic yield, have demonstrated significantly improved outcomes through the application of next-generation sequencing techniques. Hammarsjö et al. studied a group of individuals affected with skeletal ciliopathies, which resulted in a notably higher rate of molecular diagnosis [39]. Furthermore, Maddirevula et al. presented findings from a large cohort involving 411 affected individuals from 288 families. In this study, all individuals received a molecular diagnosis facilitated by the use of panel-based targeted exome sequencing and exome sequencing [40].
We performed WGS in patients with non-diagnostic exomes. WGS helped in the diagnosis of 4/7 families (Supplementary Tables 2 and 5). Three are deep intronic variants in the genes causing Mucopolysaccharidosis, type IVA (OMIM# 253000), Acromesomelic dysplasia type 3 (OMIM# 609441) and Odontochondrodysplasia 1 (OMIM# 184260). However, we would require further experiments to study their impact on splicing to definitely establish a causal role (Supplementary Table 5). In family F30, with clinical features similar to Ellis-van Creveld syndrome (EVC), we had failed to consider the exonic variant in PRKACA earlier in the exome data as the gene-disease association was not established by then [41]. Three families still did not receive a diagnosis even after WGS (F26 and F135: no candidate variants, F135: we are inexperienced in analysis of the breakpoints for a balanced translocation). Thus, we are still in the learning curve for WGS and believe good clinical handles are likely to help in detecting deep intronic variants.
In the present study, we identified 85 (54.5%) novel variants, a higher proportion compared to previous studies with different ethnicities such as 41% in the study of Chinese cohort by Lv et al. [34] and 24% in study of Arab cohort conducted by Maddirevula et al [40]. There are few studies representing the Indian population. In a study by Kulkarni et al, 169 cases are described in which the diagnosis was made in very few cases through recognisable clinical features via ultrasound [8]. The study by Nampoothiri et al., reports a molecular diagnosis in 21.2% of the 514 evaluated affected individuals [4]. Our first series of skeletal dysplasia cohorts by Uttarilli et al. also contributed to the understanding of the genetic landscape, reporting on 508 affected Indian families with confirmed molecular diagnoses [3]. However, numerous case series focusing on specific skeletal dysplasia conditions, along with case reports, studies illustrating phenotypic expansion and the identification of novel gene-disease associations have been published from India highlighting the diverse spectrum of skeletal dysplasia [16, 17, 22, 26, 27, 42–46].
The centuries-old practice of reproductive isolation including endogamy and consanguinity contributes to increased homozygosity and introduces a shift in allele frequency. The genetic consequences of founder effect and bottleneck effect are particularly evident in the prevalence of rare recessive genetic disorders in India [3, 6]. Overall consanguinity rate reported is about 11.6% in India [47]. It is higher in the Southern states of India constituting 23% [3]. In the present study it is noted to be 35% potentially contributing to a slight increase in the number of autosomal recessive disorders, accounting for 49 monogenic disorders (60%).
Our study has selection bias. It is important to acknowledge that this cohort may not completely represent the entire spectrum of skeletal dysplasia disorders in India or even in our region. A reason for lower number of patients with osteogenesis is that we had published our osteogenesis imperfecta cohort earlier [48, 49]. We describe the families who underwent genetic testing, it is noteworthy that the participants were recruited as a research activity at Manipal, from various centres across country (Supplementary Fig. 1), with no associated costs for the patients. This funding model may limit the generalisability of our findings. Some of them who did not consent for testing (clinical or research) are not enumerated here. A few patients with common skeletal dysplasias (achondroplasia) might have missed the attention of our team due to referral bias. Additionally, trio exome sequencing, and/or genome sequencing, could not be conducted for some individuals who had non-diagnostic results through targeted genetic testing or singleton-exome sequencing due to resource limitations. Moreover, some of the findings of this study had been published previously [10–12, 18–22, 41, 42, 50].
