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DNA methylation profiling and genomic analysis in 20 children with short

stature who were born small-for-gestational age

1 2 3 1 Silke Peeters, MSc , Ken Declerck, PhD , Muriel Thomas, MD , Eveline Boudin, PhD , Dominique Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 Beckers, MD4, Olimpia Chivu, MD5, Claudine Heinrichs, MD, PhD6, Koenraad Devriendt, MD, PhD7, Francis de Zegher, MD, PhD8, Wim Van Hul, PhD1, Wim Vanden Berghe, PhD2, Jean De Schepper, MD9, Raoul Rooman, MD, PhD10, Geert Mortier, MD, PhD1 on behalf of the WES-BESPEED study group°.

1Department of Medical Genetics, University of Antwerp and Antwerp University Hospital, Antwerp,

Belgium

2Laboratory of Protein Chemistry, Proteomics and Epigenetic Signalling (PPES), Department of

Biomedical Sciences, University of Antwerp, Antwerp, Belgium

3Belgian Society for Pediatric Endocrinology and Diabetology, Brussels, Belgium

4Unité d'Endocrinologie Pédiatrique, CHU NAMUR, Université catholique de Louvain, 5530 Yvoir,

Belgium and Department of Pediatrics, University Hospital Gasthuisberg, 3000, Leuven, Belgium

5Department of Pediatrics, Clinique de l'Espérance, Saint-Nicolas, Belgium

6Paediatric Endocrinology Unit, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Brussels,

Belgium

7Center for Human Genetics, University hospital Leuven, University of Leuven, Leuven, Belgium

8Department of Development & Regeneration, University of Leuven, Leuven, Belgium

9Department of Pediatrics, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium and DepaAcceptedrtment of Pediatrics, Universitair Ziek enhManuscriptuis Gent, Ghent, Belgium 10PendoCon bvba, Putte, Belgium

°Additional collaborators of the WES-BESPEED study group include Dotremont Hilde, MD, Department of Pediatrics, Antwerp University Hospital, Antwerp Belgium; Craen Margareta, MD, Department of Pediatrics, University of Ghent, Ghent, Belgium; Gies Inge, MD/PhD, Department of Pediatrics, UZ

© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: [email protected]. jc.2020-00271. See https://academic.oup.com/endocrinesociety/pages/Author_Guidelines for Accepted Manuscript disclaimer and additional information.

Brussel, Brussels Belgium; Lebrethon, Marie-Christine, MD, Department of Pediatrics, University of Liège, Liège, Belgium.

Corresponding author:

Name: Mortier Geert Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020

Address: Department of Medical Genetics

Prins Boudewijnlaan 43

2650 Edegem

Belgium

TEL: +32 3275 9774

E-mail address: [email protected]

Grant support

Supported by an unrestricted grant (53232139) from Pfizer, the Research Fund of the University of

Antwerp (Methusalem-OEC grant – “GENOMED”; FFB190208), a predoctoral grant from the University of Antwerp (to S Peeters) and a postdoctoral grant from the Research Foundation-Flanders

(12A3814N) (to E Boudin).

Disclosure Statement

RR has received consulting fees or grant support from Pfizer, Ferring, Sandoz and Ipsen. JDS has received consulting fees or grant support from Novo-Nordisk, Pfizer, Eli-Lilly, Ferring, Sandoz and Ipsen.

DB receivedAccepted travel fees from Novo Nordisk, Pfizer Manuscript and Ferring and conference fees from Merck. All other authors declare no conflict of interest. Although part of the study was supported by a grant from

Pfizer, decisions about the design of the study, collection and analysis of data or decision to publish were made independently.

Abstract

Purpose: In a significant proportion of children born small-for-gestational age (SGA) with failure of catch-up growth, the etiology of short stature remains unclear after routine diagnostic work-up. We Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 wanted to investigate if extensive analysis of the (epi)genome can unravel the cause of growth failure in a significant portion of these children.

Patients and Methods: Twenty SGA children treated with growth hormone (GH) because of short stature were selected from the BELGROW database of the Belgian Society for Pediatric Endocrinology and Diabetology for exome sequencing, SNP array and genome-wide methylation analysis to identify the (epi)genetic cause. First year response to GH was compared to the response of SGA patients in the

KIGS database.

Results: We identified (likely) pathogenic variants in 4 children (from 3 families) using exome sequencing and found pathogenic CNV in 2 probands using SNP array. In a child harboring a NSD1- containing microduplication, we identified a DNA methylation signature that is opposite to the genome-wide DNA methylation signature of Sotos syndrome. Moreover, we observed multi-locus imprinting disturbances in two children in whom no other genomic alteration could be identified. Five out of 6 children with a genetic diagnosis had an "above average" response to GH.

Conclusions: The study indicates that a more advanced approach with deep genotyping can unravel unexpected (epi)genomic alterations in SGA children with persistent growth failure. Most SGA children with a Acceptedgenetic diagnosis had a good response to GHManuscript treatment.

