Rare diseases of epigenetic origin: Challenges and opportunities

Abstract

Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene–disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.

1 Introduction

Rare diseases (RDs) are typically defined by a prevalence threshold of 5–76 cases per 100,000 in the population, with a global average incidence for each disease of 4 per 10,000 (Richter et al., 2015). Although individually rare, RDs are common in aggregate, with more than 10,000 RDs reported to date together affecting roughly 350 million people worldwide (about 4.4% of the population) (Boycott et al., 2019). Historically, however, research and development regarding RDs have been underfunded because of the difficulty of advocacy for small numbers of patients affected by any specific disease (Ekins, 2017). Most RDs are Mendelian disorders, where mutations in a single gene can explain the clinical phenotype (Ekins, 2017; Levy et al., 2022). The recent development of next-generation sequencing techniques has greatly accelerated identification of the genetic origins of RDs (Boycott et al., 2019).

Some RDs of genetic origin are driven by altered epigenetic regulation and are referred to as RDs of epigenetic origin (RDEOs). Epigenetics generally refers to the study of potentially mitotically heritable molecular marks that can perpetuate alternative gene activity states with the same underlying DNA sequence (Henikoff and Greally, 2016; Aristizabal et al., 2020; Carter and Zhao, 2021). These marks are highly relevant in early development, because of their roles in the establishment and maintenance of gene expression profiles that are specific to the functioning of defined cell populations (Feng et al., 2010; Henikoff and Greally, 2016). These gene expression profiles constitute a cellular phenotype that is inseparable from its identity; cells are not static entities, but are defined by their function. Therefore, RDEOs typically affect patients from an early age and drive significantly altered cellular functions across multiple systems (Rodenhiser, 2006; Velasco and Francastel, 2019; Janssen and Lorincz, 2022). RDEOs therefore often present as a combination of immunological, neurological, and physical developmental disorders, perhaps because of the underlying genetic complexity of these aspects of human physiology. They have generally been studied by multiple approaches, including clinical reports of patient phenotypes, diagnostic studies through which candidate genes are selected based on promising variants, bioinformatics to extract biomarkers of epigenome-wide dysregulation, and functional examination of the underlying mechanisms in cell and animal models. These multipronged approaches have led to significant advances in RDEO research.

However, RDEO research faces unique obstacles, as the variability of epigenetic regulation leads to technical challenges in both experimental design and statistical analysis. In addition, heterogeneity of both clinical features and chromatin patterns complicates interpretation of the results of such studies. Despite the difficulties in studying RDEOs, emerging technologies provide new opportunities in this field. In particular, the findings generated from RDEOs can provide valuable insights into the pathophysiology of common complex diseases. This review highlights examples of RDEOs that illustrate the current challenges in this field of research, and evaluates strategies that can be employed to overcome them.

2 Major epigenetic mechanisms associated with RDEOs

RDEOs are driven by genetic variants that lead to epigenetic dysregulation, often closely associated with the underlying chromatic template. Typically, these genetic variants have been identified in the coding regions of epigenetic regulators, altering their protein function and the downstream epigenetic patterns that they establish. While many genes contribute to epigenetic regulation in a broad sense, this review will focus on the three main chromatin-related mechanisms underlying RDEOs, i.e., disruption of DNA methylation (DNAm), histone modifications, and the activities of chromatin remodelers.

In the following sections, we will highlight some examples of RDEOs under each level of epigenetic control (Figure 1). This review does not represent a comprehensive overview of the RDEO literature, but discusses specific RDEOs chosen to highlight the challenges and novel opportunities in this field of research. A list of several known RDEOs registered in the OMIM database is included in Table 1 (Amberger et al., 2019).

FIGURE 1.

Schematic of Epigenetic Regulatory Mechanisms Associated With RDEOs: Chromatin Remodelers, Histone Modifications, and DNA Methylation. Abbreviations: ac, acetylation; me, methylation.

TABLE 1.

Known RDEOs and corresponding genetic origins and functional targets (if available). Curated based on information available in the OMIM database (https://www.omim.org).

