Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases

Assessing concordance among human, in silico predictions and functional assays on genetic variant classification

MOTIVATION

A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay.

RESULTS

We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants.

AVAILABILITY AND IMPLEMENTATION

All source code of AIVAR is deposited and freely available at https://github.com/TopGene/AIvar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Selective breeding modifies mef2ca mutant incomplete penetrance by tuning the opposing Notch pathway

Deleterious genetic mutations allow developmental biologists to understand how genes control development. However, not all loss of function genetic mutants develop phenotypic changes. Many deleterious mutations only produce a phenotype in a subset of mutant individuals, a phenomenon known as incomplete penetrance. Incomplete penetrance can confound analyses of gene function and our understanding of this widespread phenomenon remains inadequate. To better understand what controls penetrance, we capitalized on the zebrafish mef2ca mutant which produces craniofacial phenotypes with variable penetrance. Starting with a characterized mef2ca loss of function mutant allele, we used classical selective breeding methods to generate zebrafish strains in which mutant-associated phenotypes consistently appear with low or high penetrance. Strikingly, our selective breeding for low penetrance converted the mef2ca mutant allele behavior from homozygous lethal to homozygous viable. Meanwhile, selective breeding for high penetrance converted the mef2ca mutant allele from fully recessive to partially dominant. Comparing the selectively-bred low- and high-penetrance strains revealed that the strains initially respond similarly to the mutation, but then gene expression differences between strains emerge during development. Thus, altered temporal genetic circuitry can manifest through selective pressure to modify mutant penetrance. Specifically, we demonstrate differences in Notch signaling between strains, and further show that experimental manipulation of the Notch pathway phenocopies penetrance changes occurring through selective breeding. This study provides evidence that penetrance is inherited as a liability-threshold trait. Our finding that vertebrate animals can overcome a deleterious mutation by tuning genetic circuitry complements other reported mechanisms of overcoming deleterious mutations such as transcriptional adaptation of compensatory genes, alternative mRNA splicing, and maternal deposition of wild-type transcripts, which are not observed in our system. The selective breeding approach and the resultant genetic circuitry change we uncovered advances and expands our current understanding of genetic and developmental resilience.

Some deleterious gene mutations only affect a subset of genetically mutant animals. This widespread phenomenon, known as mutant incomplete penetrance, complicates discovery of causative gene mutations in both model organisms and human disease. This study utilized the zebrafish mef2ca transcription factor mutant that produces craniofacial skeleton defects with incomplete penetrance. Selectively breeding zebrafish families for low- or high-penetrance mutants for many generations created different zebrafish strains with consistently low or high penetrance. Comparing these strains allowed us to gain insight into the mechanisms that control penetrance. Specifically, genes under the control of mef2ca are initially similarly expressed between the two strains, but differences between strains emerge during development. We found that genetic manipulation of these downstream genes mimics the effects of our selective breeding. Thus, selective breeding for penetrance can change the genetic circuitry downstream of the mutated gene. We propose that small differences in gene circuitry between individuals is one mechanism underlying susceptibility or resilience to genetic mutations.

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.

Can Genetic Testing Identify Talent for Sport?

Elite athlete status is a partially heritable trait, as are many of the underpinning physiological, anthropometrical, and psychological traits that contribute to elite performance. In recent years, our understanding of the specific genetic variants that contribute to these traits has grown, such that there is considerable interest in attempting to utilise genetic information as a tool to predict future elite athlete status. In this review, we explore the extent of the genetic influence on the making of a sporting champion and we describe issues which, at present, hamper the utility of genetic testing in identifying future elite performers. We build on this by exploring what further knowledge is required to enhance this process, including a reflection on the potential learnings from the use of genetics as a disease prediction tool. Finally, we discuss ways in which genetic information may hold utility within elite sport in the future, including guiding nutritional and training recommendations, and assisting in the prevention of injury. Whilst genetic testing has the potential to assist in the identification of future talented performers, genetic tests should be combined with other tools to obtain an accurate identification of those athletes predisposed to succeed in sport. The use of total genotype scores, composed of a high number of performance-enhancing polymorphisms, will likely be one of the best strategies in the utilisation of genetic information to identify talent in sport.

Detection of homozygous genotypes for a putatively lethal recessive mutation in the porcine argininosuccinate synthase 1 (ASS1) gene

The sequencing of the pig genome revealed the existence of homozygous individuals for a nonsense mutation in the argininosuccinate synthase 1 (ASS1) gene (rs81212146, c.944T>A, L315X). Paradoxically, an AA homozygous genotype for this polymorphism is expected to abolish the function of the ASS1 enzyme that participates in the urea cycle, leading to citrullinemia, hyperammonemia, coma and death. Sequencing of five Duroc boars that sired a population of 350 Duroc barrows revealed the segregation of the c.944T>A polymorphism, so we aimed to investigate its phenotypic consequences. Genotyping of this mutation in the 350 Duroc barrows revealed the existence of seven individuals homozygous (AA) for the nonsense mutation. These AA pigs had a normal weight despite the fact that mild citrullinemia often involves impaired growth. Sequencing of the region surrounding the mutation in TT, TA and AA individuals revealed that the A substitution in the second position of the codon (c.944T>A) is in complete linkage disequilibrium with a C replacement (c.943T>C) in the first position of the codon. This second mutation would compensate for the potentially damaging effect of the c.944T>A replacement. In fact, this is the most probable reason why pigs with homozygous AA genotypes at the 944 site of the ASS1 coding region are alive. Our results illustrate the complexities of predicting the consequences of nonsense mutations on gene function and phenotypes, not only because of annotation issues but also owing to the existence of genetic mechanisms that sometimes limit the penetrance of highly harmful mutations.

Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.

Systems genetics applications in metabolism research

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

Optimizing clinical exome design and parallel gene-testing for recessive genetic conditions in preconception carrier screening: Translational research genomic data from 14,125 exomes

Limited translational genomic research data have been reported on the application of exome sequencing and parallel gene testing for preconception carrier screening (PCS). Here, we present individual-level data from a large PCS program in which exome sequencing was routinely performed on either gamete donors (5,845) or infertile patients (8,280) undergoing in vitro fertilization (IVF) treatment without any known family history of inheritable genetic conditions. Individual-level data on pathogenic variants were used to define conditions for PCS based on criteria for severity, penetrance, inheritance pattern, and age of onset. Fetal risk was defined based on actual carrier frequency data accounting for the specific inheritance pattern (fetal disease risk, FDR). In addition, large-scale application of exome sequencing for PCS allowed a deep investigation of the incidence of medically actionable secondary findings in this population. Exome sequencing achieved remarkable clinical sensitivity for reproductive risk of highly penetrant childhood-onset disorders (1/337 conceptions) through analysis of 114 selected gene-condition pairs. A significant contribution to fetal disease risk was observed for rare (carrier rate < 1:100) and X-linked conditions (16.7% and 41.2% of total FDR, respectively). Subgroup analysis of 776 IVF couples identified 37 at increased reproductive risk (4.8%; 95% CI = 3.4–6.5). Further, two additional couples had increased risk for very rare conditions when both members of a parental pair were treated as a unit and the search was extended to the entire exome. About 2.3% of participants showed at least one pathogenic variant for genes included in the updated American College of Medical Genetics and Genomics v2.0 list of secondary findings. Gamete donors and IVF couples showed similar carrier burden for both carrier screening and secondary findings, indicating no causal relationship to fertility. These translational research data will facilitate development of more effective PCS strategies that maximize clinical sensitivity with minimal counterproductive effects.

We provide here crucial information for optimizing the gene-panel design for preconception carrier screening based on the analysis of a large exome sequencing dataset from infertile individuals and gamete donors. Sequencing the entire coding portion of the human genome combined with separate analysis for few relevant genes offers the possibility to detect most of the pathogenetic variants associated with recessive Mendelian diseases and to develop preconception screening strategies that maximise clinical sensitivity with minimal counterproductive effects. Using a large dataset of individual-level exome sequencing data, we have defined gene specific and aggregate fetal risk detectable for conditions selected on discrete criteria of severity, penetrance, inheritance pattern, and age of onset. About 1 out of 300 affected pregnancies can be detected based on a gene-panel of 114 conditions and ~5% of the couples analysed showed an increased risk that warrant consideration from a reproductive viewpoint. These results suggest the use of exome sequencing and parallel gene testing is clinically effective and feasible for preconception carrier screening after proper validation and translational research has been carried out. However, further studies are necessary to define the best framework for clinical implementation and the actual detection rate of at risk couples.

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene's function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Precision oncology: lessons learned and challenges for the future

The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these "first generation" early adopters.

APOB-associated cholesterol deficiency in Holstein cattle is not a simple recessive disease

In 2015, cholesterol deficiency (CD) was reported for the first time as a new recessive defect in Holstein cattle. After GWAS mapping and identification of a disease-associated haplotype, a causative loss-of-function variant in APOB was identified. CD-clinically affected APOB homozygotes showed poor development, intermittent diarrhea and hypocholesterolemia and, consequently, a limited life expectation. Herein, we present a collection of 18 cases clinically diagnosed as CD-affected APOB heterozygotes. CD-clinically affected heterozygotes show reduced cholesterol and triglyceride blood concentrations. The differences in total blood cholesterol and triglycerides between nine CD-clinically affected and 36 non-affected heterozygotes were significant. As only some APOB heterozygotes show the clinical CD phenotype, we assume that the penetrance is reduced in heterozygotes compared to the fully penetrant effect observed in homozygotes. We conclude that APOB-associated CD represents most likely an incomplete dominant inherited metabolic disease with incomplete penetrance in heterozygotes.