Key words: small for gestational age; short stature; growth hormone; DNA methylation; NSD1

Introduction

About 20% of children with short stature are born small for gestational age (SGA)1. SGA can be defined as a birth weight and/or birth length equal or less than -2 SD for sex and gestational age2. Most of the Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 SGA children catch up on weight and height during the first two years of their life3. However, approximately 10% of the children fail to catch up on growth and remain short in adult life1. Genetic association studies with candidate genes identified several common single nucleotide polymorphisms

(SNP) associated with persistent short stature in SGA children4. Moreover, also rare single nucleotide variants (SNV), copy number variants (CNV) and larger chromosomal abnormalities have been shown to cause prenatal onset (syndromic) short stature5-7. Other causes of growth deficits are related to genomic imprinting8. Epigenetic mechanisms, including DNA methylation, control imprinting and, if disturbed, may result in imprinting associated disorders. These disorders usually affect fetal growth, metabolism and development.

Although several genetic and epigenetic factors have been identified, the etiology of short stature remains unclear in a significant proportion of children born SGA5,9,10. The multitude of different disorders with sometimes variable, complex and overlapping phenotypes often complicates a rapid and accurate genetic diagnosis11,12. Nevertheless, the elucidation of the exact cause of growth failure is important for proper treatment, management and eventually genetic counseling11.

In this study, we investigated a well-defined group of 20 SGA children with persistent short stature by applying an advanced (epi)genetic approach to unravel the underlying cause.

MaterialAccepteds and methods Manuscript

Patient recruitment

BELGROW, the Belgian database of children treated with growth hormone (GH), was searched for children born SGA and with short stature at the start of GH treatment. Other inclusion criteria were:

(1) both biological parents are alive (2) bone age at the start of GH treatment is within 1.5 year of the

calendar age; (3) Height SDS at the start of GH treatment is at least 1.5 SD below the height SDS of each parent; (4) Still prepubertal during the first year of GH treatment to allow a reliable assessment of the first year growth response to GH administration. Exclusion criteria were: (1) a known bone dysplasia or sitting height/total height > 2SDS; (2) evidence for fetotoxic factors that explained the Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 intra-uterine growth retardation (e.g. tobacco or alcohol abuse) or another known factor that explained the growth deficit or that modulated the response to GH therapy such as a chronic disease or chronic medication. Birth weight and length were calculated using the Niklasson references13.

Height and weight SDS were calculated using the 2004 Flemish growth references14. Out of 65 eligible patients, 20 participated in the study. Reasons for non-participation included: one parent not available

(died, in prison, moved to another country, …), patient had moved and address was unknown, incorrect registry data that when corrected turned the patient no longer eligible, genetic diagnosis reached but not available in the registry, refusal to participate, patient did not start treatment, patient was not invited for the study.

Ethical and regulatory aspects

The study was approved by the Academic Ethical Committee of the Brussels Alliance for Research and

Higher Education (B200-2014-043). Both parents gave their informed consent and each child received an age-appropriate study information document and signed an assent form. It was stipulated in the study information document that secondary findings would not be communicated to the parents unless the genetic finding was featured on the list of the American College of Genetics and Genomics (ACMG)Accepted of genes, conditions and variants that areManuscript recommended to be reported back because of their important consequences for childhood health15. Alternatively, parents could opt out to be informed at all.

Evaluation of response to growth hormone

Height data at the start of treatment and 9-15 months later were extracted from the registry and scaled by intra- or extrapolation to 12 months. The height velocity during the first year of treatment was

compared with the published height velocity response curves from a large registry (KIGS)16. The growth response of the patients was categorized as follows: “non-responder”: height velocity < -1 SDS, “below average”: between -1 and 0 SDS, “above average”: between 0 and +1 SDS and “super-responder”: above +1 SDS. Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020

Exome sequencing

DNA from 20 trios of children and their biological parents was extracted from peripheral white blood cells using standard methods. Sample preparation was done using the SeqCap EZ Library SR protocol

(User’s Guide version 5.0, Roche). Paired-end sequencing (2x100bp or 2x150bp) was performed on the

Hiseq 150017 (Illumina, San Diego, CA, USA) or the Nextseq 50018 (Illumina, San Diego, CA, USA) sequencing platform based on availability. Data-analysis was performed using an in-house developed automated Galaxy based pipeline19. Variant calling was done using the Genome Analysis Toolkit Unified

Genotyper20,21. Variants were annotated using ANNOVAR22,23 in VariantDB24,25. GRCh37 was used as a reference build.

Automated variant prioritization

Automated variant prioritization was performed with the MOON interpretation software26 (version

2.1.3, Diploid Orbicule BVBA, Heverlee, Belgium), as described previously27. In short, MOON uses artificial intelligence to prioritize genetic variants based on patient input data and a disorder model that consists of associations between diseases, disease genes and inheritance patterns. The disorder model is created and updated on a regular basis by performing natural language processing of the medicalAccepted literature27. The following Human Phenotype Manuscript Ontology (HPO) terms were used as input: short stature and/or proportionate short stature and/or small for gestational age and/or intrauterine growth retardation. Depending on the presence of additional clinical features, more patient-specific HPO terms were included. MOON listed variants showing autosomal dominant, autosomal recessive, X- linked dominant, or X-linked recessive inheritance that co-segregated with the phenotype in the family. Also reduced penetrance was taken into account. Variants were manually inspected with the

Integrative Genomics Viewer28,29 (IGV) to exclude false positives. Variant classification was performed using the criteria of the American College of Medical Genetics and Genomics (ACMG) and the

Association for Molecular Pathology30. Variants were considered causal if the ACMG criteria predicted the variant to be pathogenic or likely pathogenic and if the identified variant was co- Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 segregating with the phenotype in the family. Where necessary, this analysis was followed by a phenotypic re-evaluation of the family.