Epigenetic regulation	Functional group	Gene	Target	RDEO	OMIM entry
DNA methylation	DNA methyltransferases (DMTs)	DNMT1	cytosine	Hereditary sensory neuropathy type IE (HSANIE)	# 614116
				Autosomal dominant cerebellar ataxia, deafness, and narcolepsy (ADCADN)	# 604121
		DNMT3A	cytosine	Tatton-Brown-Rahman syndrome (TBRS)	# 615879
				Heyn-Sproul-Jackson syndrome (HESJAS)	# 618724
		DNMT3B	cytosine	Immunodeficiency-centromeric instability-facial anomalies syndrome 1 (ICF1)	# 242860
	Methyl-CpG-binding proteins	MBD5	modified cytosine	Intellectual developmental disorder, autosomal dominant 1	# 156200
		MECP2	modified cytosine	Rett syndrome (RTT)	# 312750
				Rett syndrome-associated severe neonatal encephalopathy	# 300673
				X-linked syndromic intellectual developmental disorder-13 (MRXS13)	# 300055
				X-linked Lubs-type syndromic intellectual developmental disorder (MRXSL)	# 300260
	Methylcytosine dioxygenase (TETs)	TET2	modified cytosine	Immunodeficiency-75 (IMD75)	# 619126
		TET3	modified cytosine	Beck-Fahrner syndrome (BEFAHRS)	# 618798
	CGG repeats	FMR1		Fragile X syndrome (FXS)	# 300624
Histone modification	Lysine-specific methyltransferases (KMTs)	KMT2A/MLL1	H3K4	Wiedemann-Steiner syndrome (WDSTS)	# 605130
		KMT2B/MLL2	H3K4	Dystonia 28, childhood-onset (DYT28)	# 617284
		KMT2C/MLL3	H3K4	Kleefstra syndrome 2	# 617768
		KMT2D/MLL4	H3K4	Kabuki syndrome 1
		KMT2E/MLL5	H3K4	O’Donnell-Luria-Rodan syndrome (ODLURO)	# 618512
		KMT2F/SETD1A	H3K4	Early-onset epilepsy with or without developmental delay (EPEDD)	# 618832
				Neurodevelopmental disorder with speech impairment and dysmorphic facies (NEDSID)	# 619056
		KMT2G/SET1DB	H3K4	Intellectual developmental disorder with seizures and language delay (IDDSELD)	# 611055
		KMT2H/ASH1L	H3K36	Autosomal dominant intellectual developmental disorder-52 (MRD52)	# 617796
		KMT1D/EHMT1/GLP	H3K9	Kleefstra syndrome 1 (KLEFS1)	# 610253
		EED	H3K27	Cohen-Gibson syndrome (COGIS)	# 617561
		EZH2	H3K27	Weaver syndrome (WVS)	# 277590
		SUZ12	H3K27	Imagawa-Matsumoto syndrome (IMMAS)/SUV12-related overgrowth	# 618786
		KMT3A/SETD2	H3K36	Luscan-Lumish syndrome (LLS)	# 616831
		KMT3B/NSD1	H3K36	Sotos syndrome (SOTOS)	# 117550
		KMT3G/NSD2	H3K36	Rauch-Steindl syndrome (RAUST)	# 619695
		SETD5	H3K36	Autosomal dominant intellectual developmental disorder-23 (MRD23)	# 615761
		KMT5B/SUV420H1	H4K20	Autosomal dominant intellectual developmental disorder-51 (MRD51)	# 617788
	Lysine-specific demethylases (KDMs)	KDM1A/LSD1	H3K4me1/2, H3K9me1/2	Cleft palate, psychomotor retardation, and distinctive facial features (CPRF)	# 616728
		KDM3B/JHDM2b	H3K9me	Diets-Jongmans syndrome	# 618846
		KDM4B/JMJD2B	H3K9/H3K36me2/3	Autosomal dominant intellectual developmental disorder-65 (MRD65)	# 619320
		KDM5B/JARID1B	H3K4me1/2/3	Autosomal recessive intellectual developmental disorder-65 (MRT65)	# 618109
		KDM5C/JARID1C/SMCX	H3K4me2/3	Claes-Jensen type of X-linked syndromic intellectual developmental disorder (MRXSCJ)	# 300534
		KDM6A/UTX	H3K27me2/3	Kabuki syndrome 2	# 300867
		KDM6B/JMJD3	H3K27me2/3	Neurodevelopmental disorder with coarse facies and mild distal skeletal abnormalities (NEDCFSA)	# 618505
		KDM7B/PHF8	H3K9	Siderius-type X-linked syndromic intellectual developmental disorder (MRXSSD)	# 300263
	Histone acetyltransferases (HATs)	KAT3A/CREBBP	H2A; H2B; H3	Rubinstein-Taybi syndrome (RSTS1)	# 180849
				Menke-Hennekam syndrome-1 (MKHK1)	# 618332
		KAT3B/EP300	H2A; H2B; H3	Rubinstein-Taybi syndrome 2 (RSTS2)	# 613684
				Menke-Hennekam syndrome-1 (MKHK2)	# 618333
		KAT5/TIP60	H4; H2A	Neurodevelopmental disorder with dysmorphic facies, sleep disturbance, and brain abnormalities (NEDFASB)	# 619103
		KAT6A/MYST3/MOZ	H3K9	Arboleda-Tham syndrome (ARTHS)	# 616268
		KAT6B/MYST4/MORF	H3K9	Genitopatellar syndrome (GTPTS)	# 606170
				SBBYS variant of Ohdo syndrome (SBBYSS)	# 603736
		KANSL1	H4K16	Koolen de Vreis syndrome (KDVS)
		KAT8/MYST1/HMOF	H4K16	Li-Ghorgani-Weisz-Hubshman syndrome (LIGOWS)	# 618974
	Histone deacetylase (HDAC)	HDAC4		neurodevelopmental disorder with central hypotonia and dysmorphic facies (NEDCHF)	# 619797
		HDAC6		X-linked dominant chondrodysplasia	# 300863
		HDAC8		Cornelia de Lange syndrome-5 (CDLS5)	# 300882
	Histone kinases	RPS6KA3/RSK2	H3S10	Coffin-Lowry syndrome (CLS)	# 303600
				X-linked intellectual developmental disorder-19 (XLID19)	# 300844
	Ubiquitination	UBE2A	H2B	Intellectual developmental disorder, X-linked syndromic, Nascimento type (MRXSN)	# 300860
	Histone deubiquitinase	BAP1	H2AK119ub	Kury-Isidor syndrome (KURIS)	# 619762
		ASXL1	H2AK119ub	Bohring-Opitz syndrome	# 605039
Chromatin remodeling	SWI/SNF family	ATRX	H3.3	X-linked alpha-thalassemia/mental retardation syndrome (ATRX)	# 301040
				X-linked intellectual disability-hypotonic facies syndrome-1 (MRXFH1)	# 309580
		HELLS		Immunodeficiency-centromeric instability-facial anomalies syndromes 4 (ICF4)	# 616911
		CDCA7		Immunodeficiency-centromeric instability-facial anomalies syndromes 3 (ICF3)	# 616910
	Transcription factor of CDCA7	ZBTB24		Immunodeficiency-centromeric instability-facial anomalies syndromes 3 (ICF2)	# 614069
	SWI/SNF family - BAF complex and associated factors	ARID1B/BAF250B		Coffin-Siris syndrome-1 (CSS1)	# 135900
		ARID1A/BAF250A		Coffin-Siris syndrome-2 (CSS2)	# 614607
		SMARCB1/BAF47		Coffin-Siris syndrome-3 (CSS3)	# 614608
		SMARCA4/BRG1		Coffin-Siris syndrome-4 (CSS4)	# 614609
		SMARCE1/BAF57		Coffin-Siris syndrome-5 (CSS5)	# 616938
		ARID2/BAF200		Coffin-Siris syndrome-6 (CSS6)	# 617808
		DPF2/BAF45D		Coffin-Siris syndrome-7 (CSS7)	# 618027
		SMARCC2/BAF170		Coffin-Siris syndrome-8 (CSS8)	# 618362
		SOX11		Coffin-Siris syndrome-9 (CSS9)	# 615866
		SMARCA2/BRM		Nicolaides-Baraitser syndrome (NCBRS)	# 601358
				Blepharophimosis-impaired intellectual development syndrome (BIS)	# 619293
		ADNP		Helsmoortel-Van der Aa syndrome (HVDAS)	# 615873
	CHD family	CHD1		Pilarowski-Björnsson syndrome (PILBOS)	# 617682
		CHD2		Developmental and epileptic encephalopathy-94 (DEE94)	# 615369
		CHD5		Parenti-Mignot neurodevelopmental syndrome (PMNDS)	# 619873
		CHD7		CHARGE syndrome	# 214800
		CHD8		Intellectual developmental disorder with autism and macrocephaly (IDDAM)	# 615032
	CHD family - NuRD/Mi-2 complex and associated proteins	CHD3		Snijders Blok-Campeau syndrome (SNIBCPS)	# 618205
		CHD4		Sifrim-Hitz-Weiss syndrome (SIHIWES)	# 617159
		PHF6		Börjeson-Forssman-Lehmann syndrome (BFLS)	# 301900
	ISWI family	BPTF/NURF301		Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL)	# 617755
	Human INO80 complex	SRCAP	H2A.Z	Floating Harbor syndrome (FLHS)	# 136140
				Developmental delay, hypotonia, musculoskeletal defects, and behavioral abnormalities (DEHMBA)	# 619595
Other epigenetic regulation	Cohesin	NIPBL		Cornelia de Lange syndrome-1 (CDLS1)	# 122470
		SMC1A		Cornelia de Lange syndrome-2 (CDLS2)	# 300590
		SMC3		Cornelia de Lange syndrome-3 (CDLS3)	# 610759
		RAD21		Cornelia de Lange syndrome-4 (CDLS4)	# 614701
	Histone variants	HIST1H1E		Rahman syndrome (RMNS)	# 617537
	Histone chaperone	FACT/SUPT16H	H2A–H2B dimer; H2A.X; H3.1–H4 dimer; H3.2–H4 dimer	Neurodevelopmental disorder with dysmorphic facies and thin corpus callosum (NEDDFAC)	# 619480

2.1 RDEOs and DNA methylation

DNAm is the most common and widely-investigated DNA modification in the human epigenome. While there are several other DNA modifications, the role of DNAm in RDEOs has been elucidated in the greatest detail (Schübeler, 2015). In the human epigenome, DNAm is commonly found at the 5th carbon of cytosine, forming 5-methylcytosine (5 mC), which is most often, but not exclusively, maintained in the context of cytosine-phosphate-guanine (CpG) dinucleotides (Schübeler, 2015). In concert with histone and chromatin modifications, DNAm facilitates maintenance of the state of gene expression within cells (Cedar and Bergman, 2009; Allis and Jenuwein, 2016; Carter and Zhao, 2021). As a simple overview, DNA methyltransferases, such as DNMT3A, DNMT3B, and DNMT3L, establish the pattern of DNAm, which is then maintained by DNMT1 and associated proteins (Schübeler, 2015; Lyko, 2018). The ten-eleven translocation (TET) protein family is responsible for the iterative oxidation of 5 mC–5-hydroxymethylcytosine (5hmC), 5-formylcytosine, and then 5-carboxylcytosine, which can eventually be removed by thymine DNA glycosylase-mediated base excision repair (Wu and Zhang, 2017). Genetic variants that alter the functions of these regulatory proteins can drive global disruption of DNAm level, leading to dysregulation of differentiation and developmental trajectories (Liao et al., 2015; Izzo et al., 2020).

2.1.1 DNMT3A-associated RDEOs

DNMT3A is the most commonly mutated gene in patients with hematopoietic malignancies (Ley et al., 2010; Xie et al., 2014). In humans, germline variants of DNMT3A have been linked to changes in both neurological and physical development. For example, constitutional loss-of-function (LoF) variants of DNMT3A lead to decreased global DNAm and altered hematopoiesis in a condition referred to as Tatton-Brown-Rahman syndrome (TBRS; OMIM #615879) (Smith et al., 2021). TBRS is characterized by generalized overgrowth, intellectual disability and a range of neurodivergent phenotypes, distinctive facial features, and an altered hematopoietic landscape (Tatton-Brown et al., 2017; 2018). In contrast, gain-of-function (GoF) variants of DNMT3A were shown to cause Heyn-Sproul-Jackson syndrome (HESJAS; OMIM #618724), a very rare condition reported in only three patients to date, all of whom presented with microcephalic primordial dwarfism (Heyn et al., 2019). Functional characterization of the disease-associated variant showed altered DNMT3A function preventing binding to di- and trimethylated histone H3 lysine 36 (H3K36me2/3) and driving increased DNAm in regions marked by H3K27me3 and H3K4me3 (Heyn et al., 2019). The dichotomy of TBRS and HESJAS, driven by DNMT3A LoF and GoF, respectively, represents an intriguing example of RDEO-associated phenotypic heterogeneity, which will be explored further below.

