Background: To facilitate the clinical implementation of genomic medicine by next-generation sequencing, it will be critically important to obtain accurate and consistent variant calls on personal genomes. Multiple software tools for variant calling are available, but it is unclear how comparable these tools are or what their relative merits in real-world scenarios might be. Methods: We sequenced 15 exomes from four families using commercial kits (Illumina HiSeq 2000 platform and Agilent SureSelect version 2 capture kit), with approximately 120X mean coverage. We analyzed the raw data using near-default parameters with five different alignment and variant-calling pipelines (SOAP, BWA-GATK, BWA-SNVer, GNUMAP, and BWA-SAMtools). We additionally sequenced a single whole genome using the sequencing and analysis pipeline from Complete Genomics (CG), with 95% of the exome region being covered by 20 or more reads per base. Finally, we validated 919 single-nucleotide variations (SNVs) and 841 insertions and deletions (indels), including similar fractions of GATK-only, SOAP-only, and shared calls, on the MiSeq platform by amplicon sequencing with approximately 5000X mean coverage. Results: SNV concordance between five Illumina pipelines across all 15 exomes was 57.4%, while 0.5 to 5.1% of variants were called as unique to each pipeline. Indel concordance was only 26.8% between three indel-calling pipelines, even after left-normalizing and intervalizing genomic coordinates by 20 base pairs. There were 11% of CG variants falling within targeted regions in exome sequencing that were not called by any of the Illumina-based exome analysis pipelines. Based on targeted amplicon sequencing on the MiSeq platform, 97.1%, 60.2%, and 99.1% of the GATK-only, SOAP-only and shared SNVs could be validated, but only 54.0%, 44.6%, and 78.1% of the GATK-only, SOAP-only and shared indels could be validated. Additionally, our analysis of two families (one with four individuals and the other with seven), demonstrated additional accuracy gained in variant discovery by having access to genetic data from a multi-generational family. Conclusions: Our results suggest that more caution should be exercised in genomic medicine settings when analyzing individual genomes, including interpreting positive and negative findings with scrutiny, especially for indels. We advocate for renewed collection and sequencing of multi-generational families to increase the overall accuracy of whole genomes.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this ‘supervised’ approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.

We describe an X-linked genetic syndrome associated with mutations in TAF1 and manifesting with global developmental delay, intellectual disability (ID), characteristic facial dysmorphology, generalized hypotonia, and variable neurologic features, all in male individuals. Simultaneous studies using diverse strategies led to the identification of nine families with overlapping clinical presentations and affected by de novo or maternally inherited single-nucleotide changes. Two additional families harboring large duplications involving TAF1 were also found to share phenotypic overlap with the probands harboring single-nucleotide changes, but they also demonstrated a severe neurodegeneration phenotype. Functional analysis with RNA-seq for one of the families suggested that the phenotype is associated with downregulation of a set of genes notably enriched with genes regulated by E-box proteins. In addition, knockdown and mutant studies of this gene in zebrafish have shown a quantifiable, albeit small, effect on a neuronal phenotype. Our results suggest that mutations in TAF1 play a critical role in the development of this X-linked ID syndrome.

The X-linked lethal Ogden syndrome was the first reported human genetic disorder associated with a mutation in an N-terminal acetyltransferase (NAT) gene. The affected males harbor an Ser37Pro (S37P) mutation in the gene encoding Naa10, the catalytic subunit of NatA, the major human NAT involved in the co-translational acetylation of proteins. Structural models and molecular dynamics simulations of the human NatA and its S37P mutant highlight differences in regions involved in catalysis and at the interface between Naa10 and the auxiliary subunit hNaa15. Biochemical data further demonstrate a reduced catalytic capacity and an impaired interaction between hNaa10 S37P and Naa15 as well as Naa50 (NatE), another interactor of the NatA complex. N-Terminal acetylome analyses revealed a decreased acetylation of a subset of NatA and NatE substrates in Ogden syndrome cells, supporting the genetic findings and our hypothesis regarding reduced Nt-acetylation of a subset of NatA/NatE-type substrates as one etiology for Ogden syndrome. Furthermore, Ogden syndrome fibroblasts display abnormal cell migration and proliferation capacity, possibly linked to a perturbed retinoblastoma pathway. N-Terminal acetylation clearly plays a role in Ogden syndrome, thus revealing the in vivo importance of N-terminal acetylation in human physiology and disease.

Background: Whole-exome sequencing has identified the causes of several Mendelian diseases by analyzing multiple unrelated cases, but it is more challenging to resolve the cause of extremely rare and suspected Mendelian diseases from individual families. We identified a family quartet with two children, both affected with a previously unreported disease, characterized by progressive muscular weakness and cardiomyopathy, with normal intelligence. During the course of the study, we identified one additional unrelated patient with a comparable phenotype. Methods: We performed whole-genome sequencing (Complete Genomics platform), whole-exome sequencing (Agilent SureSelect exon capture and Illumina Genome Analyzer II platform), SNP genotyping (Illumina HumanHap550 SNP array) and Sanger sequencing on blood samples, as well as RNA-Seq (Illumina HiSeq platform) on transformed lymphoblastoid cell lines. Results: From whole-genome sequence data, we identified RBCK1, a gene encoding an E3 ubiquitin-protein ligase, as the most likely candidate gene, with two protein-truncating mutations in probands in the first family. However, exome data failed to nominate RBCK1 as a candidate gene, due to poor regional coverage. Sanger sequencing identified a private homozygous splice variant in RBCK1 in the proband in the second family, yet SNP genotyping revealed a 1.2Mb copy-neutral region of homozygosity covering RBCK1. RNA-Seq confirmed aberrant splicing of RBCK1 transcripts, resulting in truncated protein products. Conclusions: While the exact mechanism by which these mutations cause disease is unknown, our study represents an example of how the combined use of whole-genome DNA and RNA sequencing can identify a disease-predisposing gene for a novel and extremely rare Mendelian disease.