Variant validation by PCR and Sanger sequencing

Primers were designed using Primer331,32 (version 4.1.0). PCRs were performed using the GoTaq G2 kit

(Promega, Leiden, the Netherlands) and checked on a 1 % agarose gel. PCR cleanup was performed using alkaline phosphatase (Roche, Basel, Switzerland) and exonuclease I (Bioké, Leiden, the

Netherlands). Sanger sequencing was performed using the ABI PRISM BigDye Terminator Cycle

Sequencing Ready Reaction Kit (Applied Biosystems Inc., Foster City, USA) and the ABI3130XL sequencer33 (Applied Biosystems Inc., Foster City, USA). CLC DNA Workbench 5.0.234 (CLC bio, Aarhus,

Denmark) was used for data analysis.

SNP array

Single nucleotide polymorphism (SNP) array analysis of the 20 children and their parents was performed using a HumanCytoSNP-12 v2.1 beadchip on an iScan system35 (Illumina, San Diego, CA,

USA), according to manufacturer instructions. Analysis of copy-number variations (CNV) was performed in the CNV webstore36,37. GRCh37 was used as a reference build. Homozygosity mapping was performedAccepted using Plink38 in the CNV webstore. Manuscript Classification of CNV was conducted according to the technical standards for the interpretation and reporting of CNV of the American College of Medical

Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)39. Where necessary, this analysis was followed by a phenotypic re-evaluation of the family.

Genome-wide methylation array

The Infinium MethylationEPIC BeadChip Kit (Illumina, San Diego, CA, USA) was used to target more than 850k CpGs in promoters, gene bodies, enhancers and intergenic regions in the genome (reference Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 build GRCh37). DNA was bisulfite converted using the EZ DNA methylation kit (Zymo research, Irvine,

CA, USA) according to manufacturer’s instructions. Bisulfite converted DNA was amplified, fragmented and hybridized onto the methylation BeadChips. After hybridization, unbound fragments were washed and hybridized fragments were extended using fluorescent nucleotide bases. For each probe present on the BeadChip, raw methylation data were obtained using the Illumina iScan system35 (Illumina, San

Diego, CA, USA) according to manufacturer instructions. For each CpG, the mean beta-value was calculated, where a beta-value of 0 represents no methylation and a beta-value of 1 represents full methylation. Details about the DNA methylation data analysis can be found in the supplementary methods40.

Results

Patient cohort

In the study, 13 boys and 7 girls with short stature at the start of GH treatment were investigated. All children were born SGA except for siblings 10A and 10B where only case 10A was born SGA. 10/20

(including the two siblings) showed additional features besides the short stature. Detailed clinical information on each case is provided in Table 1. ExomeAccepted sequencing Manuscript In a first step we performed exome sequencing in each child and their parents to explore a monogenic defect that could explain the short stature. Using a phenotype-based filter strategy, we were able to unravel the pathogenic/likely pathogenic variant in two siblings and two additional unrelated probands

(three families) (details in Table S140).

In cases 10A and 10B, compound heterozygosity for two variants in the ELAC2 gene was found: a one nucleotide deletion causing a frameshift (NM_173717.1: c.2009delG p.(Cys670Glyfs*13), ClinVar accession number VCV000488502.1) and a splice site variant (NC_000017.10: g.12905755T>G,

NM_173717.1: c.1218+3A>C). Pathogenic variants in ELAC2 have been shown to cause combined Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 oxidative phosphorylation deficiency 17 (MIM: 615440), a severe autosomal recessive disorder of mitochondrial dysfunction characterized by poor growth, failure to thrive, hypotonia and lactic acidosis. Some children present with severe hypertrophic cardiomyopathy in the first year of life. This diagnosis was not made in the two siblings prior to the study. However, re-evaluation of the children and their clinical records revealed features fitting with this diagnosis (case report in Supplementary

Data40). Both siblings had a response to GH comparable to other SGA patients.

In case 16, a rare but previously reported homozygous frameshift variant (NM_014679.5: c.915_925dup11 p.(Leu309Profs*9), ClinVar accession number VCV000030691.1) was identified in the

CEP57 gene. Bi-allelic pathogenic variants in CEP57 have been shown to cause mosaic variegated aneuploidy syndrome (MIM: 614114), a rare autosomal recessive disorder. This condition is characterized by poor growth and variable phenotypic manifestations such as intellectual disability, mild facial dysmorphism and congenital anomalies affecting the heart, aorta, lung and intestinal tract.

In some cases mosaic aneuploidy or structural chromosomal anomalies are found upon cytogenetic analysis. This diagnosis was not suspected in this girl prior to this study, mainly because cytogenetic analysis was reported as normal by the diagnostic lab (case report in Supplementary Data40). However, when Accepted re-evaluating the cytogenetic data, in 3 Manuscript out of 20 mitoses, a monosomy was observed for respectively chromosome 8, 15 and 16. This patient responded above average to GH when compared to SGA patients from the KIGS database.