Web resources and online tools
PRIMER 3 v.4.1.0, http://primer3.ut.ee/
Ensembl, https://asia.ensembl.org/index.html
NCBI, https://www.ncbi.nlm.nih.gov/
bwa-mem2, https://github.com/bwa-mem2/bwa-mem2
Samtools, https://www.htslib.org/
Adaptor removal, https://github.com/MikkelSchubert/adapterremoval
Genome Analysis Toolkit (GATK), https://www.broadinstitute.org/scientific-community/software/genome-analysis-toolkit-gatk
ANNOVAR, https://annovar.openbioinformatics.org/en/latest/
ExomeDepth, https://github.com/vplagnol/ExomeDepthCn.MOPS, https://github.com/KarlaLG91/mk-cn.MOPS_Mutation_Taster, http://www.mutationtaster.org/CADD_Phred, https://cadd.gs.washington.edu/
M_CAP, http://bejerano.stanford.edu/mcap/
REVEL, https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=revel
Splice AI, https://spliceailookup.broadinstitute.org/
SIFT indel, https://sift.bii.a-star.edu.sg/www/SIFT_indels2.html
Fast QC, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Online Mendelian Inheritance in Man (OMIM): https://www.omim.org/
LOVD, https://www.lovd.nl/
Regulation spotter: https://www.regulationspotter.org/ ENCODE: https://www.encodeproject.org/
Human Splice Finder: https://www.genomnis.com/access-hsf Regulome DB: https://regulomedb.org/regulome-search/
Supplementary Material
Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41431-024-01776-8.
Acknowledgements
We would like to thank the participants and their families for their consent and participation in the study. We are grateful to all the referring physicians who made this work possible. We thank the “SG10K_Pilot Investigators” for providing the SG10K_Pilot data (EGAD00001005337). The data from the “SG10K_Pilot Study” reported here were obtained from EGA. This manuscript was not prepared in collaboration with the “SG10K_Pilot Study” and does not necessarily reflect the opinions or views of the “SG10K_Pilot Study”. Additionally, we acknowledge that Figure 1 was created using BioRender.
Funding
This work was supported by the following research projects awarded to Katta M Girisha: Department of Biotechnology/Wellcome Trust India Alliance project titled “Centre for Rare Disease Diagnosis, Research and Training” (Reference number: IA/CRC/20/1/600002), Department of Science and Technology, Government of India project entitled ‘Application of Autozygosity Mapping and Exome Sequencing to Identify Genetic Basis of Disorders of Skeletal Development’ (SB/SO/HS/005/2014), and Indian Council of Medical Research project entitled “Clinical and molecular evaluation of inherited arthropathies and multiple vertebral segmentation defects” (Project ID: BMS 54/2/2013). Ashwin Dalal is supported by the Department of Biotechnology, Government of India project entitled ‘Development of Genomic Technologies for Predictive Genetic Health and Forensic Profiling’ (Grant No. BTI/AAQ/01/CDFD-Flagship/2019). Swati Singh is supported by Joint CSIR-UGC NET Junior Research Fellowship awarded by Human Resource Development Group under Council of Scientific and Industrial Research (CSIR), Government of India: (08/028(0002)/2019-EMR-I).
Footnotes
Author Contributions
Conceptualisation: KMG, HS, GSB; Data curation: PJ, SS, GSB; Formal analysis: PJ, SS, GSB, KMG; Funding acquisition: KMG, AD; Investigation: PJ, SS GSB, GN, GM, HS, KMG; Resources: KG, DLN, SN, SJP, JPS, MM, SK, BD, BSB, VB, SB, AB, MM, SVH, NK., RDS, DS, AS, SRP; Supervision: GSB, HS, KMG; Writing original draft: PJ, SS, KMG; Writing final draft: PJ, SS, GSB, KG, DLN, SN, SJP, JPS, MM, SK, BD, BVB, SB, AB, MM, SVH, NK, RDS, DS, AS, AD, SRP, GN, GM, HS, KMG All authors have read and approved the final version of the manuscript.
Competing Interests
KMG is the director of Suma Genomics Private Limited and holds shares of the company that has interests in genetic testing.
Ethical Approval
We obtained the informed consents from the families for genetic testing, publication of data and clinical photographs. This study has the approvals from the Institutional Ethics Committee at the Kasturba Medical College and Kasturba Hospital, Manipal (IEC:921/2018; IEC:363/2020, IEC: 430/2013, IEC: 570/2015, IEC: 302/2013).