2.1.2 TET2-associated RDEOs

TET proteins mediate the active DNA demethylation process in mammals (Wu and Zhang, 2017). Similar to DNMT3A, TET2 mutations have been linked to a wide range of hematopoietic malignancies (Delhommeau et al., 2009; Jiang, 2020). In addition to cancer, the role of TET2 dysregulation in development of RDEOs has been explored. Germline TET2 variants have been reported to be associated with a number of diseases with various levels of severity in humans. In one study, a patient with a heterozygous truncation variant of TET2 presented with delayed developmental milestones in early childhood. Elderly germline carriers of the same allele also exhibited CD8⁺ T-cell exhaustion, skewing toward terminally differentiated effector memory cells re-expressing CD45RA (TEMRA) (Kaasinen et al., 2019). In another study, patients homozygous for LoF TET2 variants showed severe immunodeficiency, lymphadenopathy, hepatosplenomegaly, developmental delay, autoimmunity, and B-cell or T-cell lymphoma (Stremenova Spegarova et al., 2020). Class-switch recombination defects were also detected in TET2-deficient B-cell (Stremenova Spegarova et al., 2020). Elevated levels of DNAm were observed along with decreased DNA hydroxymethylation in both heterozygous and homozygous TET2 LoF cases (Kaasinen et al., 2019; Stremenova Spegarova et al., 2020). Furthermore, a rare variant burden analysis showed that rare heterozygous TET2 variants were strongly enriched in individuals with neurodegenerative diseases (Cochran et al., 2020). These observations link heterozygous TET2 variants to long-term epigenetic dysregulation, leading to altered immune profiles and neurodegeneration later in life. In contrast, homozygous LoF variants of TET2 result in drastic alterations in early life that lead to extreme immunodeficiency. Taken together, these observations highlight the role of TET2, as well as the effects of the timing and dosage of its expression, in the regulation of hematopoiesis and neuronal specification.

2.1.3 Fragile X syndrome (FXS)

Fragile X syndrome (FXS; OMIM #300624) is one of the best-studied RDEOs, and is relatively common with a prevalence of roughly 1/7000 among males and 1/11,000 among females (Coffee et al., 2009; Lévesque et al., 2009; Hunter et al., 2014). Distinct from RDEOs with mutations in the coding regions of genes, FXS is driven by expansion of a trinucleotide CGG repeat (>200 copies) in the promoter of the FMR1 gene, which leads to excessive DNAm and transcriptional silencing of FMR1 (Sutcliffe et al., 1992). Patients with FXS typically present with delayed developmental milestones, intellectual disability, and a range of other neurodivergent phenotypes (Hagerman et al., 2017). FXS is also the most common single-gene driver of autism spectrum disorder (ASD), contributing to about 5% of cases (Fyke and Velinov, 2021). FMR1 is located on the X chromosome and has an X-linked inheritance pattern, and the phenotype is usually more severe in males with FXS (Hagerman et al., 2017).

FMR1 encodes FMRP, an RNA-binding protein that is essential for translational regulation in neuronal dendrites. Patients with FXS show the presence of dense but immature dendritic spines at excitatory synapses (Hagerman et al., 2017). Several mechanisms of action have been proposed to explain the physiology of FXS. As FMRP downregulates the activity of group 1 and group 5 metabotropic glutamate receptors (mGluR1 and mGluR5, respectively), FMRP deficiency is associated with increased glutaminergic signaling (Wang et al., 2010; Darnell et al., 2011). Activation of mGluR1 drives increased dendritic translation, AMPA receptor internalization, and long-term depression (LTD) via a series of signaling cascades, including phosphoinositide-3 kinase (PI3K) complex-mediated signaling. FMRP has been studied from a variety of perspectives to elucidate the underlying pathophysiology of FXS, and the results have highlighted the challenges in development of therapeutic strategies for RDEOs.

2.2 RDEOs and histone modifications

DNA nucleotides are wrapped around an octamer of histone proteins to form the basic unit of chromatin, the nucleosome (Allis and Jenuwein, 2016). Posttranslational modifications, such as acetylation, methylation, phosphorylation, SUMOylation, and ubiquitination, are often found on the N- or C-terminal tails of histones, thereby altering their charge and structure (Stillman, 2018). This can affect how histones interact with each other, DNA, and various other chromatin-binding proteins. For example, the negatively charged acetyl group neutralizes the electrostatic interaction between the lysine-rich histone tail and the negatively charged DNA backbone, which can lead to looser nucleosome packaging and facilitate increased accessibility of DNA to the transcriptional machinery (Shahbazian and Grunstein, 2007). Histone modifications are highly correlated with chromatin states (Ernst and Kellis, 2012), nucleosome spacing and positioning (Valouev et al., 2011), and DNAm patterns (Cedar and Bergman, 2009; Allis and Jenuwein, 2016; Velasco et al., 2021). Together, these epigenetic marks maintain transcriptional control throughout the cell cycle, and are responsive to environmental stimuli (Allis and Jenuwein, 2016).

2.2.1 PRC2-related overgrowth syndromes

Polycomb repressive complex 2 (PRC2) is one of the best-studied epigenetic writers, and is responsible for H3K27me1/2/3 (Margueron and Reinberg, 2011; Schuettengruber et al., 2017; Laugesen et al., 2019; van Mierlo et al., 2019). In particular, H3K27me3 is considered the hallmark of PRC2-mediated repression (Laugesen et al., 2019). This mark is highly enriched in the promoters of silenced genes and poised enhancers (Rada-Iglesias et al., 2011). When present together with H3K4me3, a mark of active promoters, the chromatin is in a bivalent state that can readily switch between active and repressed transcriptional states, and the genes in the bivalent chromatin tend to be critical for early embryogenesis (Bernstein et al., 2006; Rada-Iglesias et al., 2011; Allis and Jenuwein, 2016; Liu et al., 2016; Hörmanseder et al., 2017). PRC2 maintains H3K27 methylation status through cell division, and thereby plays a pivotal role in the establishment and preservation of cell identity during development (Rada-Iglesias et al., 2011; Schuettengruber et al., 2017). The PRC2 core complex is composed of four core subunits, i.e., EZH1/2, EED, SUZ12, and RBBP4/7 (van Mierlo et al., 2019), and non-conservative variants in these complexes have been shown to cause PRC2-related overgrowth syndromes: Weaver syndrome is linked to EZH2 variants (OMIM #277590; Gibson et al., 2012), Cohen–Gibson syndrome is related to EED variants (OMIM #617561; Cohen et al., 2015), and SUZ12-related overgrowth is linked to SUZ12 variants (OMIM #618786; Cyrus et al., 2019). Each of these PRC2-related overgrowth syndromes presents with tall stature, macrocephaly, advanced bone age, and intellectual disability, and Weaver syndrome and SUZ12-related overgrowth have been linked to increased risk of hematological malignancies and other cancers (Tatton-Brown et al., 2018; Cyrus et al., 2019; Gamu and Gibson, 2020). The importance of PRC2 in establishing transcriptional silencing in key developmental genes, such as the Hox gene clusters, can at least in part explain the correlation between LoF variants in these genes and overgrowth phenotypes (Gentile and Kmita, 2020). These phenomena highlight the non-redundancy of PRC2 subunits in facilitating histone modifications.

2.2.2 H3K4 methyltransferase-related neuropsychiatric phenotypes

KMT2F (also SETD1A) and KMT2G (also SETD1B) are SET1-family proteins, which are components of the Set1/COMPASS methyltransferase complexes that contribute to H3K4me1/2/3 (Shilatifard, 2012). H3K4me1 is highly enriched in enhancer regions, whereas H3K4me3 is positively correlated with transcriptionally active promoters (Collins et al., 2019). H3K4 methylation has been shown to be vital for memory formation and retrieval (Gupta et al., 2010; Collins et al., 2019). Eight H3K4-specific histone lysine methyltransferases (KMTs) have been identified in humans to date (Allis et al., 2007), all of which are associated with neurological or psychiatric disorders (Collins et al., 2019). Hypomorphic KMT2F and KMT2G variants have been linked to neuropsychiatric disorders. Genome-wide screening and analysis of de novo insertion/deletion variant transmission pattern identified KMT2F as a candidate susceptibility gene for schizophrenia (Takata et al., 2014), which was supported by a meta-analysis with 1,077 parent–proband trios (Singh et al., 2016). In human neuronal cultures, a heterozygous LoF variant of KMT2F results in increased dendritic length and complexity, as well as increased neuronal bursting activity (Wang et al., 2022). The altered neuronal morphology and activity may underlie KMT2F-associated schizophrenia. In addition, both KMT2F and KMT2G variants are associated with early-onset epilepsy; in the case of KMT2G, patients also present with developmental delay, intellectual disability, and ASD-like behaviors (Hiraide et al., 2018; Yu X. et al., 2019; Den et al., 2019; Roston et al., 2021; Weng et al., 2022). Functional magnetic resonance imaging (fMRI) showed that instead of language-related cortical regions, the precentral gyrus is activated in the brains of patients when performing language tasks (Weng et al., 2022). The molecular basis of the altered neural connectivity remains to be elucidated.