In case 13, a likely pathogenic de novo missense variant (NM_001257293.1: c.340C>T p.(Arg114Trp)) in the HNRNPH1 gene was identified. Recently, this gene has been shown to cause a X-linked form of mental retardation (Bain type)41. This disorder is characterized by short stature, neuromotor delay, hypotonia, intellectual disability, seizures, dysmorphism and various genitourinary or gastrointestinal

abnormalities. The diagnosis based on clinical evaluation is often difficult because of the rather nonspecific features of the disorder (case report in Supplementary data40). The patient responded well to standard dose GH treatment. Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 SNP array analysis

SNP array analysis was also performed in all children and their parents. In two unrelated probands a rare, pathogenic de novo copy number variation (CNV) was detected (details in Table S240). In case 7, a de novo microduplication within chromosome locus 5q35.2q35.3 was identified. Deletions of this region are associated with Sotos syndrome (MIM: 117550), a well-known overgrowth disorder.

Duplications of this region, and especially of the NSD1 gene, are causing the 5q35 microduplication syndrome (ORPHANET: 228415). This syndrome is also called “reversed” Sotos syndrome since it is characterized by opposite clinical features including short stature and microcephaly. The duplication therefore explained the short stature observed in case 7 (case report in Supplementary Information40).

This patient displayed a more than average response to GH treatment in the first year.

In case 12, a de novo deletion of chromosome locus 22q11.21q11.22 (spanning 18 genes) was found resulting in the 22q11.2 deletion syndrome (distal type I, MIM: 611867). This microdeletion syndrome is characterized by variable clinical features (including short stature) which were present in case 12

(case report in Supplementary Information40). This patient had a more than average response to GH treatment when compared to SGA patients in the KIGS registry.

ExploratoryAccepted genome-wide methylation analysis Manuscript

Since epigenetic mechanisms are known to co-regulate growth, we developed a trio-based pipeline

(Figure 1A) to assess DNA methylation in a randomly selected subset of our cohort comprising 10 children in whom no pathogenic variants were identified after the first step of exome sequencing.

Since this investigation was performed simultaneously with the SNP array analysis, also case 7 and case

12 were included. Using Illumina's MethylationEPIC '850K' BeadChip, blood samples of both children

and their parents were investigated. A density plot showed a normal distribution of methylation values

(as mean beta-values) across all samples, which is indicative of good quality (Figure S140). Because blood is a heterogeneous collection of cell types, each with different DNA methylation profiles, we estimated the cell type composition within each sample42. A principal component analysis indicated Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 that the cell type composition explained much of the observed variability in DNA methylation (Figure

S240) and statistical analysis revealed significant differences in B-lymphocyte fraction between the children and their parents (Figure S340). We therefore adjusted for differences in cellular composition using linear regression. Since it has been shown that also age has an effect on DNA methylation43 and since most remaining significant CpG probes were age-associated (Figure S440), we subsequently identified and excluded all age-responsive CpG probes (P<0.05). After this adjustment, 262,193 probes remained eligible for further analysis.

Using these probes, we found that both cases with a CNV (case 7 and case 12) had relatively more hypermethylated probes in comparison to the other 8 cases without a CNV (Fig 1B). For all cases, gene set enrichment analysis did not reveal a significant enrichment of (differentially methylated) CpG probes for genes that are linked to growth-related pathways (data not shown). This observation however does not exclude the involvement of growth-related genes in the pathogenesis of the growth failure in each case. In case 7 we analyzed the methylation profile more in detail because this child has a microduplication on 5q35 encompassing the NSD1 gene, which is a known histone methyltransferase. Interestingly, the genome-wide methylation profile of this child showed an oppositeAccepted methylation signature in comparison Manuscript to individuals with Sotos syndrome44, who have a microdeletion on 5q35 including the NSD1 gene (Figure 2A). Further analysis of the duplicated locus on 5q35 also showed hypermethylation of the promoter region of NSD1 compared to both parents

(Figure 2B).

In a next step we focused on the methylation pattern of imprinted regions that have been validated before in other studies45. It is well known that these imprinted regions are important in regulating

growth. Our initial analysis revealed a significant enrichment of differentially methylated probes

(DMPs) in these imprinted loci in almost all trios with no CNV. However, when we replaced the affected children with one of their healthy parents in a permutation analysis, similar results were found (data not shown). But when we calculated the odds ratios for the different analyses per trio, a high score for Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 the child-versus-parent analysis in comparison to the other analyses (father-versus-child&mother; mother-versus-child&father) was found for cases 4 and 5 (Figure 1C). This indicates that in these children more DMPs are located in imprinted regions than randomly expected. These DMPs are listed in Table S340 and Table S440. For the other trios there was no clear difference in odds ratios between the different analyses. Remarkably, the lowest odds ratios for the child-versus-parent analysis were observed for cases 7 and 12. These two cases have a CNV as explanation for their growth disorder and may therefore not have methylation disturbances in imprinted loci.