Reprints and permission information is available at http://www.nature.com/reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Data Availability
Most of the data relevant to this study are included here. However, any additional information about the study is available from the corresponding authors upon reasonable request.
References
- 1.Unger S, Ferreira CR, Mortier GR, Ali H, Bertola DR, Calder A, et al. Nosology of genetic skeletal disorders: 2023 revision. Am J Med Genet A. 2023;191:1164–1209. doi: 10.1002/ajmg.a.63132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Spranger J. Pattern recognition in bone dysplasias. Prog Clin Biol Res. 1985;200:315–42. [PubMed] [Google Scholar]
- 3.Uttarilli A, Shah H, Bhavani GS, Upadhyai P, Shukla A, Girisha KM. Phenotyping and genotyping of skeletal dysplasias: evolution of a center and a decade of experience in India. Bone. 2019;120:204–11. doi: 10.1016/j.bone.2018.10.026. [DOI] [PubMed] [Google Scholar]
- 4.Nampoothiri S, Yesodharan D, Sainulabdin G, Narayanan D, Padmanabhan L, Girisha KM, et al. Eight years experience from a skeletal dysplasia referral center in a tertiary hospital in Southern India: a model for the diagnosis and treatment of rare diseases in a developing country. Am J Med Genet A. 2014;164A(9):2317–23. doi: 10.1002/ajmg.a.36668. [DOI] [PubMed] [Google Scholar]
- 5.Krakow D, Rimoin DL. The skeletal dysplasias. Genet Med. 2010;12(6):327–41. doi: 10.1097/GIM.0b013e3181daae9b. [DOI] [PubMed] [Google Scholar]
- 6.Nakatsuka N, Moorjani P, Rai N, Sarkar B, Tandon A, Patterson N, et al. The promise of discovering population-specific disease-associated genes in South Asia. Nat Genet. 2017;49(9):1403–7. doi: 10.1038/ng.3917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bhasin MK, Nag S. Consanguinity and its effects on fertility, mortality and morbidity in the Indian region: a review. Int J Human Genet. 2012;12(4):197–301. [Google Scholar]
- 8.Kulkarni ML, Samuel K, Bhagyavathi M, Sureshkumar C. Skeletal dysplasias in a hospital in southern India. Ind Pediatr. 1995;32(6):657–65. [PubMed] [Google Scholar]
- 9.Sheth J, Bhavsar R, Mistri M, Pancholi D, Bavdekar A, Dalal A, et al. Gaucher disease: single gene molecular characterization of one-hundred Indian patients reveals novel variants and the most prevalent mutation. BMC Med Genet. 2019;20(1):31. doi: 10.1186/s12881-019-0759-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Holling T, von Bhavani GS, Elsner L, Shah H, Kausthubham N, Bhattacharyya SS, et al. A homozygous hypomorphic BNIP1 variant causes an increase in autophagosomes and reduced autophagic flux and results in a spondylo-epiphyseal dysplasia. Hum Mutat. 2022;43(5):625–42. doi: 10.1002/humu.24368. [DOI] [PubMed] [Google Scholar]
- 11.Lin YC, Niceta M, Muto V, Vona B, Pagnamenta AT, Maroofian R, et al. SCUBE3 loss-of-function causes a recognizable recessive developmental disorder due to defective bone morphogenetic protein signaling. Am J Hum Genet. 2021;108(1):115–33. doi: 10.1016/j.ajhg.2020.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Girisha KM, von Elsner L, Neethukrishna K, Muranjan M, Shukla A, Bhavani GS, et al. The homozygous variant c.797G>A/p.(Cys266Tyr) in PISD is associated with a Spondyloepimetaphyseal dysplasia with large epiphyses and disturbed mitochondrial function. Hum Mutat. 2019;40(3):299–309. doi: 10.1002/humu.23693. [DOI] [PubMed] [Google Scholar]