2.3 RDEOs and chromatin remodeling

ATP-dependent chromatin remodeling complexes comprise another major class of epigenetic regulators. There are four main families of chromatin remodelers in eukaryotes: SWI/SNF, ISWI, CHD, and INO80 (Längst and Manelyte, 2015). Complexes in all families share the ability to bind to nucleosomes and break the DNA–histone interaction (Wang et al., 2007). Some remodeler families exhibit more specialized functions, such as the SWI/SNF family complexes that are responsible for nucleosome sliding and increasing DNA accessibility (Cenik and Shilatifard, 2021). Other remodeler families have dynamic functions, such as the INO80 family remodelers, which are involved in histone variant deposition, transcriptional activation, and DNA repair (Längst and Manelyte, 2015). Moreover, as chromatin remodelers alter nucleosome organization and chromatin assembly, their functions also do affect the access of DNMT, TET, and histone-modifying proteins, thus indirectly regulating DNAm and histone modifications (Allis and Jenuwein, 2016). For example, deposition of the histone variant H2A.Z by the chromatin remodeler SRCAP is negatively correlated with DNAm level across plants and animals (Zilberman et al., 2008; Conerly et al., 2010; Zemach et al., 2010). As the chromatin remodelers influence chromatin accessibility, they also play important roles in development, and pathogenic variants of the members of these families often lead to developmental disorders and malignancies (Boerkoel et al., 2002; Wang et al., 2007; Alfert et al., 2019; Cenik and Shilatifard, 2021).

2.3.1 SRCAP-associated RDEOs

SNF2-related CREBBP activator protein (SRCAP; OMIM #611421) is the core catalytic component of the SRCAP chromatin remodeling complex, which is responsible for the deposition of H2A.Z–H2B dimers into nucleosomes (Wong et al., 2007; Feng et al., 2018). In addition to its chromatin remodeling capacity, SRCAP acts as a transcriptional regulator (Monroy et al., 2001) and promotes the DNA damage response (Dong et al., 2014). Recent evidence has shown that SRCAP is also critical for cell cycle progression by recruiting cytokinesis regulators to the midbody (Messina et al., 2021). Variants of SRCAP have been identified in patients presenting with neurodevelopmental disorders (NDDs), and their relations with Floating-Harbor syndrome (FLHS; OMIM #136140) are particularly well defined. FLHS is an extremely rare RDEO with about 100 cases reported to date, which is characterized by short stature, delayed bone age, distinctive craniofacial features, and delayed language development (Hood et al., 2012). FLHS is driven by truncation variants in exons 33 and 34 of SRCAP (“FLHS locus”), upstream of the region encoding the AT-hook DNA-binding motifs. In contrast, SRCAP variants upstream of exon 33 have been linked to non-FLHS SRCAP-related NDD, presenting with distinct DNAm profiles and an alternative set of phenotypes, including behavioral, psychiatric, and musculoskeletal problems as well as hypotonia (Rots et al., 2021). The DNAm changes may be driven by their inverse relations with H2A.Z deposition (Zilberman et al., 2008; Zemach et al., 2010). A recent study showed that transposable insertion variants in the SRCAP gene lead to particularly severe conditions characterized by failure to thrive, hypotonia, developmental delay, seizures, ASD, and mood disorders (Zhao et al., 2022). The range of clinical presentations of SRCAP-associated RDEOs demonstrates the phenotypic heterogeneity of these diseases.

2.3.2 Immunodeficiency with centromeric instability and facial anomalies syndrome (ICF)

Immunodeficiency with centromeric instability and facial anomalies syndrome (ICF) exemplifies the heterogeneity of genetic causes and clinical presentation of RDEOs. ICF is characterized by chromosomal instability, global developmental delay, and humoral immune deficiency that results in recurrent and often fatal infections (Kamae et al., 2018; Helfricht et al., 2020). The molecular hallmarks of ICF include chromosomal deletions or duplications, heterochromatin decondensation, and centromeric breakage (Robertson and Wolffe, 2000). ICF has been linked to hypomorphic variants in four genes: DNMT3B (ICF1; OMIM #242860), ZBTB24 (ICF2; OMIM #614069), CDCA7 (ICF3; OMIM #616910), and HELLS (ICF4; OMIM #616911) (Xu et al., 1999, 199; Vukic and Daxinger, 2019). Whereas DNMT3B directly modifies DNA methylation status, CDCA7 and HELLS are chromatin remodelers. Despite the common etiology of low DNAm at pericentromeric satellite 2 and 3 repeats (Velasco et al., 2018), ICF2, 3, and 4 are driven by defects in chromatin remodelers or their expression; CDCA7 and HELLS form an ATP-dependent nucleosome remodeling complex that catalyzes nucleosome sliding (Jenness et al., 2018), while ZBTB24 is a transcriptional regulator that binds directly to the CDCA7 promoter and activates its transcription (Wu et al., 2016; Jenness et al., 2018). Mechanistically, the functions of these three proteins are intertwined, thus explaining the similar clinical phenotypes associated with mutations in their genes.

ATP-dependent nucleosome remodeling complexes and their roles in maintaining nucleosome accessibility have been implicated in DNA double-strand break (DSB) repair (Groth et al., 2007; Harrod et al., 2020). One type of DSB repair, non-homologous end-joining (NHEJ), is crucial for B-cell maturation by mediating class-switch recombination (Woodbine et al., 2014). Hypo- or agammaglobulinemia in patients with ICF2–4 can be driven by the NHEJ defect (Blanco-Betancourt et al., 2004; Unoki et al., 2018; He et al., 2020). In contrast, however, DNMT3B has not been implicated in NHEJ. Again, these observations suggest that the mechanism underlying DNAm dysregulation in ICF2–4 may be different from than in ICF1 (Dunican et al., 2015). The dimorphism in regulatory processes between ICF1 and ICF2–4 despite the shared clinical phenotypes presents a challenge in the study of RDEOs, and is explored in more detail below.

3 Challenges and opportunities in studying RDEOs

3.1 Cell type and tissue specificity of epigenetic data can confound disease status and must be studied in a relevant model

The specificity of epigenetic signatures for each cell type and tissue poses challenges in studying RDEOs not encountered in genomic research, including cellular heterogeneity and difficulties in obtaining the tissue or cell type of interest. As epigenetic mechanisms play important roles in defining cell type and cell function (Carter and Zhao, 2021; Janssen and Lorincz, 2022), RDEOs that are driven by alternative epigenetic states can present atypical cell differentiation and maturation patterns as well as altered epigenetic states in each cell population (Blanco-Betancourt et al., 2004; Lindsley et al., 2016; Kamae et al., 2018; Stremenova Spegarova et al., 2020). These phenomena pose unique problems in analyzing bulk tissue epigenetic data. In heterogeneous tissues, the sources of epigenetic variability can be driven by changes in either cell type composition or the epigenetic regulation within a given cell type, or a combination thereof, and two main methods have been applied to resolve these issues. Many groups have constructed algorithms for computationally estimating cell type proportions in complex tissues. Specifically, novel methods have been developed to deconvolute cell type-specific effects of diseases (Zheng et al., 2018; Rahmani et al., 2019), providing new avenues for delineating the effects of RDEOs on cell type proportion versus cell type-specific effects. It should be noted that these methods have high computing power requirements as they involve complex modeling with interaction terms, and so are more appropriate in cohorts with larger sample sizes or for dimension-reduced data.

