A genome-wide spectrum of tandem repeat expansions in 338,963 humans.

The Genome Aggregation Database (gnomAD), widely recognized as the gold-standard reference map of human genetic variation, has largely overlooked tandem repeat (TR) expansions, despite the fact that TRs constitute ∼6% of our genome and are linked to over 50 human diseases. Here, we introduce the TR-gnomAD (https://wlcb.oit.uci.edu/TRgnomAD), a biobank-scale reference of 0.86 million TRs derived from 338,963 whole-genome sequencing (WGS) samples of diverse ancestries (39.5% non-European samples). TR-gnomAD offers critical insights into ancestry-specific disease prevalence using disparities in TR unit number frequencies among ancestries. Moreover, TR-gnomAD is able to differentiate between common, presumably benign TR expansions, which are prevalent in TR-gnomAD, from those potentially pathogenic TR expansions, which are found more frequently in disease groups than within TR-gnomAD. Together, TR-gnomAD is an invaluable resource for researchers and physicians to interpret TR expansions in individuals with genetic diseases.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward.

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction.

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

A review of SNP heritability estimation methods.

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.

Rare variations within the serine/arginine-rich splicing factor PtoRSZ21 modulate stomatal size to determine drought tolerance in Populus.

Rare variants contribute significantly to the 'missing heritability' of quantitative traits. The genome-wide characteristics of rare variants and their roles in environmental adaptation of woody plants remain unexplored. Utilizing genome-wide rare variant association study (RVAS), expression quantitative trait loci (eQTL) mapping, genetic transformation, and molecular experiments, we explored the impact of rare variants on stomatal morphology and drought adaptation in Populus. Through comparative analysis of five world-wide Populus species, we observed the influence of mutational bias and adaptive selection on the distribution of rare variants. RVAS identified 75 candidate genes correlated with stomatal size (SS)/stomatal density (SD), and a rare haplotype in the promoter of serine/arginine-rich splicing factor PtoRSZ21 emerged as the foremost association signal governing SS. As a positive regulator of drought tolerance, PtoRSZ21 can recruit the core splicing factor PtoU1-70K to regulate alternative splicing (AS) of PtoATG2b (autophagy-related 2). The rare haplotype PtoRSZ21hap2 weakens binding affinity to PtoMYB61, consequently affecting PtoRSZ21 expression and SS, ultimately resulting in differential distribution of Populus accessions in arid and humid climates. This study enhances the understanding of regulatory mechanisms that underlie AS induced by rare variants and might provide targets for drought-tolerant varieties breeding in Populus.

Therapeutic drug monitoring, liquid biopsies or pharmacogenomics for prediction of human drug metabolism and response.

Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes.

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease.

Albinism is characterized by a variable degree of hypopigmentation affecting the skin and the hair, and causing ophthalmologic abnormalities. Its oculocutaneous, ocular and syndromic forms follow an autosomal or X-linked recessive mode of inheritance, and 22 disease-causing genes are implicated in their development. Our aim was to clarify the genetic background of a Hungarian albinism cohort. Using a 22-gene albinism panel, the genetic background of 11 of the 17 Hungarian patients was elucidated. In patients with unidentified genetic backgrounds (n = 6), whole exome sequencing was performed. Our investigations revealed a novel, previously unreported rare variant (N687S) of the two-pore channel two gene (TPCN2). The N687S variant of the encoded TPC2 protein is carried by a 15-year-old Hungarian male albinism patient and his clinically unaffected mother. Our segregational analysis and in vitro functional experiments suggest that the detected novel rare TPCN2 variant alone is not a disease-causing variant in albinism. Deep genetic analyses of the family revealed that the patient also carries a phenotype-modifying R305W variant of the OCA2 protein, and he is the only family member harboring this genotype. Our results raise the possibility that this digenic combination might contribute to the observed differences between the patient and the mother, and found the genetic background of the disease in his case.

Whole-Exome Sequencing Identified Two Novel Pathogenic Mutations in the PTCH1 Gene in BCNS.