- 13.Girisha KM, Pande S, Dalal A, Phadke SR. Untapped opportunities for rare disease gene discovery in India. Am J Med Genet A. 2020;182(12):3056–9. doi: 10.1002/ajmg.a.61866. [DOI] [PubMed] [Google Scholar]
- 14.Kausthubham N, Shukla A, Gupta N, Bhavani GS, Kulshrestha S, Das Bhowmik A, et al. A dataset of variants derived from 1455 clinical and research exomes is efficient in variant prioritization for early-onset monogenic disorders in Indians. Hum Mutat. 2021;42(4):e15–61. doi: 10.1002/humu.24172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. doi: 10.1038/gim.2015.30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Girisha KM, Kortüm F, Shah H, Alawi M, Dalal A, Bhavani GS, et al. A novel multiple joint dislocation syndrome associated with a homozygous nonsense variant in the EXOC6B gene. Eur J Human Genet. 2016;24(8):1206–10. doi: 10.1038/ejhg.2015.261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Girisha KM, Bhavani GS, Shah H, Moirangthem A, Shukla A, Kim OH, et al. Biallelic variants p.Arg1133Cys and p.Arg1379Cys in COL2A1: Further delineation of phenotypic spectrum of recessive Type 2 collagenopathies. Am J Med Genet A. 2020;182(2):338–47. doi: 10.1002/ajmg.a.61414. [DOI] [PubMed] [Google Scholar]
- 18.Chang HR, Cho SY, Lee JH, Lee E, Seo J, Lee HR, et al. Hypomorphic mutations in TONSL Cause SPONASTRIME dysplasia. Am J Hum Genet. 2019;104(3):439–53. doi: 10.1016/j.ajhg.2019.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Singh S, Danda S, Sharma N, Shah H, Madhuri V, Mir TA, et al. Biallelic variants in CCN2 underlie an autosomal recessive kyphomelic dysplasia. Eur J Hum Genet. 2024 Nov 6; doi: 10.1038/s41431-024-01725-5. Published online. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Singh S, Shah H, Dalal A, Shukla A, Bhavani GS, Girisha KM. Two sisters with RSPRY1-related spondyloepimetaphyseal dysplasia. Am J Med Genet A. 2024;2:e63601. doi: 10.1002/ajmg.a.63601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Singh S, Nampoothiri S, Narayanan DL, Chaudhry C, Salvankar S, Girisha KM. Biallelic loss of function variants in FUZ result in an orofaciodigital syndrome. Eur J Hum Genet. 2024;32:1022–1026. doi: 10.1038/s41431-024-01619-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Jacob P, Lindelöf H, Rustad CF, Sutton VR, Moosa S, Udupa P, et al. Clinical, genetic and structural delineation of RPL13-related spondyloepimetaphyseal dysplasia suggest extra-ribosomal functions of eL13. NPJ Genom Med. 2023;8(1):39. doi: 10.1038/s41525-023-00380-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Simsek-Kiper PO, Jacob P, Upadhyai P, Taşkıran ZE, Guleria VS, Karaosmanoglu B, et al. Biallelic loss-of-function variants in EXOC6B are associated with impaired primary ciliogenesis and cause spondylo-epi-metaphyseal dysplasia with joint laxity type 3. Hum Mutat. 2022;43(12):2116–29. doi: 10.1002/humu.24478. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Girisha KM, Jacob P, SriLakshmi Bhavani G, Shah H, Mortier GR. Steel syndrome: Report of three patients, including monozygotic twins and review of clinical and mutation profiles. Eur J Med Genet. 2022;65(6):104521. doi: 10.1016/j.ejmg.2022.104521. [DOI] [PubMed] [Google Scholar]