In addition to deconvolution, which may suffer from predictor errors or the lack of appropriate reference data sets, many studies have opted to analyze sorted cell samples or to use a single-cell approach. Cell sorting requires the isolation of relevant cell types using techniques such as fluorescence-activated cell sorting (FACS) (Hines et al., 2014; Gasparoni et al., 2018). Alternatively, examination of the patient’s epigenome at the single-cell level can easily distinguish whether the differential regulation is due to compositional changes, cell-specific dysregulation, or a mixture of both (Angermueller et al., 2016; Cheung et al., 2018; Hui et al., 2018; Karemaker and Vermeulen, 2018; Carter and Zhao, 2021). One potential caveat of FACS for purified cells or single-cell analysis is that the tissue dissociation and sorting procedures have been shown to alter transcriptomic profiles (van den Brink et al., 2017). These effects of the sorting process on epigenetic profiles warrant further investigation. Moreover, current single-cell epigenomic technologies result in data degradation and limit the interpretability of the data, so improvements in the experimental and analytic pipelines will be necessary for general application (Ahn et al., 2021; Carter and Zhao, 2021). Overall, single-cell approaches can be used to distinguish the sources of epigenetic variations, which is necessary to gain an understanding of the mechanisms underlying RDEOs.

Animal models, tissue culture, and postmortem tissues are often used to study RDEOs. Some of the most significant strides in RDEO research have been made in model organisms (Mizuguchi et al., 2004; Hagerman et al., 2017; Greenberg et al., 2019; Gamu and Gibson, 2020; Izzo et al., 2020; Huisman et al., 2021; Smith et al., 2021), but not all of this research can be readily translated to humans. For example, mGluR1 signaling blockers showed therapeutic potential for FXS in animal trials, but the effects failed to replicate in clinical trials in adolescents or adults with FXS (Berry-Kravis et al., 2016). These discrepancies may be due to differences in the trajectories of mouse and human brain development and FMRP function (Hagerman et al., 2017). A study examining FMRP binding partners in a human forebrain organoid model showed that, in addition to the presence of many human-specific FMRP binding partners, the organoid model could only be rescued by inhibition of PI3K and not mGluR1 (Kang et al., 2021). The human forebrain model utilized organoids, which are tiny progenitor cell-derived 3D structures in tissue culture that recapitulate basic tissue-level properties (Rossi et al., 2018). Compared to tissue cultures of immortalized cell lines, organoid technology promises to provide higher fidelity representation of complex tissue architecture and cell–cell interactions, with growth and regeneration properties more closely resembling those of primary tissues (Forsberg et al., 2018; Rossi et al., 2018). However, protocols for many organoid types have yet to be optimized; their formation can be unstable and they often fail to reach later stages of development (Rossi et al., 2018). Despite this caveat, organoid culture is an example of a method for mimicking human physiology, and represents an alternative approach for investigating RDEOs in biologically relevant cell models.

Similarly, patient-derived induced pluripotent stem cells (iPSCs) are also valuable for studying RDEOs with cell type specificity. A cocktail of transcription factors is used to convert somatic cells into pluripotent stem cells, which can then be further differentiated to address research questions with tissue specificity (Takahashi et al., 2007; Pozo et al., 2022; Sheridan et al., 2022). As iPSCs retain the donor genotype, they have been widely used to study RDs of genetic origin (Casanova et al., 2014). iPSCs in monolayer culture are more scalable and easier to maintain than 3D organoids, and so serve as better platforms for high-throughput genetic and drug screening (Mellios et al., 2018; Balafkan et al., 2020).

3.2 The developmental nature of many RDEOs leads to difficulty in timing of sample collection

In addition to cell specificity, epigenetic regulation is also sensitive to developmental timing. Most RDEOs have an early onset, as dysregulation of the epigenome affects the trajectory of cell fate decisions and cell identity in a critical period, which is often early embryogenesis (Butler et al., 2012; Lai et al., 2018; Calle-Fabregat et al., 2020; Izzo et al., 2020). Defects in these epigenetic regulatory mechanisms, therefore, lead to specific disease phenotypes that arise within a given developmental window. For example, Dnmt3A and Dnmt3B knockdown are lethal in mice at the embryonic stage, as their functions are critical for epigenetic reprogramming (Okano et al., 1999; Li, 2002). Similarly, null Kmt2d variants in mice are also lethal before embryonic day 10.5, as the protein encoded by this gene is essential for gastrulation (Ashokkumar et al., 2020). There is also evidence that the RDEO disease phenotype can be difficult to reverse past the early developmental period. This phenomenon has been demonstrated in clinical trials of candidate therapeutic agents for FXS, many of which have shown greater efficacy in children than in adolescents or adults (Berry-Kravis et al., 2016; Hagerman et al., 2017). Therefore, experimental conditions that resemble the relevant developmental time point may be crucial to extrapolate the findings for application to patient care.

Animal models are instrumental in understanding the effects of RDEOs on developmental trajectories beyond the molecular phenotypes. In contrast to cell-based models, model organisms can be used to examine morphological and behavioral anomalies as well as system-level dysregulation. In a mouse study, Vallianatos et al. (2020) showed that mutations in key regulators of H3K4 methylation led to changes in dendritic morphology, memory formation, and behavioral aggression. This level of complexity speaks to the unique value of animal studies. Cell-based models, such as iPSCs, can also be used to mimic critical periods (Robinton and Daley, 2012). The process of creating iPSCs drives epigenetic reprogramming, thereby mimicking an embryonic state that can be examined as it is or after differentiation into specialized cell types (Scesa et al., 2021; Pozo et al., 2022). In addition, the use of primary patient samples, such as blood spots or amniotic fluid collected by amniocentesis, can facilitate the identification of RDEOs with biomarkers or be studied later to explore epigenetic dysregulation at an early stage.

Many clinical and experimental studies of RDEOs focus on early developmental time points, potentially due to the early onset of many of these diseases. For example, follow-up of patients into adulthood is comparatively rare, but such studies can be crucial to obtain a detailed clinical picture of RDEOs (Kodra et al., 2018). Studies of the natural history of RDEOs with complex clinical phenotypes can provide insights into the heterogeneity of symptoms and potential comorbidities, and thus inform long-term management and treatment solutions (Hagleitner et al., 2007; Arvio, 2016; Van Remmerden et al., 2020). Similar long-term observations can also be beneficial in animal studies. While animal models are often used to validate the clinical phenotypes observed in patients with RDEOs and validate the genotype–phenotype associations (Cacheiro et al., 2019), they can also be informative regarding potential disease progression. One example outside of RDEO is the report of B-cell malignancy in aging Nfatc2 knockout (KO) mice before the discovery of the first human patient with homozygous LoF NFATC2 variant, who developed B-cell lymphoma as a young adult (May et al., 2014; Sharma et al., 2022). Previous Nfatc2 KO mouse studies mainly focused on skeletal and cartilage defects, as these symptoms manifest early (Ranger et al., 2000; Koga et al., 2005). Comprehensive analysis of RDEO animal models may also be instructive regarding the conditions of patients with corresponding variants. Qualitative studies of patients with RDEO and corresponding animal models can elucidate the effects of epigenetic dysregulation in sensitive tissues throughout the developmental trajectory.

3.3 RDEO analyses are inherently underpowered for high-dimensional omics analysis

Although there has been a great deal of progress in the discovery and characterization of RDEOs, technical limitations remain major challenges in the field. These challenges are exemplified in the epigenome-wide association study (EWAS), which is the most widely employed method of studying DNAm and uses multiple regression analysis to test for associations between measured DNAm sites and the phenotype of interest (Lappalainen and Greally, 2017; Li et al., 2019). An EWAS typically incorporates hundreds to thousands of samples, with measurement of hundreds of thousands of CpGs (Wahl et al., 2017; Merid et al., 2020). With multiple testing on all sites measured on the commonly employed Illumina EPIC BeadChip array ( ∼ 865 k), around 200 samples, with balanced cases and controls, would be needed to detect a 5% mean difference in methylation status at > 80% power, if a p-value threshold of 0.05 is set after correction for multiple comparisons (Mansell et al., 2019). In RDEOs, the case and control numbers are seldom balanced, leading to a higher type 1 error rate, or the identification of false-positive signals. In addition, the inherent rarity of RDEOs prevents recruitment of sufficient numbers of patients for fully powered analyses, except in the special case of differentially methylated CpGs that show strong and consistent disease-dependent effects, making type II error, or the identification of false-negatives, much more likely.