Basal cell nevus syndrome (BCNS, OMIM 109400) is a familial cancer syndrome characterized by the development of numerous basal cell cancers and various other developmental abnormalities, including epidermal cysts of the skin, calcified dural folds, keratocysts of the jaw, palmar and plantar pits, ovarian fibromas, medulloblastomas, lymphomesenteric cysts, and fetal rhabdomyomas. BCNS shows autosomal dominant inheritance and is caused by mutations in the patched 1 (PTCH1) gene and the suppressor of the fused homolog (SUFU) gene. In a few cases, variants of patched 2 (PTCH2) have been found in patients who met the criteria for BCNS. In an investigation of 11 Hungarian families who fulfilled the diagnostic criteria for BCNS, whole-exome sequencing (WES) and multiplex ligation-dependent probe amplification (MLPA) identified two novel pathogenic variants (c.2994C>A; p.Cys998Ter and c.814_818del; p.Asn272SerfsTer11), one recently identified variant (c.1737_1745del p.Val580_Val582del), and three recurrent disease-causing variants of the PTCH1 gene with a diagnosis rate of 63.6%. Disease-causing variants were not found for the SUFU and PTCH2 genes. These applied methods could not fully elucidate the genetic background of all the BCNS cases that we investigated. To uncover the missing heritability of BCNS, whole-genome sequencing or an epigenetic approach might be considered in the future.

Heritability jointly explained by host genotype and microbiome: will improve traits prediction?

As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.

Genetic nurturing, missing heritability, and causal analysis in genetic statistics.

Genetic nurturing, the effect of parents' genotypes on offspring phenotypes through parental phenotypic transmission, can be modeled in terms of gene-culture interactions. This paper first uses a simple one-locus, two-phenotype gene-culture cotransmission model to compute the effect of genetic nurturing in terms of regression of children's phenotypes on transmitted and nontransmitted alleles. With genetic nurturing, interpreting heritability and hence the meaning of "missing heritability" becomes problematic. Other factors, for example, population subdivision and assortative mating, generate similar signals to those of genetic nurturing, namely, correlation between parents' nontransmitted alleles and children's phenotypes. Corrections must be made for these to isolate the signal of genetic nurturing. Finally, a unified causal framework is constructed for genetic nurturing, population subdivision, and assortative mating. Causal and noncausal paths from transmitted and nontransmitted alleles to children's phenotypes are identified and investigated in the presence of genetic nurturing, population subdivision, and assortative mating. Using causal analysis, assumptions made in inferring direct and indirect effects are then clarified and evaluated in a broader causal context.

Whole genome sequencing and inheritance-based variant filtering as a tool for unraveling missing heritability in pediatric cancer.

Survival rates for pediatric cancer have significantly increased the past decades, now exceeding 70-80% for most cancer types. The cause of cancer in children and adolescents remains largely unknown and a genetic susceptibility is considered in up to 10% of the cases, but most likely this is an underestimation. Families with multiple pediatric cancer patients are rare and strongly suggestive for an underlying predisposition to cancer. The absence of identifiable mutations in known cancer predisposing genes in such families could indicate undiscovered heritability. To discover candidate susceptibility variants, whole genome sequencing was performed on germline DNA of a family with two children affected by Burkitt lymphoma. Using an inheritance-based filtering approach, 18 correctly segregating coding variants were prioritized without a biased focus on specific genes or variants. Two variants in FAT4 and DCHS2 were highlighted, both involved in the Hippo signaling pathway, which controls tissue growth and stem cell activity. Similarly, a set of nine non-coding variants was prioritized, which might contribute, in differing degrees, to the increased cancer risk within this family. In conclusion, inheritance-based whole genome sequencing in selected families or cases is a valuable approach to prioritize variants and, thus, to further unravel genetic predisposition in childhood cancer.

The enhancer rare germline variation rs548071605 contributes to lung cancer development.

Rare germline variations contribute to the missing heritability of human complex diseases including cancers. Given their very low frequency, discovering and testing disease-causing rare germline variations remains challenging. The tag-single nucleotide polymorphism rs17728461 in 22q12.2 is highly associated with lung cancer risk. Here, we identified a functional rare germline variation rs548071605 (A>G) in a p65-responsive enhancer located within 22q12.2. The enhancer significantly promoted lung cancer cell proliferation in vitro and in a xenograft mouse model by upregulating the leukemia inhibitory factor (LIF) gene via the formation of a chromatin loop. Differential expression of LIF and its significant correlation with first progression survival time of patients further supported the lung cancer-driving effects of the 22q-Enh enhancer. Importantly, the rare variation was harbored in the p65 binding sequence and dramatically increased the enhancer activity by increasing responsiveness of the enhancer to p65 and B-cell lymphoma 3 protein, an oncoprotein that assisted the p65 binding. Our study revealed a regulatory rare germline variation with a potential lung cancer-driving role in the 22q12.2 risk region, providing intriguing clues for investigating the "missing heritability" of cancers, and also offered a useful experimental model for identifying causal rare variations.