Quantitative Trait Locus and Integrative Genomics Revealed Candidate Modifier Genes for Ectopic Mineralization in Mouse Models of Pseudoxanthoma Elasticum

Pseudoxanthoma elasticum, a prototype of heritable multisystem ectopic mineralization disorders, is caused by mutations in the ABCC6 gene encoding a putative efflux transporter, ABCC6. The phenotypic spectrum of pseudoxanthoma elasticum varies, and the correlation between genotype and phenotype has not been established. To identify genetic modifiers, we performed quantitative trait locus analysis in inbred mouse strains that carry the same hypomorphic allele in Abcc6 yet with highly variable ectopic mineralization phenotypes of pseudoxanthoma elasticum. Abcc6 was confirmed as a major determinant for ectopic mineralization in multiple tissues. Integrative analysis using functional genomics tools that included GeneWeaver, String, and Mouse Genome Informatics identified a total of nine additional candidate modifier genes that could influence the organ-specific ectopic mineralization phenotypes. Integration of the candidate genes into the existing ectopic mineralization gene network expands the current knowledge on the complexity of the network that, as a whole, governs ectopic mineralization in soft connective tissues.

Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm

The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of the pathogenicity of genes and the total number of Mendelian disease genes are both important questions worth studying. However, very few studies have addressed these issues to date, so we attempt to answer them in this study. We calculated the gene pathogenicity prediction (GPP) score by a machine learning approach (random forest algorithm) to evaluate the pathogenicity of genes. When we applied the GPP score to the testing gene set, we obtained an accuracy of 80%, recall of 93% and area under the curve of 0.87. Our results estimated that a total of 10,384 protein-coding genes were Mendelian disease genes. Furthermore, we found the GPP score was positively correlated with the severity of disease. Our results indicate that GPP score may provide a robust and reliable guideline to predict the pathogenicity of protein-coding genes. To our knowledge, this is the first trial to estimate the total number of Mendelian disease genes.

Variation in Cilia Protein Genes and Progression of Lung Disease in Cystic Fibrosis

RATIONALE

Cystic fibrosis, like primary ciliary dyskinesia, is an autosomal recessive disorder characterized by abnormal mucociliary clearance and obstructive lung disease. We hypothesized that genes underlying the development or function of cilia may modify lung disease severity in persons with cystic fibrosis.

OBJECTIVES

To test this hypothesis, we compared variants in 93 candidate genes in both upper and lower tertiles of lung function in a large cohort of children and adults with cystic fibrosis with those of a population control dataset.

METHODS

Variants within candidate genes were tested for association using the SKAT-O test, comparing cystic fibrosis cases defined by poor (n = 127) or preserved (n = 127) lung function with population controls (n = 3,269 or 3,148, respectively). Associated variants were then tested for association with related phenotypes in independent datasets.

RESULTS

Variants in DNAH14 and DNAAF3 were associated with poor lung function in cystic fibrosis, whereas variants in DNAH14 and DNAH6 were associated with preserved lung function in cystic fibrosis. Associations between DNAH14 and lung function were replicated in disease-related phenotypes characterized by obstructive lung disease in adults.

CONCLUSIONS

Genetic variants within DNAH6, DNAH14, and DNAAF3 are associated with variation in lung function among persons with cystic fibrosis.

Predicting disease-causing variant combinations

Directly assessing the pathogenicity of variant combinations in multiple genes was until now difficult. Nonetheless, this type of assessment can provide important benefits in identifying the genetic causes of rare diseases. The work presented in this paper aims to resolve this problem by presenting a machine-learning method able to predict the pathogenicity of variant combinations in gene pairs, based on pathogenic data. We demonstrate the high accuracy of this method and its effective capacity to identify novel instances. The method’s decision-making process is also made explicit, a contribution that is useful for clinical interpretation. This pioneering work will lead to toolboxes for geneticists and clinicians that can aid them in counselling their patients more effectively.

Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

HDAC4 mutations cause diabetes and induce β-cell FoxO1 nuclear exclusion

Background

Studying patients with rare Mendelian diabetes has uncovered molecular mechanisms regulating β‐cell pathophysiology. Previous studies have shown that Class IIa histone deacetylases (HDAC4, 5, 7, and 9) modulate mammalian pancreatic endocrine cell function and glucose homeostasis.

Methods

We performed exome sequencing in one adolescent nonautoimmune diabetic patient and detected one de novo predicted disease‐causing HDAC4 variant (p.His227Arg). We screened our pediatric diabetes cohort with unknown etiology using Sanger sequencing. In mouse pancreatic β‐cell lines (Min6 and SJ cells), we performed insulin secretion assay and quantitative RT‐PCR to measure the β‐cell function transfected with the detected HDAC4 variants and wild type. We carried out immunostaining and Western blot to investigate if the detected HDAC4 variants affect the cellular translocation and acetylation status of Forkhead box protein O1 (FoxO1) in the pancreatic β‐cells.

Results

We discovered three HDAC4 mutations (p.His227Arg, p.Asp234Asn, and p.Glu374Lys) in unrelated individuals who had nonautoimmune diabetes with various degrees of β‐cell loss. In mouse pancreatic β‐cell lines, we found that these three HDAC4 mutations decrease insulin secretion, down‐regulate β‐cell‐specific transcriptional factors, and cause nuclear exclusion of acetylated FoxO1.

Conclusion

Mutations in HDAC4 disrupt the deacetylation of FoxO1, subsequently decrease the β‐cell function including insulin secretion, resulting in diabetes.

SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data

As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits, a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data, sample quality issues, such as gender mismatch, abnormal inbreeding coefficient, cryptic relatedness, and population outliers, can also have fundamental impact on downstream analysis. However, there is a lack of tools specialized in identifying problematic samples from NGS data, often due to the limitation of sample size and variant counts. We developed SeqSQC, a Bioconductor package, to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access, and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor.org/packages/SeqSQC.

Uncovering Missing Heritability in Rare Diseases

The problem of 'missing heritability' affects both common and rare diseases hindering: discovery, diagnosis, and patient care. The 'missing heritability' concept has been mainly associated with common and complex diseases where promising modern technological advances, like genome-wide association studies (GWAS), were unable to uncover the complete genetic mechanism of the disease/trait. Although rare diseases (RDs) have low prevalence individually, collectively they are common. Furthermore, multi-level genetic and phenotypic complexity when combined with the individual rarity of these conditions poses an important challenge in the quest to identify causative genetic changes in RD patients. In recent years, high throughput sequencing has accelerated discovery and diagnosis in RDs. However, despite the several-fold increase (from ~10% using traditional to ~40% using genome-wide genetic testing) in finding genetic causes of these diseases in RD patients, as is the case in common diseases-the majority of RDs are also facing the 'missing heritability' problem. This review outlines the key role of high throughput sequencing in uncovering genetics behind RDs, with a particular focus on genome sequencing. We review current advances and challenges of sequencing technologies, bioinformatics approaches, and resources.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Pharmacologic normalization of pathogenic dosage underlying genetic diseases: an overview of the literature and path forward

Most monogenic disorders are caused by a pathologic deficit or excess of a single transcript and/or protein. Given that small molecules, including drugs, can affect levels of mRNA and protein, the pharmacologic normalization of such pathogenic dosage represents a possible therapeutic approach for such conditions. Here, we review the literature exploring pharmacologic modulation of mRNA and/or protein levels for disorders with paralogous modifier genes, for haploinsufficient disorders (insufficient gene-product), as well as toxic gain-of-function disorders (surplus or pathologic gene-product). We also discuss challenges facing the development of rare disease therapy by pharmacologic modulation of mRNA and protein. Finally, we lay out guiding principles for selection of disorders which may be amenable to this approach.

The model of "genetic compartments": a new insight into reproductive genetics

Currently, we are witnessing revolutionary advances in the analytical power of genetic tools. An enormous quantity of data can now be obtained from samples; however, the translation of genetic findings to the general status of individuals, or their offspring, should be done with caution. This is especially relevant in the reproductive context, where the concepts of "transmission" and "inheritability" of a trait are crucial. Against this background, we offer new insight based on a systemic view of genetic constitution in the compartmentalized organism, that is, the human body. This model considers the coexistence of "different" genomes in the same individual and the repercussion of this on reproductive efficacy and offspring. Herein, we review the major differences between somatic, germinal, embryonic, and fetal/placental genomes and their contribution to the next generation and its reproductive efficacy. The major novelty of our approach is the holistic interaction between microsystems within a macrosystem (i.e., the reproductive system). This panoramic model allows us to sketch the future implications of genetic results in function of the origin (compartment) of the sample: peripheral blood or other somatic tissues, gametes, zygotes, preimplantation embryos, fetus, or placenta. We believe this perspective can be of great use in the context of reproductive genetic counseling.

Complex modifier landscape underlying genetic background effects

Genetic background impacts the phenotypic outcome of a mutation in different individuals; however, the underlying molecular mechanisms are often unclear. We characterized genes exhibiting conditional essentiality when mutated in two genetically distinct yeast strains. Hybrid crosses and whole-genome sequencing revealed that conditional essentiality can be associated with nonchromosomal elements or a single-modifier locus, but most involve a complex set of modifier loci. Detailed analysis of the cysteine biosynthesis pathway showed that independent, rare, single-gene modifiers, related to both up- and downstream pathway functions, can arise in multiple allelic forms from separate lineages. For several genes, we also resolved complex sets of modifying loci underlying conditional essentiality, revealing specific genetic interactions that drive an individual strain’s background effect.

The phenotypic consequence of a given mutation can be influenced by the genetic background. For example, conditional gene essentiality occurs when the loss of function of a gene causes lethality in one genetic background but not another. Between two individual Saccharomyces cerevisiae strains, S288c and Σ1278b, ∼1% of yeast genes were previously identified as “conditional essential.” Here, in addition to confirming that some conditional essential genes are modified by a nonchromosomal element, we show that most cases involve a complex set of genomic modifiers. From tetrad analysis of S288C/Σ1278b hybrid strains and whole-genome sequencing of viable hybrid spore progeny, we identified complex sets of multiple genomic regions underlying conditional essentiality. For a smaller subset of genes, including CYS3 and CYS4, each of which encodes components of the cysteine biosynthesis pathway, we observed a segregation pattern consistent with a single modifier associated with conditional essentiality. In natural yeast isolates, we found that the CYS3/CYS4 conditional essentiality can be caused by variation in two independent modifiers, MET1 and OPT1, each with roles associated with cellular cysteine physiology. Interestingly, the OPT1 allelic variation appears to have arisen independently from separate lineages, with rare allele frequencies below 0.5%. Thus, while conditional gene essentiality is usually driven by genetic interactions associated with complex modifier architectures, our analysis also highlights the role of functionally related, genetically independent, and rare variants.

