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intra- and extra cellular (environmental) clues. This experience-dependent neural plasticity is particularly
[58]
high during development . Therefore, it is not surprising that in addition to genetic factors, the
environment has particular influence during gestation or the early postnatal period and both contribute to
the development of ID. Examples of such environmental factors contributing to ID include cerebrovascular
incidents associated with premature birth or perinatal asphyxia, prenatal exposure to neurodevelopmental
toxins or bacterial and viral infections, maternal conditions such as diabetes, phenylketonuria and immune
system alterations, malnutrition (of both mother and child) and specific deficiencies such as that of iodine.
Some of these ID-contributing environmental factors affect normal neurodevelopment directly by inducing
genetic mutations, enhancing cell death, inhibiting differentiation processes and blocking the activity of key
developmental proteins. However, the effects of the vast majority of environmental factors involve gene-
environment interactions that drive long-lasting neural and behavioral changes. Currently, these effects are
strongly linked with epigenetic changes elicited by environmental factors. For example, emerging evidence
[59]
suggests that environmental perturbations can alter DNA methylation patterns in the developing brain ,
leading to the currently prevailing theory that changes in the brain methylome likely contribute to the
pathogenesis of ID.

Aberrant DNA methylation (induced by environmental factors, stochastically arisen or resulting from an
underlying change in DNA sequence) that leads to dysregulated genome function, affecting genes relevant
for neurodevelopment and brain plasticity can potentially cause ID. These genomic (epi) variations are
missed by conventional sequencing approaches and can potentially underlie a considerable fraction of
genetically undiagnosed ID cases. Recently, array-based methylation profiling of a large cohort of patients
[60]
with neurodevelopmental disorders identified rare epigenetic changes in ~20% of patients . These changes
were absent in thousands of controls, repeatedly identified in unrelated patients and located in promoters
of known NDD genes, suggesting that abnormal methylation contributes to the phenotype of the patients.
Further support for this hypothesis came from findings that epivariations in gene promoters were often
associated with changes in gene expression, some of which were so extreme as to mimic the loss of function
coding mutations. Thus, the search for epivariations should be considered as a complementary, molecular
[61]
diagnostic tool in patients with genetically unexplained ID .

Consistent with epigenetic mechanisms underlying the development of the brain and cognitive phenotypes,
amongst the hundreds of genes already identified as contributing to ID, a large number of them encode
for epigenetic regulators [62-65] . Neurodevelopmental disorders exhibiting ID such as Rett syndrome
(OMIM #312750 ), ATR-X (OMIM #301040), Kleefstra syndrome (OMIM #610253), Fragile X (OMIM
#300624), X-linked syndromic ID (XLID) Claes-Jensen type (OMIM #300534) and Rubinstein-Taybi
syndrome (OMIM #180849) are caused by mutations in chromatin-remodeling proteins (ATRX in ATR-X),
transcriptional regulators (MeCP2 in Rett syndrome and CREBBP/CBP in Rubinstein-Taybi syndrome) and
histone modifiers (EHMT1 in Kleefstra syndrome and KDM5C in XLID). Further, mutations in the DNA
methyltransferase gene DNMT3A and HIST1H1E, encoding histone H1.4 were shown to cause ID [66,67] .

In summary, generating genotype-phenotype correlations for ID is incredibly complex. This is due in
part to the confounding effect of phenotypic and etiologic heterogeneity, along with the rare and variable
penetrant nature of the underlying risk variants identified so far . One consequence of this complexity
[68]
is the application of artificial intelligence (AI) for precision medicine in neurodevelopmental disorders,
including ID, autism spectrum disorder and epilepsy, which is still far from accurate. Larger sample
sizes and broader (in terms of technologies) studies are expected to allow identification of the relative
contributions of each gene/loci to different, but overlapping and highly correlated phenotypes related to
ID, such as intelligence quotient (IQ), educational attainment, schizophrenia and depression among others.
Finally, increasing data availability will also allow for the development of phenotype specific polygenic risk
[69]
scores (PRS) .