Detecting X-linked common and rare variant effects in family-based sequencing studies.

The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.

Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability.

BACKGROUND: Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders. METHODS: Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed. RESULTS: The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a "long-with-young" and "short-with-all" basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10-10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs. CONCLUSIONS: In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential "Common Disease-Hotspot Variant" approach to the missing heritability problem.

The missing heritability in type 1 diabetes.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine.

The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered "third source" of phenotypic variation, beside genotype and environment, and argue that "missing heritability" for some complex diseases is likely to be a case of "diluted heritability". There is a need for radically new ways of thinking about the principles of genotype-phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.

Missing heritability in Bloom syndrome: First report of a deep intronic variant leading to pseudo-exon activation in the BLM gene.

Pathogenic biallelic variants in the BLM/RECQL3 gene cause a rare autosomal recessive disorder called Bloom syndrome (BS). This syndrome is characterized by severe growth delay, immunodeficiency, dermatological manifestations and a predisposition to a wide variety of cancers, often multiple and very early in life. Literature shows that the main mode of BLM inactivation is protein translation termination. We expanded the molecular spectrum of BS by reporting the first deep intronic variant causing intron exonisation. We describe a patient with a clinical phenotype of BS and a strong increase in sister chromatid exchanges (SCE), who was found to be compound heterozygous for a novel nonsense variant c.3379C>T, p.(Gln1127Ter) in exon 18 and a deep intronic variant c.3020-258A>G in intron 15 of the BLM gene. The deep intronic variant creates a high-quality de novo donor splice site, which leads to retention of two intron segments. Both pseudo-exons introduce a premature stop codon into the reading frame and abolish BLM protein expression, confirmed by Western Blot analysis. These findings illustrate the role of non-coding variation in Mendelian disorders and herewith highlight an unmet need in routine testing of Mendelian disorders, being the added value of RNA-based approaches to provide a complete molecular diagnosis.

Genomics of Endometriosis: From Genome Wide Association Studies to Exome Sequencing.

This review aims at better understanding the genetics of endometriosis. Endometriosis is a frequent feminine disease, affecting up to 10% of women, and characterized by pain and infertility. In the most accepted hypothesis, endometriosis is caused by the implantation of uterine tissue at ectopic abdominal places, originating from retrograde menses. Despite the obvious genetic complexity of the disease, analysis of sibs has allowed heritability estimation of endometriosis at ~50%. From 2010, large Genome Wide Association Studies (GWAS), aimed at identifying the genes and loci underlying this genetic determinism. Some of these loci were confirmed in other populations and replication studies, some new loci were also found through meta-analyses using pooled samples. For two loci on chromosomes 1 (near CCD42) and chromosome 9 (near CDKN2A), functional explanations of the SNP (Single Nucleotide Polymorphism) effects have been more thoroughly studied. While a handful of chromosome regions and genes have clearly been identified and statistically demonstrated as at-risk for the disease, only a small part of the heritability is explained (missing heritability). Some attempts of exome sequencing started to identify additional genes from families or populations, but are still scarce. The solution may reside inside a combined effort: increasing the size of the GWAS designs, better categorize the clinical forms of the disease before analyzing genome-wide polymorphisms, and generalizing exome sequencing ventures. We try here to provide a vision of what we have and what we should obtain to completely elucidate the genetics of this complex disease.

The Somatic Mutation Paradigm in Congenital Malformations: Hirschsprung Disease as a Model.

Patients with Hirschsprung disease (HSCR) do not always receive a genetic diagnosis after routine screening in clinical practice. One of the reasons for this could be that the causal mutation is not present in the cell types that are usually tested-whole blood, dermal fibroblasts or saliva-but is only in the affected tissue. Such mutations are called somatic, and can occur in a given cell at any stage of development after conception. They will then be present in all subsequent daughter cells. Here, we investigated the presence of somatic mutations in HSCR patients. For this, whole-exome sequencing and copy number analysis were performed in DNA isolated from purified enteric neural crest cells (ENCCs) and blood or fibroblasts of the same patient. Variants identified were subsequently validated by Sanger sequencing. Several somatic variants were identified in all patients, but causative mutations for HSCR were not specifically identified in the ENCCs of these patients. Larger copy number variants were also not found to be specific to ENCCs. Therefore, we believe that somatic mutations are unlikely to be identified, if causative for HSCR. Here, we postulate various modes of development following the occurrence of a somatic mutation, to describe the challenges in detecting such mutations, and hypothesize how somatic mutations may contribute to 'missing heritability' in developmental defects.