High-frequency actionable pathogenic exome variants in an average-risk cohort

Exome sequencing is increasingly utilized in both clinical and nonclinical settings, but little is known about its utility in healthy individuals. Most previous studies on this topic have examined a small subset of genes known to be implicated in human disease and/or have used automated pipelines to assess pathogenicity of known variants. To determine the frequency of both medically actionable and nonactionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multiomics profiling study. We analyzed exomes by identifying rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We then used American College of Medical Genetics (ACMG) guidelines for the classification of rare sequence variants. Additionally, we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes. Five had phenotypes or family histories associated with their genetic variants. The frequency of actionable variants is higher than that reported in most previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 63 participants (90%) had additional nonactionable findings, including 60 who were found to be carriers for recessive diseases and 21 who have increased Alzheimer's disease risk because of heterozygous or homozygous APOE e4 alleles (18 participants had both). Our results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases.

Developmental processes regulate craniofacial variation in disease and evolution

Variation in development mediates phenotypic differences observed in evolution and disease. Although the mechanisms underlying phenotypic variation are still largely unknown, recent research suggests that variation in developmental processes may play a key role. Developmental processes mediate genotype-phenotype relationships and consequently play an important role regulating phenotypes. In this review, we provide an example of how shared and interacting developmental processes may explain convergence of phenotypes in spliceosomopathies and ribosomopathies. These data also suggest a shared pathway to disease treatment. We then discuss three major mechanisms that contribute to variation in developmental processes: genetic background (gene-gene interactions), gene-environment interactions, and developmental stochasticity. Finally, we comment on evolutionary alterations to developmental processes, and the evolution of disease buffering mechanisms.

Genetic variants in major depressive disorder: From pathophysiology to therapy

In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.

Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed 'variable penetrance'. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.

Haploinsufficiency of CUX1 Causes Nonsyndromic Global Developmental Delay With Possible Catch-up Development

OBJECTIVE

Developmental delay (DD) with favorable intellectual outcome and mild intellectual disability (ID) are mostly considered to be of complex genetic and environmental origin, but, in fact, often remain unclear. We aimed at proving our assumption that also mild cases of DD and ID may be of monogenic etiology.

METHODS

We clinically evaluated 8 individuals and performed exome sequencing or array copy number analysis and identified variants in CUX1 as the likely cause. In addition, we included a case from the public database, DECIPHER.

RESULTS

All 9 individuals harbored heterozygous null-allele variants in CUX1, encoding the Cut-homeobox 1 transcription factor that is involved in regulation of dendritogenesis and cortical synapse formation in layer II to IV cortical neurons. Six variants arose de novo, while in one family the variant segregated with ID. Of the 9 included individuals, 2 were diagnosed with moderate ID, 3 with mild ID, and 3 showed a normal age-related intelligence at ages 4, 6, and 8 years after a previous history of significant DD.

INTERPRETATION

Our results suggest that null-allele variants, and thus haploinsufficiency of CUX1, cause an isolated phenotype of DD or ID with possible catch-up development. This illustrates that such a developmental course is not necessarily genetic complex, but may also be attributed to a monogenic cause. Ann Neurol 2018;84:200-207.

Opportunities for community awareness platforms in personal genomics and bioinformatics education

Precision and personalized medicine will be increasingly based on the integration of various type of information, particularly electronic health records and genome sequences. The availability of cheap genome sequencing services and the information interoperability will increase the role of online bioinformatics analysis. Being on the Internet poses constant threats to security and privacy. While we are connected and we share information, websites and internet services collect various types of personal data with or without the user consent. It is likely that genomics will merge with the internet culture of connectivity. This process will increase incidental findings, exposure and vulnerability. Here we discuss the social vulnerability owing to the genome and Internet combined security and privacy weaknesses. This urges more efforts in education and social awareness on how biomedical data are analysed and transferred through the internet and how inferential methods could integrate information from different sources. We propose that digital social platforms, used for raising collective awareness in different fields, could be developed for collaborative and bottom-up efforts in education. In this context, bioinformaticians could play a meaningful role in mitigating the future risk of digital-genomic divide.

Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression

The phenotypic consequences of a given mutation can vary across individuals. This so-called background effect is widely observed, from mutant fitness of loss-of-function variants in model organisms to variable disease penetrance and expressivity in humans; however, the underlying genetic basis often remains unclear. Taking insights gained from recent large-scale surveys of genetic interaction and suppression analyses in yeast, we propose that the genetic network context for a given mutation may shape its propensity of exhibiting background-dependent phenotypes. We argue that further efforts in systematically mapping the genetic interaction networks beyond yeast will provide not only key insights into the functional properties of genes, but also a better understanding of the background effects and the (un)predictability of traits in a broader context.