An additional genome-wide analysis of the most frequently (=present in more than 4 trios) differentially methylated probes (DMPs) in the 8 children without a CNV revealed multiple DMPs within, among others, the FGFR2 and the HOOK2 loci (Table S540). Differential methylation of those probes was less frequently observed in a permutation analysis in which the affected children were replaced with one of their healthy parents (Figure S540).

Discussion

With this study we investigated a cohort of children with idiopathic short stature who were born SGA and who were treated with GH. We were interested to know to what extent deep genotyping including exomeAccepted sequencing, SNP array analysis and genome Manuscript-wide DNA methylation profiling could unravel (epi)genomic alterations explaining the growth disorder in this group of children. In contrast to a recent study in SGA newborns46, we investigated a subgroup of SGA children with persistent short stature.

We hypothesized that in this subgroup of SGA children different disease mechanisms may be involved compared to SGA children that do show catch-up growth. To maximize the chance of finding a genomic defect and reduce the risk of including children with a polygenic or multifactorial etiology for the

growth failure, we included only children for whom the height SDS at start of GH treatment was more than 1.5 SDS below the height SDS of each parent. Using this strategy, we were able to identify in 2 siblings and 2 sporadic cases a pathogenic/likely pathogenic intragenic variant and in 2 additional cases a causal CNV. This means that in 5/19 families a genetic diagnosis could be made. Interestingly, 5 of Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 these 6 children with a genetic diagnosis responded well (“above average”) during the first year of GH treatment when compared to the expected growth response in SGA reported in the KIGS database16.

In the group of 13 children without a genetic diagnosis, only 6 had an “above average” or “super” response (Table 1). Our results question the relevance of the exclusion of syndromic SGA patients from

GH treatment.

In four children from three unrelated families, we identified pathogenic/likely pathogenic variants by using exome sequencing. The homozygous frameshift variant in CEP57, found in case 16, has previously been shown to cause mosaic variegated aneuploidy syndrome type 2, a disorder with variable clinical features. Similar to the patient reported by Brightman et al.47, the initial karyotype of case 16 was reported as normal, which probably contributed to the fact that this patient remained undiagnosed.

In case 13, we identified a de novo likely pathogenic variant (NM_001257293.1: c.340C>T p.(Arg114Trp)) in the HNRNPH1 gene. Pathogenic variants in HNRNPH2, a homologous gene, have been shown to cause an X-linked form of mental retardation (Bain type). Only recently, a de novo variant (NM_001257293.1: p.(Arg206Trp)) in HNRNPH1 was reported in a boy with a similar phenotype. This variant is an exact paralogue of a recurrent pathogenic HNRNPH2 variant that has been describedAccepted in boys with mental retardation ManuscriptBain type41. Here, we describe a de novo HNRNPH1 variant in a young boy that (1) substitutes a conserved arginine into a tryptophan residue, (2) is absent in The Genome Aggregation Database (GnomAD version 2.1) and (3) is predicted to have a deleterious effect on the protein (Table S140). Moreover, this arginine residue is conserved in both HNRNPH1 and

HNRNPH2. We are also aware that more (likely) pathogenic variants in HNRNPH1 have been recently identified in patients with a similar neurodevelopmental phenotype (personal communications,

September 2019). We should emphasize that this variant was only identified after an automated re-

analysis of the original WES data. Similar to what has been described48, this case illustrates that reanalysis of sequencing data can increase the diagnostic yield. In this study we used an artificial intelligence-based software for variant interpretation, called Moon (Diploid Orbicule BVBA, Belgium).

The software uses patient-specific phenomes and a disorder model that links genes to diseases, Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 phenotypes and inheritance patterns. By continuously updating its disorder model through natural language processing of the medical literature and by autonomously re-analyzing data, the software makes it possible to identify variants in recently published genes. Furthermore, the software strongly takes into account the phenotype of the patients during variant filtering and ranking. Because the identification of the cause of short stature is often complicated by the multitude of different short stature disorders with sometimes variable, complex and overlapping phenotypes12, we believe that this patient-specific filtering strategy may have contributed to the identification of the pathogenic variants.

After the first step of exome sequencing, we randomly selected a subset of 10 children with normal exome results for DNA methylation profiling. In contrast to the study by Stalman et al. (2018)46, we performed a trio-based analysis, hereby taking into account both child and parental DNA methylation.

With this analysis we could show evidence for abnormal methylation in imprinted regions for at least case 4 and case 5. Some of these differentially methylated regions (DMRs) have already been associated with imprinting disorders that affect growth (Table S340 and Table S440). Interestingly, two specific regions, the GNASAS-DMR and the VTRNA2-1-DMR, were differentially methylated in both cases 4Accepted and 5. The GNASAS gene encodes an antisense Manuscript RNA transcript that regulates GNAS, which is an imprinted gene involved in G-protein signaling. The DNA methylation status of GNASAS has shown to be under influence of famine exposure during pregnancy49 and consumption of certain nutrients and specific dietary components50. In one study, a lower methylation status of the locus was observed in SGA children compared to children born appropriate for gestational age, however, these findings were statistically non-significant51. The vault RNA 2-1 (vtrna2-1) gene encodes a direct inhibitor of protein kinase and plays an important role in the regulation of cell growth. Methylation of this locus

seems to be influenced by the periconceptional environment and folic acid exposure52. Moreover, this region has previously been associated with increased birth weight53. An association with high birth weight was in the latter study also shown for the FGFR2 and HOOK2 loci. Interestingly, these two loci also contained some of the most frequently DMP that were picked up by the genome-wide methylation Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 analysis in the 8 children without a CNV (Table S540). However, the children in our cohort had a decreased birth weight (birth weight SDS between -1.3 and -3.02). Moreover, in contrast to the study by Haworth et al., we observed both hyper- and hypomethylation of probes in these regions.

