Timely molecular diagnosis of RB1 mutations enables earlier treatment, lower risk, and better health outcomes for patients with retinoblastoma; empowers families to make informed family-planning decisions; and costs less than conventional surveillance. However, complexity has hindered clinical implementation of molecular diagnosis. The majority of RB1 mutations are unique and distributed throughout the RB1 gene, with no real hot spots. We devised a sensitive and efficient strategy to identify RB1 mutations that combines quantitative multiplex polymerase chain reaction (QM-PCR), double-exon sequencing, and promoter-targeted methylation-sensitive PCR. Optimization of test order by stochastic dynamic programming and the development of allele-specific PCR for four recurrent point mutations decreased the estimated turnaround time to <3 wk and decreased direct costs by one-third. The multistep method reported here detected 89% (199/224) of mutations in bilaterally affected probands and both mutant alleles in 84% (112/134) of tumors from unilaterally affected probands. For 23 of 27 exons and the promoter region, QM-PCR was a highly accurate measure of deletions and insertions (accuracy 95%). By revealing those family members who did not carry the mutation found in the related proband, molecular analysis enabled 97 at-risk children from 20 representative families to avoid 313 surveillance examinations under anesthetic and 852 clinic visits. The average savings in direct costs from clinical examinations avoided by children in these families substantially exceeded the cost of molecular testing. Moreover, health care savings continue to accrue, as children in succeeding generations avoid unnecessary repeated anaesthetics and examinations.

Next-generation sequencing has been invaluable in the elucidation of the genetic etiology of many subtypes of intellectual disability in recent years. Here, using exome sequencing and whole-genome sequencing, we identified three de novo truncating mutations in WAS protein family member 1 (WASF1) in five unrelated individuals with moderate to profound intellectual disability with autistic features and seizures. WASF1, also known as WAVE1, is part of the WAVE complex and acts as a mediator between Rac-GTPase and actin to induce actin polymerization. The three mutations connected by Matchmaker Exchange were c.1516C>T (p.Arg506Ter), which occurs in three unrelated individuals, c.1558C>T (p.Gln520Ter), and c.1482delinsGCCAGG (p.Ile494MetfsTer23). All three variants are predicted to partially or fully disrupt the C-terminal actin-binding WCA domain. Functional studies using fibroblast cells from two affected individuals with the c.1516C>T mutation showed a truncated WASF1 and a defect in actin remodeling. This study provides evidence that de novo heterozygous mutations in WASF1 cause a rare form of intellectual disability.

Genetic and environmental factors influence complex disease in humans, such as metabolic syndrome, and Drosophila melanogaster serves as an excellent model in which to test these factors experimentally. Here we explore the modularity of endophenotypes with an in-depth reanalysis of a previous study by Reed et al. (2014), where we raised 20 wild-type genetic lines of Drosophila larvae on four diets and measured gross phenotypes of body weight, total sugar, and total triglycerides, as well as the endophenotypes of metabolomic and whole-genome expression profiles. We then perform new gene expression experiments to test for conservation of phenotype-expression correlations across different diets and populations. We find that transcript levels correlated with gross phenotypes were enriched for puparial adhesion, metamorphosis, and central energy metabolism functions. The specific metabolites L-DOPA and N-arachidonoyl dopamine make physiological links between the gross phenotypes across diets, whereas leucine and isoleucine thus exhibit genotype-by-diet interactions. Between diets, we find low conservation of the endophenotypes that correlate with the gross phenotypes. Through the follow-up expression study, we found that transcript-trait correlations are well conserved across populations raised on a familiar diet, but on a novel diet, the transcript-trait correlations are no longer conserved. Thus, physiological canalization of metabolic phenotypes breaks down in a novel environment exposing cryptic variation. We cannot predict the physiological basis of disease in a perturbing environment from profiles observed in the ancestral environment. This study demonstrates that variation for disease traits within a population is acquired through a multitude of physiological mechanisms, some of which transcend genetic and environmental influences, and others that are specific to an individual’s genetic and environmental context.

Mutations in more than 250 genes are implicated in inherited retinal dystrophy; the encoded proteins are involved in a broad spectrum of pathways. The presence of unsolved families after highly parallel sequencing strategies suggests that further genes remain to be identified. Whole-exome and -genome sequencing studies employed here in large cohorts of affected individuals revealed biallelic mutations in ARHGEF18 in three such individuals. ARHGEF18 encodes ARHGEF18, a guanine nucleotide exchange factor that activates RHOA, a small GTPase protein that is a key component of tight junctions and adherens junctions. This biological pathway is known to be important for retinal development and function, as mutation of CRB1, encoding another component, causes retinal dystrophy. The retinal structure in individuals with ARHGEF18 mutations resembled that seen in subjects with CRB1 mutations. Five mutations were found on six alleles in the three individuals: c.808A>G (p.Thr270Ala), c.1617+5G>A (p.Asp540Glyfs63), c.1996C>T (p.Arg666), c.2632G>T (p.Glu878), and c.2738_2761del (p.Arg913_Glu920del). Functional tests suggest that each disease genotype might retain some ARHGEF18 activity, such that the phenotype described here is not the consequence of nullizygosity. In particular, the p.Thr270Ala missense variant affects a highly conserved residue in the DBL homology domain, which is required for the interaction and activation of RHOA. Previously, knock-out of Arhgef18 in the medaka fish has been shown to cause larval lethality which is preceded by retinal defects that resemble those seen in zebrafish Crumbs complex knock-outs. The findings described here emphasize the peculiar sensitivity of the retina to perturbations of this pathway, which is highlighted as a target for potential therapeutic strategies.

A major objective of genomics is to elucidate the mapping between genotypic and phenotypic space as a step toward understanding how small changes in gene function can lead to elaborate phenotypic changes. One approach that has been utilized is to examine overall patterns of covariation between phenotypic variables of interest, such as morphology, physiology, and behavior, and underlying aspects of gene activity, in particular transcript abundance on a genome-wide scale. Numerous studies have demonstrated that such patterns of covariation occur, although these are often between samples with large numbers of unknown genetic differences (different strains or even species) or perturbations of large effect (sexual dimorphism or strong loss-of-function mutations) that may represent physiological changes outside of the normal experiences of the organism. We used weak mutational perturbations in genes affecting wing development in Drosophila melanogaster that influence wing shape relative to a co-isogenic wild type. We profiled transcription of 1150 genes expressed during wing development in 27 heterozygous mutants, as well as their co-isogenic wild type and one additional wild-type strain. Despite finding clear evidence of expression differences between mutants and wild type, transcriptional profiles did not covary strongly with shape, suggesting that information from transcriptional profiling may not generally be predictive of final phenotype. We discuss these results in the light of possible attractor states of gene expression and how this would affect interpretation of covariation between transcriptional profiles and other phenotypes.