Map of synthetic rescue interactions for the Fanconi anemia DNA repair pathway identifies USP48

Defects in DNA repair can cause various genetic diseases with severe pathological phenotypes. Fanconi anemia (FA) is a rare disease characterized by bone marrow failure, developmental abnormalities, and increased cancer risk that is caused by defective repair of DNA interstrand crosslinks (ICLs). Here, we identify the deubiquitylating enzyme USP48 as synthetic viable for FA-gene deficiencies by performing genome-wide loss-of-function screens across a panel of human haploid isogenic FA-defective cells (FANCA, FANCC, FANCG, FANCI, FANCD2). Thus, as compared to FA-defective cells alone, FA-deficient cells additionally lacking USP48 are less sensitive to genotoxic stress induced by ICL agents and display enhanced, BRCA1-dependent, clearance of DNA damage. Consequently, USP48 inactivation reduces chromosomal instability of FA-defective cells. Our results highlight a role for USP48 in controlling DNA repair and suggest it as a potential target that could be therapeutically exploited for FA.

SURF1 knockout cloned pigs: Early onset of a severe lethal phenotype

Leigh syndrome (LS) associated with cytochrome c oxidase (COX) deficiency is an early onset, fatal mitochondrial encephalopathy, leading to multiple neurological failure and eventually death, usually in the first decade of life. Mutations in SURF1, a nuclear gene encoding a mitochondrial protein involved in COX assembly, are among the most common causes of LS. LSSURF1 patients display severe, isolated COX deficiency in all tissues, including cultured fibroblasts and skeletal muscle. Recombinant, constitutive SURF1-/- mice show diffuse COX deficiency, but fail to recapitulate the severity of the human clinical phenotype. Pigs are an attractive alternative model for human diseases, because of their size, as well as metabolic, physiological and genetic similarity to humans. Here, we determined the complete sequence of the swine SURF1 gene, disrupted it in pig primary fibroblast cell lines using both TALENs and CRISPR/Cas9 genome editing systems, before finally generating SURF1-/- and SURF1-/+ pigs by Somatic Cell Nuclear Transfer (SCNT). SURF1-/- pigs were characterized by failure to thrive, muscle weakness and highly reduced life span with elevated perinatal mortality, compared to heterozygous SURF1-/+ and wild type littermates. Surprisingly, no obvious COX deficiency was detected in SURF1-/- tissues, although histochemical analysis revealed the presence of COX deficiency in jejunum villi and total mRNA sequencing (RNAseq) showed that several COX subunit-encoding genes were significantly down-regulated in SURF1-/- skeletal muscles. In addition, neuropathological findings, indicated a delay in central nervous system development of newborn SURF1-/- piglets. Our results suggest a broader role of sSURF1 in mitochondrial bioenergetics.

The developmental-genetics of canalization

Canalization, or robustness to genetic or environmental perturbations, is fundamental to complex organisms. While there is strong evidence for canalization as an evolved property that varies among genotypes, the developmental and genetic mechanisms that produce this phenomenon are very poorly understood. For evolutionary biology, understanding how canalization arises is important because, by modulating the phenotypic variation that arises in response to genetic differences, canalization is a determinant of evolvability. For genetics of disease in humans and for economically important traits in agriculture, this subject is important because canalization is a potentially significant cause of missing heritability that confounds genomic prediction of phenotypes. We review the major lines of thought on the developmental-genetic basis for canalization. These fall into two groups. One proposes specific evolved molecular mechanisms while the other deals with robustness or canalization as a more general feature of development. These explanations for canalization are not mutually exclusive and they overlap in several ways. General explanations for canalization are more likely to involve emergent features of development than specific molecular mechanisms. Disentangling these explanations is also complicated by differences in perspectives between genetics and developmental biology. Understanding canalization at a mechanistic level will require conceptual and methodological approaches that integrate quantitative genetics and developmental biology.

Functional missense and splicing variants in the retinoic acid catabolizing enzyme CYP26C1 in idiopathic short stature

Height is a complex quantitative trait with a high heritability. Short stature is diagnosed when height is significantly below the average of the general population for that person's age and sex. We have recently found that the retinoic acid degrading enzyme CYP26C1 modifies SHOX deficiency phenotypes toward more severe clinical manifestations. Here, we asked whether damaging variants in CYP26C1 alone could lead to short stature. We performed exome and Sanger sequencing to analyze 856 individuals with short stature where SHOX deficiency was previously excluded. Three different damaging missense variants and one splicing variant were identified in six independent individuals; the functional significance of the identified variants was tested in vitro or in vivo using zebrafish as a model. The genetic and functional data reported here indicate that CYP26C1 represents a novel gene underlying growth disorders and that damaging variants in the absence of SHOX variants can lead to short stature.