Consequently, the impact or functional relevance of these findings remains uncertain. Up to now, it is generally assumed that DNA hypermethylation of promoter regions is correlated with gene silencing and that DNA hypomethylation results in gene activation. However, it has become clear that much more complex interactions do exist, making predictions about functional consequences of (opposite)

DNA methylation changes difficult. The latter is nicely illustrated by a recent study that found two separate and partly opposite blood methylation signatures in one single Mendelian disorder, named the ADNP syndrome54. Interestingly, these two (partly) opposite methylation patterns were identified in two groups of patients with pathogenic variants in the same gene and without distinguishable phenotypic characteristics. These results show that different epigenetic mechanisms may contribute to the development of the same (or a similar) phenotype54. For the current study, we do acknowledge that our findings are preliminary and need validation in a larger patient cohort.

As for the exome sequencing, in all children of our cohort, SNP array analysis was performed to explore the poAcceptedssibility of a CNV as cause of the short Manuscript stature. SNP array analysis was not systematically performed in these children before they entered this study. In trio 7, we identified a duplication in the

5q35.2q35.3 region where the NSD1 gene is located. This gene codes for a histone lysine methyltransferase known to catalyze the transfer of methyl groups to histones, especially to lysine 36 at histone 3 (H3K36). Although the exact mechanism remains unknown, several studies have linked

H3K36 methylation to de novo DNA methylation55. In patients with Sotos syndrome, an overgrowth disorder caused by loss-of-function variants in NSD1, DNA methylation profiling shows

hypomethylation of CpG probes located in regulatory regions important for neural and cellular development44. Since case 7 was also included in the subset of children in whom we performed DNA methylation analysis, the epiprofile of this case was available for comparison with that observed in

Sotos syndrome. Interestingly, the methylation profile of case 7 shows the reverse pattern of Sotos Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 syndrome with mainly hypermethylation of the CpG probes (Figure 2A). The increased total number of hypermethylated CpG probes in Figure 1B already suggested a disturbed genome-wide methylation profile. In addition, we observed hypermethylation of the promoter region of the NSD1 gene in case

7. As suggested in other studies56,57, we believe that this might be the consequence of an attempt to rebalance gene dosage by inhibiting gene transcription of the duplicated gene56,57. An increased number of hypermethylated CpG probes was also observed for case 12, who was also included in our methylation analysis (Figure 1B). A genome-wide methylation profile for the distal type 22q11.2 deletion syndrome has not yet been described. More children with a distal 22q11.2 deletion need to be studied in order to determine if a specific methylation signature does exist for this microdeletion syndrome.

It already has been shown that the identification of epiprofiles through methylation profiling can complement diagnostic testing in individuals with a genetic disorder. For example, methylation analysis in a group of 67 children with a neurodevelopmental disorder facilitated the identification of the underlying molecular defect in 31% of the cases58. Even more interesting, in most children the methylation analysis reclassified the case in the correct diagnostic category. Similar to this neurodevelopmentalAccepted study, the analysis of genome Manuscript-wide methylation signatures in short stature phenotypes may also help in the diagnostic process. More specifically, methylation analysis may resolve cases with nonspecific clinical features in whom no pathogenic variant can be identified and help in the interpretation of (non-)coding variants of unknown significance (VUS) in (candidate) genes.

Larger studies are necessary to validate this statement and also to determine if single integrated platforms for both genome- and epigenome-wide analysis (such as single-molecule real-time (SMRT)

sequencing) can be a future diagnostic approach given the highly heterogeneous etiology of growth disorders.

Acknowledgments Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020

We thank Christine Derycke for collecting the blood samples. We are grateful to the families for their cooperation.

