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Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

Purpose: Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration., Methods: Over the past 10 years, the National Institutes of Health-supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution., Results: We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher., Conclusion: The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients., Competing Interests: Conflict of Interest Baylor College of Medicine and Miraca Holdings Inc have formed a joint venture with shared ownership and governance of Baylor Genetics, formerly the Baylor Miraca Genetics Laboratories, which performs clinical ES and chromosomal microarray analysis for genome-wide detection of copy number variants. J.R.L. serves on the Scientific Advisory Board of Baylor Genetics. J.R.L. has stock ownership in 23andMe, is a paid consultant for Regeneron Pharmaceuticals, and is a coinventor on multiple United States and European patents related to molecular diagnostics for inherited neuropathies, eye diseases, and bacterial genomic fingerprinting. H.L.R. receives funding from Illumina to support rare disease gene discovery and diagnosis. Consortium author conflicts of interest are listed in the Supplement. All other authors have no disclosures relevant to the manuscript., (Copyright © 2021 American College of Medical Genetics and Genomics. All rights reserved.)

The NIH Roadmap Epigenomics Program was launched to deliver reference epigenomic data from human tissues and cells, develop tools and methods for analyzing the epigenome, discover novel epigenetic marks, develop methods to manipulate the epigenome, and determine epigenetic contributions to diverse human diseases. Here, we comment on the outcomes from this program: the scientific contributions made possible by a consortium approach and the challenges, benefits, and lessons learned from this group science effort.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

This chapter describes resources and technologies generated by the NIH Roadmap Epigenomics Program that may be useful to epigenomics researchers investigating a variety of diseases including cancer. Highlights include reference epigenome maps for a wide variety of human cells and tissues, the development of new technologies for epigenetic assays and imaging, the identification of novel epigenetic modifications, and an improved understanding of the role of epigenetic processes in a diversity of human diseases. We also discuss future needs in this area including exploration of epigenomic variation between individuals, single-cell epigenomics, environmental epigenomics, exploration of the use of surrogate tissues, and improved technologies for epigenome manipulation.

The NIH Roadmap Reference Epigenome Mapping Consortium is developing a community resource of genome-wide epigenetic maps in a broad range of human primary cells and tissues. There are large amounts of data already available, and a number of different options for viewing and analyzing the data. This report will describe key features of the websites where users will find data, protocols and analysis tools developed by the consortium, and provide a perspective on how this unique resource will facilitate and inform human disease research, both immediately and in the future.

Rett syndrome is a progressive neurological disorder caused by mutations in the methyl-DNA binding protein MeCP2. The longstanding model depicting MeCP2 as a transcriptional repressor predicts that the Rett syndrome phenotype probably results from misregulation of MeCP2 target genes. Somewhat unexpectedly, the identification of such targets has proven challenging. The recent identification of two MeCP2 targets, BDNF and DLX5, are suggestive of two very different roles for this protein--one as a classical repressor protein, and the other as a mediator of a complex, specialized chromatin structure.

In early mammalian development, one of the two X chromosomes is silenced in each female cell as a result of X chromosome inactivation, the mammalian dosage compensation mechanism. In the mouse epiblast, the choice of which chromosome is inactivated is essentially random, but can be biased by alleles at the X-linked X controlling element (Xce). Although this locus was first described nearly four decades ago, the identity and precise genomic localization of Xce remains elusive. Within the X inactivation center region of the X chromosome, previous linkage disequilibrium studies comparing strains of known Xce genotypes have suggested that Xce is physically distinct from Xist, although this has not yet been established by genetic mapping or progeny testing. In this report, we used quantitative trait locus (QTL) mapping strategies to define the minimal Xce candidate interval. Subsequent analysis of recombinant chromosomes allowed for the establishment of a maximum 1.85-Mb candidate region for the Xce locus. Finally, we use QTL approaches in an effort to identify additional modifiers of the X chromosome choice, as we have previously demonstrated that choice in Xce heterozygous females is significantly influenced by genetic variation present on autosomes (Chadwick and Willard 2005). We did not identify any autosomal loci with significant associations and thus show conclusively that Xce is the only major locus to influence X inactivation patterns in the crosses analyzed. This study provides a foundation for future analyses into the genetic control of X chromosome inactivation and defines a 1.85-Mb interval encompassing all the major elements of the Xce locus.