Emerging Role of Precision Medicine in Cardiovascular Disease

Precision medicine is an integrative approach to cardiovascular disease prevention and treatment that considers an individual's genetics, lifestyle, and exposures as determinants of their cardiovascular health and disease phenotypes. This focus overcomes the limitations of reductionism in medicine, which presumes that all patients with the same signs of disease share a common pathophenotype and, therefore, should be treated similarly. Precision medicine incorporates standard clinical and health record data with advanced panomics (ie, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics) for deep phenotyping. These phenotypic data can then be analyzed within the framework of molecular interaction (interactome) networks to uncover previously unrecognized disease phenotypes and relationships between diseases, and to select pharmacotherapeutics or identify potential protein-drug or drug-drug interactions. In this review, we discuss the current spectrum of cardiovascular health and disease, population averages and the response of extreme phenotypes to interventions, and population-based versus high-risk treatment strategies as a pretext to understanding a precision medicine approach to cardiovascular disease prevention and therapeutic interventions. We also consider the search for resilience and Mendelian disease genes and argue against the theory of a single causal gene/gene product as a mediator of the cardiovascular disease phenotype, as well as an Erlichian magic bullet to solve cardiovascular disease. Finally, we detail the importance of deep phenotyping and interactome networks and the use of this information for rational polypharmacy. These topics highlight the urgent need for precise phenotyping to advance precision medicine as a strategy to improve cardiovascular health and prevent disease.

Disruption of protein function by pathogenic mutations: common and uncommon mechanisms 1

Mutations in protein-coding regions underlie almost all Mendelian disorders, drive tumorigenesis, and contribute to susceptibility to common diseases. Despite the great diversity of diseases that are caused by coding mutations, the cellular processes that affect, and are affected by, pathogenic variants at the molecular level are fundamentally conserved. Experimental and computational approaches have revealed that a substantial fraction of disease mutations are not simple loss-of-function alleles. Rather, these pathogenic variants disrupt protein function in more subtle ways by tuning protein folding pathways, altering subcellular trafficking, interrupting signaling cascades, and rewiring highly connected interaction networks. Focusing mainly on Mendelian disorders, this review discusses the common mechanisms by which deleterious mutations disrupt protein function and how these disruptions can be exploited in the development of novel therapies.

Canine NAPEPLD-associated models of human myelin disorders

Canine leukoencephalomyelopathy (LEMP) is a juvenile-onset neurodegenerative disorder of the CNS white matter currently described in Rottweiler and Leonberger dogs. Genome-wide association study (GWAS) allowed us to map LEMP in a Leonberger cohort to dog chromosome 18. Subsequent whole genome re-sequencing of a Leonberger case enabled the identification of a single private homozygous non-synonymous missense variant located in the highly conserved metallo-beta-lactamase domain of the N-acyl phosphatidylethanolamine phospholipase D (NAPEPLD) gene, encoding an enzyme of the endocannabinoid system. We then sequenced this gene in LEMP-affected Rottweilers and identified a different frameshift variant, which is predicted to replace the C-terminal metallo-beta-lactamase domain of the wild type protein. Haplotype analysis of SNP array genotypes revealed that the frameshift variant was present in diverse haplotypes in Rottweilers, and also in Great Danes, indicating an old origin of this second NAPEPLD variant. The identification of different NAPEPLD variants in dog breeds affected by leukoencephalopathies with heterogeneous pathological features, implicates the NAPEPLD enzyme as important in myelin homeostasis, and suggests a novel candidate gene for myelination disorders in people.

DASH, the data and specimen hub of the National Institute of Child Health and Human Development

The benefits of data sharing are well-established and an increasing number of policies require that data be shared upon publication of the main study findings. As data sharing becomes the new norm, there is a heightened need for additional resources to drive efficient data reuse. This article describes the development and implementation of the Data and Specimen Hub (DASH) by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) to promote data sharing from NICHD-funded studies and enable researchers to comply with NIH data sharing policies. DASH’s flexible architecture is designed to archive diverse data types and formats from NICHD’s broad scientific portfolio in a manner that promotes FAIR data sharing principles. Performance of DASH over two years since launch is promising: the number of available studies and data requests are growing; three manuscripts have been published from data reanalysis, all within two years of access. Critical success factors included NICHD leadership commitment, stakeholder engagement and close coordination between the governance body and technical team.

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

Potential role of gender specific effect of leptin receptor deficiency in an extended consanguineous family with severe early-onset obesity

Congenital Leptin receptor (LEPR) deficiency is a rare genetic cause of early-onset morbid obesity characterised by severe early onset obesity, major hyperphagia, hypogonadotropic hypogonadism and immune and neuroendocrine/metabolic dysfunction. We identified a homozygous loss-of-function mutation, NM_002303.5:c.464 T > G; p.(Tyr155*), in the LEPR in an extended consanguineous family with multiple individuals affected by early-onset severe obesity and hyperphagia. Interestingly, the LEPR-deficient adult females have extremely high body mass index (BMI) with hypogonadal infertility, the BMI of the affected males began to decline around the onset of puberty (13-15 years) with fertility being preserved. These findings lead to the speculation that LEPR deficiency may have a gender-specific effect on the regulation of body weight. In order to elucidate gender-specific effects of LEPR deficiency on reproduction further investigations are needed. The limitations of this study are that our conclusion is based on observations of two males and two females. Further LEPR deficient males and females are required for comparison in order to support this finding more confidently.