Accepted Manuscript

References

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45. Mora JRH, Tayama C, Sanchez-Delgado M, et al. Characterization of parent-of-origin methylation using the Illumina Infinium MethylationEPIC array platform. Epigenomics-Uk. 2018;10(7):941-954. 46. Stalman SE, Solanky N, Ishida M, et al. Genetic Analyses in Small-for-Gestational-Age Newborns. J Clin Endocrinol Metab. 2018;103(3):917-925. 47. Brightman DS, Ejaz S, Dauber A. Mosaic variegated aneuploidy syndrome caused by a CEP57 mutation diagnosed by whole exome sequencing. Clin Case Rep. 2018;6(8):1531-1534. Downloaded from https://academic.oup.com/jcem/article-abstract/doi/10.1210/clinem/dgaa465/5873625 by KU Leuven Libraries user on 24 July 2020 48. Wenger AM, Guturu H, Bernstein JA, Bejerano G. Systematic reanalysis of clinical exome data yields additional diagnoses: implications for providers. Genet Med. 2017;19(2):209-214. 49. Tobi EW, Lumey LH, Talens RP, et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum Mol Genet. 2009;18(21):4046-4053. 50. Arpon A, Milagro FI, Razquin C, et al. Impact of Consuming Extra-Virgin Olive Oil or Nuts within a Mediterranean Diet on DNA Methylation in Peripheral White Blood Cells within the PREDIMED-Navarra Randomized Controlled Trial: A Role for Dietary Lipids. Nutrients. 2017;10(1). 51. Tobi EW, Heijmans BT, Kremer D, et al. DNA methylation of IGF2, GNASAS, INSIGF and LEP and being born small for gestational age. Epigenetics. 2011;6(2):171-176. 52. Silver MJ, Kessler NJ, Hennig BJ, et al. Independent genomewide screens identify the tumor suppressor VTRNA2-1 as a human epiallele responsive to periconceptional environment. Genome Biol. 2015;16. 53. Haworth KE, Farrell WE, Emes RD, et al. Methylation of the FGFR2 gene is associated with high birth weight centile in humans. Epigenomics-Uk. 2014;6(5):477-491. 54. Bend EG, Aref-Eshghi E, Everman DB, et al. Gene domain-specific DNA methylation episignatures highlight distinct molecular entities of ADNP syndrome. Clin Epigenetics. 2019;11(1):64. 55. Rose NR, Klose RJ. Understanding the relationship between DNA methylation and histone lysine methylation. Bba-Gene Regul Mech. 2014;1839(12):1362-1372. 56. Chang AYF, Liao BY. DNA Methylation Rebalances Gene Dosage after Mammalian Gene Duplications. Mol Biol Evol. 2012;29(1):133-144. 57. Keller TE, Yi SV. DNA methylation and evolution of duplicate genes. P Natl Acad Sci USA. 2014;111(16):5932-5937. 58. Aref-Eshghi E, Bend EG, Colaiacovo S, et al. Diagnostic Utility of Genome-wide DNA Methylation Testing in Genetically Unsolved Individuals with Suspected Hereditary Conditions. Am J Hum Genet. 2019;104(4):685-700.

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Figure 1: Genome-wide methylation analysis. a) Flowchart of the trio-based DNA methylation analysis in 10 randomly selected children. b) The total number of differentially methylated probes (DMP) with at least 10% difference (|delta beta parents|>0.1) in methylation between the children and both parents. Children (“trios”) in whom a CNV was identified are indicated with an asterisk. c) Comparison of the odds ratios obtained after permutation analysis. Odds ratios for the different analyses are shown, including the child versus the parents analysis (black line), the father versus the child and the mother analysis (dark grey), and the mother versus the child and the father analysis (light grey). Children (“trios”) in whom a CNV was identified are indicated with an asterisk.

Figure 2: Methylation signature in the child (case 7) with the 5q35 microduplication syndrome. a) Comparison of the methylation signature of case 7 to the methylation signature of patients with Sotos Syndrome (extracted from Choufani et al.). Differences in methylation between the child and the mother (delta beta-mother, horizontal axis) and the father (delta beta-father, vertical axis) are shown. Hypomethylated CpG probes in Sotos syndrome (blue dots) are mainly hypermethylated (delta beta values>0) in case 7 whereas the hypermethylated CpG probes in Sotos syndrome (red dots) are mainly hypomethylated (delta beta values<0) in the child. b) Differences in methylation of the promoter region of NSD1 in case 7 compared to both parents (upper panel, delta betas father = blue, delta betas mother = pink) and the absolute beta-values (lower panel).

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Table 1. Growth and clinical data of the 20 children

CASE 1 2 3 4 5 6 7 8 9 10A 10B Sex male male male female male male male female male female male Birth weight (g) 1530 2290 2250 2650 1880 758 2570 1400 2485 2650 2650 Birth weight SDS -2.0 -2.8 -1.8 -2.0 -2.2 -2.9 -2.5 -4.7 -2.7 -2.0 -1.8 Birth length (cm) 39.5 44 44 46 40 30 45,5 39 46 48 48 Birth length SDS -2.7 -3.2 -2.2 -2.2 -3.6 -4.8 -2.8 -5.0 -2.5 -1.1 -1.1 Birth head circumference 29 33,5 unknown unknown unknown unknown 33 30 unknown unknown unknown (cm) Birth head circumference -1.8 -1,3 unknown unknown unknown unknown -2.0 -3.2 unknown unknown unknown SDS Gestational age 34 39 37 40 36 31 40 38 40 40 39 (weeks) Age start GH 7.8 3.9 10.6 7.7 7.5 9.0 9.4 7.8 4.5 3.6 4.2 (years) Height start GH 108.8 87.7 126.8 111.5 110.9 119.2 114.5 108.5 94.2 92.9 88.9 (cm) (-3.5SDS) (-3.7SDS) (-2.7SDS) (-2.8SDS) (-3.0SDS) (-2.8SDS) (-3.9SDS) (-3.5SDS) (-3.0SDS) (-2.6SDS) (-3.1SDS) GH start 38 43 35 42 38 39 37 67 34 26 dose(µg/kg/d) 37 First year height 9.6 6.8 9.0 6.8 8.5 9.6 9.1 8.2 8.9 8.3 velocity (cm) 6.7 First year Delta H- 0.79 0.35 0.87 0.39 0.63 0.89 0.85 0.68 0.72 0.63 SDS 0.33 GH response ++ + + + ++ +++ ++ ++ + + categorya + Height mother 171 160 161 170 160 154.2 165 163 166 166 159.9 (1.2SDS) (cm) (0.8SDS) (-1.1SDS) (1.0SDS) (0.6SDS) (-1.1SDS) (-2.1SDS) (-0.3SDS) (-0.6SDS) (-0.1SDS) (-0.1SDS) Height father 173 170 174 172.2 178 176 174 171 188 179 179 (cm) (-0.8SDS) (-1.1SDS) (-0.7SDS) (-0.9SDS) (-0.3SDS) (-0.5SDS) (-0.7SDS) (-1.0SDS) (0.8SDS) (-0.2SDS) (-0.2SDS) facial retrognathia, motor dysmorphism, bilateral and developmental recurrent intraventricular facial dysmorphism delay, speech delay, pneumonia, retrognathia, bleeding with initial thrombocytopenia, hypotonia, elevated Other significant pulmonary psychomotor delay, hydrocephaly cryptorchidism pigment alteration of blood lactate, low clinical features hypertension, hypotonia, chronic (good resorption), the iris, single bicarbonate levels, orchidopexia, eczema coarctatio aortae, umbilical artery hypergonadotropic hypospadias, hypospadias hypogonadism, gastric outlet chronic eczema obstruction Enrichment Enrichment