This problem has led to machine learning strategies and use of polyepigenetic predictors for analyzing RDEO DNAm data. For disease diagnosis, many groups focus on identifying and validating episignatures, i.e., polyepigenetic predictors that use a set of CpGs to estimate disease status (Aref-Eshghi et al., 2019; Sadikovic et al., 2019; Turinsky et al., 2020). To date, useful episignatures have been identified in 65 RDEOs (Levy et al., 2022). Similarly, the EpigenCentral web portal has been created to predict RDEO status using an algorithm that combines the classification results of three machine learning algorithms—penalized logistic regression, random forest, and support vector machine—for seven RDEOs as well as ASD and Down syndrome (Turinsky et al., 2020). However, the prediction accuracy for each disease requires additional validation. These and similar methods have the advantage that clustering-based analysis and machine learning-driven predictors avoid multiple testing, and instead focus on disease classification through the combination of smaller, relevant effects.

Other strategies to mitigate the problem of multiple testing include lessons learned from analysis of ChIP-Seq (chromatin immunoprecipitation followed by sequencing) and ATAC-Seq (assay for transposase-accessible chromatin followed by sequencing) data. Here, the problem of power can sometimes be overcome by summarizing test statistics and developing an analysis pipeline that typically involves chromatin state, motif, and enrichment analysis (Bardet et al., 2012; Steinhauser et al., 2016; Park et al., 2017). A similar strategy has been applied to DNAm data, where genomic regions with similar epigenetic regulation are grouped using R packages, such as CoMeBack and DMRcate (Peters et al., 2015; Gatev et al., 2020). These analyses summarize the high-dimensional data into limited values, so instead of hundreds of thousands of tests, only a few are performed.

There is a strong bias regarding which RDEOs are well-characterized and which are understudied (Ekins, 2017). The less common RDEOs are understudied not only due to funding constraints, but also because of the lack of access to samples. Animal models sometimes do not recapitulate the physiology of RDEOs (Berry-Kravis et al., 2016; Lui et al., 2016; Lui et al., 2018), and primary samples are difficult to obtain because of the rarity of these conditions. For example, both homozygous germline TET2 deficiency and HESJAS have been described in only one report each in the literature (Heyn et al., 2019; Stremenova Spegarova et al., 2020). Alternative bioinformatics approaches can be useful to identify dysregulated epigenetic elements in such cases. With the availability of publicly available data sets, and efforts to create reference epigenomes, such as the Encyclopedia of DNA Elements (ENCODE) project and the NIH Roadmap Epigenomics Project, it is becoming increasingly possible to create a reference epigenome for characterization of RDEOs (Kundaje et al., 2015). While the currently available reference epigenomes are diverse in the types of epigenetic marks examined and the tissues from which the data were generated, they nevertheless have small sample sizes and are low in demographic diversity. The International Human Epigenome Consortium (IHEC) web portal coordinates the creation and publication of reference epigenome maps from seven consortia, including ENCODE, Roadmap, and Blueprint (Bujold et al., 2016). At present, 178 whole-genome bisulfite sequencing (WGBS) data sets in primary human blood cells (including a variety of cell types) and 10 data sets in brain are available on IHEC. By comparing small disease cohorts against large healthy control groups, transcriptomics data have been used to pinpoint disease-associated genes in RDs with variants of unknown significance (Frésard et al., 2019; Ferraro et al., 2020). By expanding on the reference epigenome efforts and applying a similar outlier detection method to identify significantly dysregulated elements, future studies will better identify the downstream pathways that contribute to the pathophysiology of ultra-rare RDEOs.

3.4 Complex RDEO spectrum: Unknown gene–disease associations

Further complicating the technical challenges, it can be particularly difficult to elucidate the mechanisms driving RDEOs, as the interaction between genotype and phenotype is often unclear. With the rapid development of whole-genome sequencing technology, it has become increasingly feasible to determine the genetic variants present in patients with aberrant developmental trajectories. However, after sequencing, it can be difficult to determine the variants driving the disease or to delineate the link between the gene of interest and the phenotype in question.

For example, DNMT3A is necessary for de novo DNAm (Lyko, 2018). Differentiated cells derived from Dnmt3a-null mouse hematopoietic stem cells (HSCs) show a global loss of DNAm and deficient repression of HSC-specific genes, demonstrating its role in transcriptional silencing (Challen et al., 2012). These links between DNMT3A LoF and overgrowth phenotype, and between GoF variants and primordial dwarfism can be understood intuitively, but the relations between DNMT3A and brain subfunctions are less well understood. While some studies have demonstrated the necessity of DNMT3A expression for memory formation (LaPlant et al., 2010; Morris et al., 2013; Lavery et al., 2020) and emotional regulation (LaPlant et al., 2010; Elliott et al., 2016; Christian et al., 2020; Lavery et al., 2020), which were further supported by recent findings suggesting a role of non-CpG DNAm dysregulation as a mediator of these effects (Christian et al., 2020; Lavery et al., 2020), the regulatory mechanism has yet to be elucidated. This is mainly because DNMT3A regulates a wide variety of molecular pathways, and changes in its function can sometimes have a cascade effect as DNMT3A targets transcription factors, kinases, or other proteins that in turn exert regulatory functions. Although it is clear that several hundred genes are differentially expressed and methylated in the brains of DNMT3A LoF model mice (Christian et al., 2020; Lavery et al., 2020), the mechanisms underlying the functions of DNMT3A remain to be determined.

To elucidate the mechanisms underlying the regulatory disruptions in patients with RDEOs, it is crucial to identify downstream targets in relevant models and tissues. Targeted epigenomic or transcriptomic profiling in such models can be useful for identifying downstream dysregulated elements. For example, CLIP-Seq (crosslinking immunoprecipitation followed by sequencing) has been used to identify the RNA binding partners of FMRP to study FXS, as FMRP is an mRNA-binding protein. One recent study differentiated four brain cell types consisting of human dorsal and ventral forebrain neural progenitors and neurons, and applied integrated CLIP-Seq and transcriptomic analysis to determine FMRP targets (Li et al., 2020). The results showed that the neurogenesis pathway was consistently disrupted in all four cell types, while cell type-specific differential regulation of genes, such as PIK3CB and SEC24C, was identified and validated in dorsal neurons (Li et al., 2020). Interestingly, upregulation of the catalytic subunit of PI3K, p110b, has been reported to drive deficits in dendritic maturation and cognition in fmr1 knockout mice, and inhibition of p110b reversed the phenotypes associated with FXS (Gross et al., 2010; 2015). These findings were further validated in FXS forebrain organoids, and the results also identified PI3K as the main treatment target (Kang et al., 2021). Taken together, the extensive profiling of FMRP binding partners can generate a list of molecular targets for analysis of their therapeutic potential. This approach can be applied to studying other RDEOs, thus delineating the cascade effects of epigenetic dysregulation and identifying potential therapeutic targets.

3.5 Phenotypic heterogeneity indicates that a range of molecular pathways are affected in RDEOs

The phenotypic and genetic heterogeneity of RDEOs present further challenges in the study of these diseases. In most RDEOs, different patients with genetic variants in the same gene will present a spectrum of clinical phenotypes. Conversely, variants in functionally distinct genes can either lead to the same RDEO or drive similar clinical presentations. While these phenomena complicate the analysis and interpretation of experimental results, a number of tools are available to overcome these challenges. One of the simplest examples illustrating the heterogeneity of RDEO is the apparent reciprocity of the phenotypes associated with GoF versus LoF variants. For example, TBRS and HESJA are driven by LoF and GoF variants of DNMT3A, respectively (Heyn et al., 2019; Smith et al., 2021). The effect of a given variant can be established by a series of functional studies to explore the expression levels of the gene product and downstream target. For example, a global increase in DNAm was demonstrated in germline TET2 LoF disorder, which is consistent with the established function of TET2 in the DNA demethylation pathway (Wu and Zhang, 2017; Stremenova Spegarova et al., 2020).