- 25.Upadhyai P, Radhakrishnan P, Guleria VS, Kausthubham N, Nayak SS, Superti-Furga A, et al. Biallelic deep intronic variant c.5457+81T>A in TRIP11 causes loss of function and results in achondrogenesis 1A. Hum Mutat. 2021;42(8):1005–14. doi: 10.1002/humu.24235. [DOI] [PubMed] [Google Scholar]
- 26.Narayanan DL, Shukla A, Kausthubham N, Bhavani GS, Shah H, Mortier G, et al. An emerging ribosomopathy affecting the skeleton due to biallelic variations in NEPRO. Am J Med Genet A. 2019;179(9):1709–17. doi: 10.1002/ajmg.a.61267. [DOI] [PubMed] [Google Scholar]
- 27.Salian S, Cho TJ, Phadke SR, Gowrishankar K, Bhavani GS, Shukla A, et al. Additional three patients with Smith-McCort dysplasia due to novel RAB33B mutations. Am J Med Genet A. 2017;173(3):588–95. doi: 10.1002/ajmg.a.38064. [DOI] [PubMed] [Google Scholar]
- 28.Moirangthem A, Narayanan DL, Jacob P, Nishimura G, Mortier G, Girisha KM. Report of second case and clinical and molecular characterization of Eiken syndrome. Clin Genet. 2018;94(5):457–60. doi: 10.1111/cge.13413. [DOI] [PubMed] [Google Scholar]
- 29.Jacob P, Soni JP, Mortier G, Girisha KM. The third family with Eiken syndrome. Clin Genet. 2019;96(4):378–9. doi: 10.1111/cge.13601. [DOI] [PubMed] [Google Scholar]
- 30.Li S, Lyu S, Fu W, Hu Y, Yue H, Chen L, et al. Clinical and genetic profiles of 985 Chinese families with skeletal dysplasia. Chin Med J. 2023;136(12):1485–7. doi: 10.1097/CM9.0000000000002654. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Scocchia A, Kangas-Kontio T, Irving M, Hero M, Saarinen I, Pelttari L, et al. Diagnostic utility of next-generation sequencing-based panel testing in 543 patients with suspected skeletal dysplasia. Orphanet J Rare Dis. 2021;16(1):412. doi: 10.1186/s13023-021-02025-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kurt-Sukur ED, Simsek-Kiper PO, Utine GE, Boduroglu K, Alanay Y. Experience of a skeletal dysplasia registry in Turkey: a five-years retrospective analysis. Am J Med Genet A. 2015;167A(9):2065–74. doi: 10.1002/ajmg.a.37122. [DOI] [PubMed] [Google Scholar]
- 33.Mortier GR, Cohn DH, Cormier-Daire V, Hall C, Krakow D, Mundlos S, et al. Nosology and classification of genetic skeletal disorders: 2019 revision. Am J Med Genet. 2019;179(12):2393–419. doi: 10.1002/ajmg.a.61366. [DOI] [PubMed] [Google Scholar]
- 34.Lv S, Zhao J, Xi L, Lin X, Wang C, Yue H, et al. Genetics evaluation of targeted exome sequencing in 223 Chinese probands with genetic skeletal dysplasias. Front Cell Dev Biol. 2021;9:715042. doi: 10.3389/fcell.2021.715042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Bae JS, Kim NKD, Lee C, Kim SC, Lee HR, Song HR, et al. Comprehensive genetic exploration of skeletal dysplasia using targeted exome sequencing. Genet Med. 2016;18(6):563–9. doi: 10.1038/gim.2015.129. [DOI] [PubMed] [Google Scholar]
- 36.Zhang H, Yang R, Wang Y, Ye J, Han L, Qiu W, et al. A pilot study of gene testing of genetic bone dysplasia using targeted next-generation sequencing. J Hum Genet. 2015;60(12):769–76. doi: 10.1038/jhg.2015.112. [DOI] [PubMed] [Google Scholar]
- 37.Retterer K, Juusola J, Cho MT, Vitazka P, Millan F, Gibellini F, et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med. 2016;18(7):696–704. doi: 10.1038/gim.2015.148. [DOI] [PubMed] [Google Scholar]
- 38.Silveira KC, Kanazawa TY, Silveira C, Lacarrubba-Flores MDJ, Carvalho BS, Cavalcanti DP. Molecular diagnosis in a cohort of 114 patients with rare skeletal dysplasias. Am J Med Genet C Semin Med Genet. 2021;187(3):396–408. doi: 10.1002/ajmg.c.31937. [DOI] [PubMed] [Google Scholar]