Maternal 5mCpG Imprints at the PARD6G-AS1 and GCSAML Differentially Methylated Regions Are Decoupled From Parent-of-Origin Expression Effects in Multiple Human Tissues

A hallmark of imprinted genes in mammals is the occurrence of parent-of-origin-dependent asymmetry of DNA cytosine methylation (5^mC) of alleles at CpG islands (CGIs) in their promoter regions. This 5^mCpG asymmetry between the parental alleles creates allele-specific imprinted differentially methylated regions (iDMRs). iDMRs are often coupled to the transcriptional repression of the methylated allele and the activation of the unmethylated allele in a tissue-specific, developmental-stage-specific and/or isoform-specific fashion. iDMRs function as regulatory platforms, built through the recruitment of chemical modifications to histones to achieve differential, parent-of-origin-dependent chromatin segmentation states. Here, we used a comparative computational data mining approach to identify 125 novel constitutive candidate iDMRs that integrate the maximal number of allele-specific methylation region records overlapping CGIs in human methylomes. Twenty-nine candidate iDMRs display gametic 5^mCpG asymmetry, and another 96 are candidate secondary iDMRs. We established the maternal origin of the 5^mCpG imprints of one gametic (PARD6G-AS1) and one secondary (GCSAML) iDMRs. We also found a constitutively hemimethylated, nonimprinted domain at the PWWP2AP1 promoter CGI with oocyte-derived methylation asymmetry. Given that the 5^mCpG level at the iDMRs is not a sufficient criterion to predict active or silent locus states and that iDMRs can regulate genes from a distance of more than 1 Mb, we used RNA-Seq experiments from the Genotype-Tissue Expression project and public archives to assess the transcriptional expression profiles of SNPs across 4.6 Mb spans around the novel maternal iDMRs. We showed that PARD6G-AS1 and GCSAML are expressed biallelically in multiple tissues. We found evidence of tissue-specific monoallelic expression of ZNF124 and OR2L13, located 363 kb upstream and 419 kb downstream, respectively, of the GCSAML iDMR. We hypothesize that the GCSAML iDMR regulates the tissue-specific, monoallelic expression of ZNF124 but not of OR2L13. We annotated the non-coding epigenomic marks in the two maternal iDMRs using data from the Roadmap Epigenomics project and showed that the PARD6G-AS1 and GCSAML iDMRs achieve contrasting activation and repression chromatin segmentations. Lastly, we found that the maternal 5^mCpG imprints are perturbed in several hematopoietic cancers. We conclude that the maternal 5^mCpG imprints at PARD6G-AS1 and GCSAML iDMRs are decoupled from parent-of-origin transcriptional expression effects in multiple tissues.

Current developments in the genetics of Rett and Rett-like syndrome

PURPOSE OF REVIEW

This article reviews the current molecular genetic studies, which investigate the genetic causes of Rett syndrome or Rett-like phenotypes without a MECP2 mutation.

RECENT FINDINGS

As next generation sequencing becomes broadly available, especially whole exome sequencing is used in clinical diagnosis of the genetic causes of a wide spectrum of intellectual disability, autism, and encephalopathies. Patients who were diagnosed with Rett syndrome or Rett-like syndrome because of their phenotype but were negative for mutations in the MECP2, CDKL5 or FOXG1 genes were subjected to whole exome sequencing and the results of the last few years revealed yet 69 different genes. Many of these genes are involved in epigenetic gene regulation, chromatin shaping, neurotransmitter action or RNA transcription/translation. Genetic data also allows to investigate the individual genetic background of an individual patient, which can modify the severity of a genetic disorder.

SUMMARY

We conclude that the Rett syndrome phenotype has a much broader underlying genetic cause and the typical phenotype overlap with other genetic disorders. For proper genetic counselling, patient perspective and treatment it is important to include both phenotype and genetic information.

Clinical and genetic spectrum of AMPD2-related pontocerebellar hypoplasia type 9

Pontocerebellar hypoplasia (PCH) represents a group of autosomal-recessive progressive neurodegenerative disorders of prenatal onset. Eleven PCH subtypes are classified according to clinical, neuroimaging and genetic findings. Individuals with PCH type 9 (PCH9) have a unique combination of postnatal microcephaly, hypoplastic cerebellum and pons, and hypoplastic or absent corpus callosum. PCH9 is caused by biallelic variants in AMPD2 encoding adenosine monophosphate deaminase 2; however, a homozygous AMPD2 frameshift variant has recently been reported in two family members with spastic paraplegia type 63 (SPG63). We identified homozygous or compound heterozygous AMPD2 variants in eight PCH-affected individuals from six families. The eight variants likely affect function and comprise one frameshift, one nonsense and six missense variants; seven of which were novel. The main clinical manifestations in the eight new patients and 17 previously reported individuals with biallelic AMPD2 variants were postnatal microcephaly, severe global developmental delay, spasticity, and central visual impairment. Brain imaging data identified hypomyelination, hypoplasia of the cerebellum and pons, atrophy of the cerebral cortex, complete or partial agenesis of the corpus callosum and the "figure 8" shape of the hypoplastic midbrain as consistent features. We broaden the AMPD2-related clinical spectrum by describing one individual without microcephaly and absence of the characteristic "figure 8" shape of the midbrain. The existence of various AMPD2 isoforms with different functions possibly explains the variability in phenotypes associated with AMPD2 variants: variants leaving some of the isoforms intact may cause SPG63, while those affecting all isoforms may result in the severe and early-onset PCH9.