In addition to the direct contrast of phenotypes attributable to GoF versus LoF variants, the genetic variants can sometimes drive differing phenotypes depending on their location within specific protein domains. For example, variants in two paralogous genes—CREBBP and EP300—are linked to two separate RDEOs; whereas variants outside of exon 30 and 31 of either gene are linked to Rubenstein-Taybi syndrome (RSTS), those within the two exons cause Menke-Hennekam syndrome (MKHK) with distinct clinical phenotypes and DNAm signatures (Bedford et al., 2010; Menke et al., 2016; 2018; Levy et al., 2022). Depending on the functions of the affected domain, variants in the same gene can drive significant phenotypic heterogeneity. Similarly, FLHS is driven by truncation variants in exons 33 and 34 of SRCAP, known as the FLHS locus (Hood et al., 2012; Nikkel et al., 2013; Seifert et al., 2014), whereas non-FLHS SRCAP-related NDD is linked to truncation variants proximal to the FLHS locus (Rots et al., 2021). Although all of the abovementioned SRCAP variants are likely to be non-functional, patients can present with a spectrum of clinical phenotypes, and again functional studies in relevant tissue contexts are warranted to further explore the underlying causes of this phenotypic heterogeneity. The functional differences of the SRCAP variants can drive different epigenome-wide profiles depending on the affected domain. To characterize FLHS SRCAP variants, Greenberg et al. assessed not only the effects of the variants on craniofacial development, but also evaluated the deposition of H2A.Z (a direct downstream target of SRCAP), by ChIP-Seq analyses. FLHS-associated variants were shown to disrupt the nuclear localization of SRCAP and prevent the deposition of H2A.Z.2, a subtype of H2A.Z, demonstrating that the variant is associated with SRCAP LoF (Greenberg et al., 2019). As SRCAP is also a transcriptional activator, RNA-Seq was performed to examine transcriptomic disruption. The application of a similar experimental pipeline to other SRCAP variants may help to resolve the issue of SRCAP-related phenotypic heterogeneity.

Phenotypic heterogeneity can also present as differences in disease onset. While most of the RDEOs discussed above manifest early in life, germline variants of the RDEO-associated genes may also confer a risk of later-onset neurodegenerative diseases. For example, fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disease that affects premutation carriers (55–200 CGG repeats) in the FMR1 gene (Hagerman and Hagerman, 2016; Cabal-Herrera et al., 2020). The symptoms of FXTAS include intention tremor, cerebellar gait ataxia, neuropathic pain, and memory or executive function deficits, which are primarily observed in men older than 50 years (Hagerman and Hagerman, 2016; Cabal-Herrera et al., 2020; Salcedo-Arellano et al., 2020), although heterozygous female premutation carriers may also be affected (Hunter et al., 1998). Interestingly, in contrast to the transcriptional silencing associated with full mutation expansion in FXS, FMR1 mRNA is transcriptionally upregulated by 2–8-fold in patients with FXTAS in comparison to healthy controls (Tassone et al., 2000; 2007). However, this upregulation is accompanied by translational defects, resulting in a decreased FMRP protein level, which is negatively correlated with the number of CGG repeats (Kenneson, 2001; Iliff et al., 2013; Schneider et al., 2020). The sex-specific presentation of the disease indicates that the decreased FMRP dosage contributes to the disease phenotype (Cabal-Herrera et al., 2020). In addition, while the FXTAS variants do not functionally impair early development, the delayed onset also suggests that they cause an accumulation of molecular defects in alternative pathways resulting in neurodegeneration later in life.

Other RDEO-associated genes have also been implicated in neurodegenerative disorders. A recent study showed that rare LoF or non-coding TET2 variants were significantly enriched in populations with early-onset Alzheimer’s disease (EOAD) and frontotemporal dementia (FTD) (Cochran et al., 2020). However, Tet2 loss has also been shown to be neuroprotective in a mouse model of Parkinson’s disease (PD) (Marshall et al., 2020). These conflicting results may be due to differences in experimental setup between studies, or the homeostasis of DNAm regulation may be crucial for brain health. Similarly, DNMT3B and SRCAP variants have also been linked to neurodegenerative disorders, including Alzheimer’s disease (AD), PD, and amyotrophic lateral sclerosis (ALS) (Chesi et al., 2013; de Bem et al., 2016; Chen et al., 2017; Pezzi et al., 2017; Vardarajan et al., 2017). Defects in the DNA damage response have been proposed as potential mechanisms underlying FXTAS (Hagerman and Hagerman, 2016), as DNAm damage and oxidative stress have been shown to induce cellular senescence and neurodegeneration (Coppedè and Migliore, 2015; Martínez-Cué and Rueda, 2020; Gonzalez-Hunt and Sanders, 2021). As FMRPs (Alpatov et al., 2014), TET2 (Feng et al., 2019), DNMT3B (Jin and Robertson, 2013; Shih et al., 2022), and SRCAP (Dong et al., 2014) are all involved in the DNA damage response, the associations between variants in these genes and neurodegenerative disorders may be mediated by altered DNA damaged responses, although further research is needed to establish this potentially shared pathway.

These observations highlight the functional importance of these genes and the harmful effects of associated epigenetic dysregulation. This dysregulation may manifest as epigenetic drift, a phenomenon where the epigenetic profile becomes increasingly variable with age as the epigenetic machinery fails to faithfully maintain regulation through mitosis (Jones et al., 2015; Hernando-Herraez et al., 2019; Bergstedt et al., 2022). It is plausible that variants in key epigenetic regulators exacerbate the rate at which this deterioration occurs. The variants can drive cellular changes that may not translate to clinical symptoms early in life but become apparent at later stages. For individuals with deleterious variants in these genes, biomarkers such as epigenetic clocks—bioinformatics predictors of the biological aging process (Horvath, 2013; Jones et al., 2015; Levine et al., 2018)—may be useful for monitoring disease progression. Acceleration of age-related epigenetic changes was shown to be associated with a diagnosis of ASD using an epigenetic clock trained specifically for children, thereby demonstrating the utility of the clock for detecting altered developmental trajectories in diseases (McEwen et al., 2020). Epigenetic clocks trained in adults can be used to detect aging trajectories later in life (Levine et al., 2018; Lu et al., 2019) and to monitor the processes of epigenetic dysregulation in individuals carrying damaging variants in key epigenetic regulators. This bioinformatics tool should be considered in future RDEO studies across the age spectrum.

3.6 Genetic heterogeneity of RDEOs May inform shared targets of distinct genes

Finally, genetic heterogeneity is also commonly observed in RDEOs. Prior to the availability of sequencing technologies to detect disease subtypes with unique genetic origins, diseases such as ICF disorders and PRC2-related overgrowth syndromes were classified as single entities due to the overlap in their clinical phenotypes. In the case of ICF, the functional links between genes driving different subtypes of RDEO are unclear. In ICF, the interactions among drivers of ICF2–4 have been well characterized, while their relations with DNMT3B, the driver of ICF1, remain unknown (Wu et al., 2016; Jenness et al., 2018). ChIP-Seq analyses showed that ICF1 and ICF2-associated variants colocalized to a similar set of genes, which are enriched in pathways involving cellular maintenance, DNA repair, and telomere function (Thompson et al., 2018). The results further suggest that ZBTB24 is necessary for loading of DNMT3B onto DNA (Thompson et al., 2018). While additional studies are required to further characterize these interactions, these observations showed that the identification of shared binding partners and dysregulated effects can be used to probe functional overlap in RDEOs with genetic heterogeneity.

In contrast, genes associated with PRC2-related overgrowth syndromes all encode components of PRC2 (van Mierlo et al., 2019), with defects in each component preventing the normal functioning of the complex. PRC2 subunits have highly coordinated functions: EZH1/2 binds to histone targets and acts as the main catalytic unit (Margueron et al., 2008; Jani et al., 2019); EED provides the epigenetic reader function, propagating transcriptional repression by binding to nucleosomes with H3K27me3 and H3K9me3 and enhancing EZH2 activity (Margueron et al., 2009; Antonysamy et al., 2013; Wu et al., 2013); SUZ12 both stabilizes and recruits PRC2 to chromatin (Choi et al., 2017; Højfeldt et al., 2018); RBBP4/7 are necessary for binding of PRC2 to genomic regions without preexisting H3K27 methylation (Schuettengruber et al., 2017; van Mierlo et al., 2019). Regardless of the distinct roles of each member of core PRC2, they are all essential for its function. Furthermore, PRC2-associated transcriptional regulation involves interactions that extend to other proteins or complexes, including PRC1 (Yu J.-R. et al., 2019), PR-DUB complex (Abdel-Wahab et al., 2012, 2; Balasubramani et al., 2015), and the PRC2 accessory proteins (Oksuz et al., 2018; Glancy et al., 2021). Variants in genes encoding or regulating these elements can also alter the functions of PRC2, and lead to clinical symptoms involving similar pathways to PRC2-related overgrowth syndromes (Gamu and Gibson, 2020). Given that epigenetic regulatory mechanisms are highly interconnected, from DNA and histone modifications to chromatin organization (Cedar and Bergman, 2009; Velasco et al., 2021; Janssen and Lorincz, 2022), it may be relevant to focus on the study of RDEO genes with shared functions. Algorithms can also contribute to the identification of RDEOs with shared clinical and molecular characteristics. GestaltMatcher, for example, uses a deep convolution neural network to classify RDs based on patient‛s facial phenotypes (Hsieh et al., 2022). Data from patients with the same syndromes but different underlying genetic causes were shown to cluster together; for example, subtypes of Kabuki syndrome did not form distinct clusters (Hsieh et al., 2022). Such algorithms can be used to identify RDEOs with overlapping molecular origins based on clinical features. With accumulation of findings regarding related genes and the application of network analyses, the commonality of downstream targets and key regulators should provide valuable insights into the molecular bases of RDEOs.