- 39.Hammarsjö A, Pettersson M, Chitayat D, Handa A, Anderlid BM, Bartocci M, et al. High diagnostic yield in skeletal ciliopathies using massively parallel genome sequencing, structural variant screening and RNA analyses. J Hum Genet. 2021;66(10):995–1008. doi: 10.1038/s10038-021-00925-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Maddirevula S, Alsahli S, Alhabeeb L, Patel N, Alzahrani F, Shamseldin HE, et al. Expanding the phenome and variome of skeletal dysplasia. Genet Med. 2018;20(12):1609–16. doi: 10.1038/gim.2018.50. [DOI] [PubMed] [Google Scholar]
- 41.Sithambaram S, Jacob P, Neethukrishna K, Bhavani GS, Dalal A, Shah H, et al. PRKACA-related, atrial defects-polydactyly-multiple congenital malformation syndrome in an Indian patient. Am J Med Genet A. 2024;194:e63566. doi: 10.1002/ajmg.a.63566. [DOI] [PubMed] [Google Scholar]
- 42.Singh S, Jacob P, Patil SJ, Muranjan M, Shah H, Girisha KM, et al. Indian patients with CHST3-related chondrodysplasia with congenital joint dislocations. Am J Med Genet A. 2024;194(3):e63422. doi: 10.1002/ajmg.a.63422. [DOI] [PubMed] [Google Scholar]
- 43.Bhavani GS, Shah H, Shukla A, Gupta N, Gowrishankar K, Rao AP, et al. Clinical and mutation profile of multicentric osteolysis nodulosis and arthropathy. Am J Med Genet Part A. 2016;170(2):410–7. doi: 10.1002/ajmg.a.37447. [DOI] [PubMed] [Google Scholar]
- 44.Jacob P, Bhavani GSL, Shah H, Galada C, Nampoothiri S, Kamath N, et al. Pseudoachondroplasia: phenotype and genotype in 11 Indian patients. Am J Med Genet A. 2022;188(3):751–9. doi: 10.1002/ajmg.a.62566. [DOI] [PubMed] [Google Scholar]
- 45.Bhavani GS, Shah H, Dalal AB, Shukla A, Danda S, Aggarwal S, et al. Novel and recurrent mutations in WISP3 and an atypical phenotype. Am J Med Genet A. 2015;167A(10):2481–4. doi: 10.1002/ajmg.a.37164. [DOI] [PubMed] [Google Scholar]
- 46.Bidchol AM, Dalal A, Shah H, Nampoothiri S, Kabra M, et al. GALNS mutations in Indian patients with mucopolysaccharidosis IVA. Am J Med Genet A. 2014;164A(11):2793–801. doi: 10.1002/ajmg.a.36735. [DOI] [PubMed] [Google Scholar]
- 47.Bittles AH. Endogamy, consanguinity and community genetics. J Genet. 2002;81(3):91–8. doi: 10.1007/BF02715905. [DOI] [PubMed] [Google Scholar]
- 48.Mrosk J, Bhavani GS, Shah H, Hecht J, Krüger U, Shukla A, et al. Diagnostic strategies and genotype-phenotype correlation in a large Indian cohort of osteogenesis imperfecta. Bone. 2018;110:368–77. doi: 10.1016/j.bone.2018.02.029. [DOI] [PubMed] [Google Scholar]
- 49.Stephen J, Shukla A, Dalal A, Girisha KM, Shah H, Gupta N, et al. Mutation spectrum of COL1A1 and COL1A2 genes in Indian patients with osteogenesis imperfecta. Am J Med Genet Part A. 2014;164(6):1482–9. doi: 10.1002/ajmg.a.36481. [DOI] [PubMed] [Google Scholar]
- 50.Mamadapur M, Mahadevan S, ArulRajamurugan PS, Gandham S, Singh S. Multi-centric Osteolysis Nodulosis and Arthropathy (MONA): A Case Series and Review of the Literature. Mediterr J Rheumatol. 2024;35:486–9. doi: 10.31138/mjr.311203.mon. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Most of the data relevant to this study are included here. However, any additional information about the study is available from the corresponding authors upon reasonable request.