differentially differentially ELAC2 Molecular 5q35.2q35.3 ELAC2 (c.2009delG; Accepted methylated methylated Manuscript (c.2009delG; findingsb microduplication c.1218+3A>C) imprinted imprinted c.1218+3A>C)

regions regions

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CASE 11 12 13 14 15 16 17 18 19 Sex female male male male female female male male female Birth weight (g) 2500 1650 1810 3020 2260 2400 1270 2810 2400 Birth weight SDS -2.0 -1.6 -3.4 -1.3 -3.0 -2.6 -2.5 -1.4 -2.6 Birth length (cm) 48 41 42 47 46 44.5 unknown 45 46 Birth length SDS -0.7 -2.0 -3.4 -2.0 -2.2 -3.0 unknown -2.7 -2.2 Birth head unknown unknown 31,2 unknown unknown unknown unknown unknown unknown circumference (cm) Birth head unknown unknown -2.3 unknown unknown unknown unknown unknown unknown circumference SDS Gestational age (weeks) 39 34 38 40 40 40 33.5 39 40 Age start GH (years) 9.0 3.0 4.0 10.2 7.0 5.7 13.8 8.6 7.9 Height start GH (cm) 104.3 (-5.0SDS) 81.4 (-4.3SDS) 87 (-4.0SDS) 125.8 (-2.6SDS) 99.5 (-4.5SDS) 88.1 (-5.4SDS) 143.2 (-2.7SDS) 118.6 (-2.6SDS) 127.7 (-2.4SDS) GH start dose(µg/kg/d) 39 76 41 31 34 53 34 33 44 First year height 7.0 10.9 9.7 9.7 6.1 8.6 8.7 9.0 12.8 velocity (cm) Delta H-SDS 0.54 1.41 0.99 0.81 0.29 0.75 0.28 0.76 0.99 GH response categorya + ++ ++ +++ 0 ++ unknown ++ +++ 160 167 160 164 150 154 160 164 170 Height mother (cm) (-1.1SDS) (0.1SDS) (-1.1SDS) (-0.4SDS) (-2.9SDS) (-2.2SDS) (-1.1SDS) (-0.4SDS) (0.6SDS) 165 179 174 178 164 183 178 176 195 Height father (cm) (-1.7SDS) (-0.2SDS) (-0.7SDS) (-0.3SDS) (-1.8SDS) (0.3SDS) (-0.3SDS) (-0.5SDS) (1.6SDS) dysmorphic features, clinodactyly, micropenis, triangular face, low gastroesophageal reflux, frontal bossing, high implanted ears; tracheomalacia, arched palate, clinodactyly, failure- frontal bossing, low obstructive sleep apnea, retrognathia, mild to-thrive, Silver nasal bridge, anteverted triangular face, ectopic kidney, blepharophimosis, Other significant clinical Russel Syndrome- nostrils, mild short neck, high vesicoureteral reflux with rhizomelic shortening features like behaviour, clinodactyly, thin upper arched palate, episodes of of arms/legs, speech difficulties, lip, hypertelorism, outstanding ears pyelonephritis, severe clinodactyly digit V, motor delay, epicanthus fold motor and developmental mild intellectual pseudomuscular delay, speech delay, disability built, microcephaly epileptic episode, intellectual disability 22q11.21q11.22 HNRNPH1 CEP57 Molecular findingsb deletion (c.340C>T) (c.915_925dup11) GH growth hormone aGH response categories: height velocity during the first year of GH therapy compared to outcomes reported in SGA patients from KIGS: (0) non-responder (+) below average (++) above average (+++) super- responder bReference sequences and version numbers for ELAC2: NM_173717.1, CEP57: NM_014679.5, HNRNPH1: NM_001257293.1 Accepted Manuscript

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