X chromosome inactivation is unique among dosage compensation mechanisms in that the two X chromosomes in females are treated differently within the same cell; one X chromosome is stably silenced while the other remains active. It is widely believed that, when X inactivation is initiated, each cell makes a random choice of which X chromosome will be silenced. In mice, only one genetic locus, the X-linked X controlling element (X ce), is known to influence this choice, because animals that are heterozygous at X ce have X-inactivation patterns that differ markedly from a mean of 0.50. To document other genetic and epigenetic influences on choice, we have performed a population-based study of the effect of X ce genotype on X-inactivation patterns. In B 6 CAST F(1) females (X ce(b)/X ce(c)), the X-inactivation pattern followed a symmetric distribution with a mean of 0.29 (SD=0.08). Surprisingly, however, in a population of X ce(b)/X ce(c) heterozygous B 6 CAST F(2) females, we observed significant differences in both the mean (p=0.004) and variance (p=0.004) of the X-inactivation patterns. This finding is incompatible with a single-locus model and suggests that additional genetic factors also influence X chromosome choice. We show that both parent-of-origin and naturally occurring genetic variation at autosomal loci contribute to these differences. Taken together, these data reveal further genetic complexity in this epigenetic control pathway.

Mutations in MCOLN1 have been found to cause mucolipidosis type IV (MLIV; MIM 252650), a rare autosomal recessive lysosomal storage disorder found primarily in the Ashkenazi Jewish population. As a part of the successful cloning of MCOLN1, we constructed a 1.4-Mb physical map containing 14 BACs and 4 cosmids that encompasses the region surrounding MCOLN1 on human chromosome 19p13.3-p13.2-a region to which linkage or association has been reported for multiple diseases. Here we detail the precise physical mapping of 28 expressed sequence tags that represent unique UniGene clusters, of which 15 are known genes. We present a detailed transcript map of the MCOLN1 gene region that includes the genes KIAA0521, neuropathy target esterase (NTE), a novel zinc finger gene, and two novel transcripts in addition to MCOLN1. We also report the identification of eight new polymorphic markers between D19S406 and D19S912, which allowed us to pinpoint the location of MCOLN1 by haplotype analysis and which will facilitate future fine-mapping in this region. Additionally, we briefly describe the correlation between the observed haplotypes and the mutations found in MCOLN1. The complete 14-marker haplotypes of non-Jewish disease chromosomes, which are crucial for the genetic diagnosis of MLIV in the non-Jewish population, are presented here for the first time., (Copyright 2001 Academic Press.)

Family and twin studies have indicated that genes influence susceptibility to panic and phobic anxiety disorders, but the location of the genes involved remains unknown. Animal models can simplify gene-mapping efforts by overcoming problems that complicate human pedigree studies including genetic heterogeneity and high phenocopy rates. Homology between rodent and human genomes can be exploited to map human genes underlying complex traits. We used regions identified by quantitative trait locus (QTL)-mapping of anxiety phenotypes in mice to guide a linkage analysis of a large multiplex pedigree (99 members, 75 genotyped) segregating panic disorder/agoraphobia. Two phenotypes were studied: panic disorder/agoraphobia and a phenotype ("D-type") designed to capture early-onset susceptibility to anxiety disorders. A total of 99 markers across 11 chromosomal regions were typed. Parametric lod score analysis provided suggestive evidence of linkage (lod = 2.38) to a locus on chromosome 10q under a dominant model with reduced penetrance for the anxiety-proneness (D-type) phenotype. Nonparametric (NPL) analysis provided evidence of linkage for panic disorder/agoraphobia to a locus on chromosome 12q13 (NPL = 4.96, P = 0.006). Modest evidence of linkage by NPL analysis was also found for the D-type phenotype to a region of chromosome 1q (peak NPL = 2.05, P = 0.035). While these linkage results are merely suggestive, this study illustrates the potential advantages of using mouse gene-mapping results and exploring alternative phenotype definitions in linkage studies of anxiety disorder., (Copyright 2001 Wiley-Liss, Inc.)

Anomalies in folate and homocysteine metabolism can result in homocysteinemia and are implicated in disorders ranging from vascular disease to neural tube defects. Two enzymes are known to methylate homocysteine, vitamin B(12)-dependent methionine synthase (MTR) and betaine-homocysteine methyltransferase (BHMT). BHMT uses betaine, an intermediate of choline oxidation, as a methyl donor and is expressed primarily in the liver and kidney. We report the discovery of a novel betaine-homocysteine methyltransferase gene in humans and mice. The human BHMT2 gene is predicted to encode a 363-amino-acid protein (40.3 kDa) that shows 73% amino acid identity to BHMT. The BHMT2 transcript in humans is most abundant in adult liver and kidney and is found at reduced levels in the brain, heart, and skeletal muscle. The mouse Bhmt2 gene shows 69% amino acid identity and 79% similarity to the mouse Bhmt gene and 82% amino acid identity and 87% similarity to the human BHMT2 gene. Bhmt2 is expressed in fetal heart, lung, liver, kidney and eye. The discovery of a third gene with putative homocysteine methyltransferase activity is important for understanding the biochemical balance in using methyltetrahydrofolate and betaine as methyl donors as well as the metabolic flux between folate and choline metabolism in health and disease., (Copyright 2000 Academic Press.)