4 Extending the RDEO findings to common complex diseases

Rare monogenic diseases provide unique insights into the functions of the affected genes and the mechanisms underlying the disorders, and this knowledge can be applied to understanding common complex diseases. It can be difficult to study common diseases with complex etiologies, as disease risk is driven by a combination of genetic, environmental, and lifestyle factors. The existence of monogenic diseases, such as RDEOs, suggests that a key gene and its associated molecular pathways have crucial pathophysiological roles, thus focusing research on the gene and pathway with translation of relevant findings to common diseases. Schizophrenia, for example, is a common psychiatric disorder with an array of factors that contribute to its pathogenesis, ranging from genetics, prenatal complications, lifetime adversity, and substance use (McCutcheon et al., 2020). The monogenic nature of KMT2F-associated schizophrenia, however, informs the relevance of H3K4 methylation to the pathophysiology of schizophrenia. Furthermore, a genome-wide association study highlighted the link between genetic variants in the H3K4 methylation pathway and schizophrenia status, validating these molecular relations outside of the context of rare damaging variants (Nesbit et al., 2021). H3K4 methylation pattern is tightly linked to human glial cell differentiation (Shulha et al., 2013). Microglia are a type of glial cells that are responsible for immune defense and maintenance of the central nervous system (Ginhoux et al., 2013). They also regulate synaptic pruning, a process that is crucial for reorganization of the brain connectomes and healthy brain function (Sowell et al., 2003; Paolicelli et al., 2011; Sakai, 2020). Samples from schizophrenia patients show increased microglial activity, accompanied by aberrant synaptic pruning and lowered dendritic spine density (Glausier and Lewis, 2013; Sellgren et al., 2019). However, iPSC-derived neurons with KMT2F-associated schizophrenia variants showed increased dendritic length and complexity (Wang et al., 2022). The cellular phenotype resembles that of ASD (Hutsler and Zhang, 2010; Varghese et al., 2017; Weir et al., 2018), another common disorder that is often caused by altered H3K4 methylation (Shen et al., 2014; Collins et al., 2019). Despite conflicting findings, altered H3K4 methylation has consistently been shown to induce aberrant pruning activity and dendritic structure (Sellgren et al., 2019; Chen et al., 2022; Wang et al., 2022). Further exploration of the roles of H3K4 modifications in microglial function, dendritic pruning, brain network activity, and memory formation and retrieval will increase our understanding of not only KMT2F-associated schizophrenia, but also other forms of schizophrenia and ASD. Similarly, exploring the molecular basis of TET2-associated immunodeficiency and TBRS, both of which are strongly linked to increased risk of hematopoietic malignancy, will help to elucidate the roles of TET2 and DNMT3A mutations as drivers of cancer. These RDEOs can shed light on the function of a given gene in specific tissues throughout the developmental trajectory, thus providing a foundation for anchoring research regarding common complex diseases.

5 Future directions

Several novel approaches have been developed to address the technical limitations of studying RDEOs and the complexities of these diseases (Figure 2). After identification of variants associated with RDEOs, basic research is required to characterize the molecules and mechanisms underlying the disease phenotypes. To address the problems of limited sample availability and cell type specificity, statistical tools and single-cell experiments in relevant tissues can be applied to facilitate detailed analysis. Early diagnosis using epigenome-wide profiles can facilitate the implementation of interventions through behavioral therapy, specialized learning programs, and individualized medicine. Epigenetic clocks may also be useful if applied to estimate cellular senescence and monitor disease progression in individuals with susceptible variants. Research on RDEOs can be further applied to understanding the roles of a single gene or factors that modify its activity in common complex diseases. The application of multidisciplinary research to RDEOs will facilitate the development of evidence-based treatments and management solutions. The development of tools such as the Matchmaker Exchange, DeepGestalt, and GestaltMatcher, which connect investigators studying the same genes or similar clinical phenotypes (Sobreira et al., 2015; Gurovich et al., 2019; Boycott et al., 2022; Hsieh et al., 2022), and efforts such as the Rare Diseases Models and Mechanism Network (Boycott et al., 2020) to connect basic and clinical researchers working on the same RDs will facilitate the investigation of RDEOs. In addition, health initiatives, such as national birth registries with postnatal dried blood spot collection or amniocentesis in individuals at higher risk, can facilitate early screening for RDEOs. This review highlighted the need to create a community of clinicians, patients, and researchers for the multidisciplinary study of RDEOs.

FIGURE 2.

Challenges (left panel) and opportunities (right panel) in RDEO research. (A) Technical challenges include small sample size and low statistical power, cell type heterogeneity, and specificities of developmental time points. Example solutions include a reference epigenome, single-cell epigenomic profiling, and relevant RDEO models, such as animal models, primary samples, iPSCs, and organoids. (B) RDEOs often present with complex disease–phenotype relations, as the variants driving the RDEOs can drive widespread epigenetic dysregulation. Approaches such as RNA-Seq and EWAS can help to identify affected downstream elements and allow investigation of the mechanisms underlying the RDEO phenotype. (C) RDEOs commonly present with genetic and phenotypic heterogeneity. As an example of RDEO genetic heterogeneity, PRC2-related overgrowth syndromes are a group of RDEOs with a similar clinical phenotype all of which are linked to variants in PRC2. In contrast, patients with the same RDEO and variants in the same gene can have a range of clinical symptoms. Studies of interacting proteins in the same pathway can help to delineate the complexity of genetic heterogeneity, and tools such as the epigenetic clock may be applied to evaluate or monitor phenotypic heterogeneity. Abbreviations: CpGs, cytosine-guanine dinucleotides; DNAm, DNA methylation; RDEO, rare disease of epigenetic origin; EWAS, epigenome-wide association study; iPSC, induced pluripotent stem cell; PRC2, Polycomb repressive complex 2; RNA-Seq, RNA sequencing.

Funding Statement

MK was supported by a grant from the Canadian Institutes of Health Research [EGM-141897] and is the Edwin S.H. Leong UBC Chair in Healthy Aging. ST was supported by grants from the Canadian Institutes of Health Research and BC Children’s Hospital Foundation. WG is supported by a BCCHRI IGAP award and CIHR Project Grant PJT-168982. SET holds a Tier one Canada Research Chair in Pediatric Precision Health and the Aubrey J. Tingle Professor of Pediatric Immunology.

Author contributions

MF, SM, MS, WG, ST, and MK: conceptualization. ST and MK: funding acquisition. MF: writing—original draft preparation. MF, SM, MS, WG, ST, and MK: writing—review and editing. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Glossary

Technologies for assaying molecular signals in rare disease research

ATAC-Seq (assay for transposase-accessible chromatin followed by sequencing)

NGS technology used to identify accessible DNA regions by probing for open chromatin regions

ATAC-Seq is used to assess genome-wide chromatin accessibility in biological samples ChIP-Seq (chromatin immunoprecipitation followed by sequencing)

NGS-based method to identify binding sites for DNA-associated proteins. The method involves crosslinking of DNA–protein complexes, precipitation of these complexes using an antibody against the protein of interest, and recovery of DNA fragments for sequencing

CLIP-Seq (crosslinking immunoprecipitation followed by sequencing)

NGS-based method to identify binding sites of RNA-binding proteins. This method involves the in vivo crosslinking of RNA–protein complexes, precipitation of these complexes, and recovery of the RNA fragments for sequencing

RNA-Seq (RNA sequencing)

NGS technology is used to detect and quantify mRNA molecules in biological samples

Single-cell sequencing

NGS method to assess sequencing information from individual cells. This involves the isolation of single cells before the recovery of DNA or RNA and amplification of the material for sequencing. Examples of single-cell sequencing technologies include single-cell RNA-Seq (scRNA-Seq), single-cell ATAC-Seq (scATAC-Seq), and single-cell CHIP-Seq (scCHIP-Seq)

DNAm microarray

High-throughput microarray for characterization of DNAm of bulk tissue after bisulfite conversion of unmodified cytosine residues to uracil. Converted DNA fragments are hybridized to probes on the array, followed by single-base extension of fluorescently tagged probes. The fluorescence pattern provides information of the level of DNAm at a given locus.