Background The UK 100,000 Genomes Project is in the process of investigating the role of genome sequencing of patients with undiagnosed rare disease following usual care, and the alignment of research with healthcare implementation in the UK’s national health service. (Other parts of this Project focus on patients with cancer and infection.) Methods We enrolled participants, collected clinical features with human phenotype ontology terms, undertook genome sequencing and applied automated variant prioritization based on virtual gene panels (PanelApp) and phenotypes (Exomiser), alongside identification of novel pathogenic variants through research analysis. We report results on a pilot study of 4660 participants from 2183 families with 161 disorders covering a broad spectrum of rare disease. Results Diagnostic yields varied by family structure and were highest in trios and larger pedigrees. Likely monogenic disorders had much higher diagnostic yields (35%) with intellectual disability, hearing and vision disorders, achieving yields between 40 and 55%. Those with more complex etiologies had an overall 25% yield. Combining research and automated approaches was critical to 14% of diagnoses in which we found etiologic non-coding, structural and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohort-wide burden testing across 57,000 genomes enabled discovery of 3 new disease genes and 19 novel associations. Of the genetic diagnoses that we made, 24% had immediate ramifications for the clinical decision-making for the patient or their relatives. Conclusion Our pilot study of genome sequencing in a national health care system demonstrates diagnostic uplift across a range of rare diseases. (Funded by National Institute for Health Research and others)

Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

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Embryonic development is driven by tightly regulated patterns of gene expression, despite extensive genetic variation among individuals. Studies of expression quantitative trait loci (eQTL) indicate that genetic variation frequently alters gene expression in cell-culture models and differentiated tissues. However, the extent and types of genetic variation impacting embryonic gene expression, and their interactions with developmental programs, remain largely unknown. Here we assessed the effect of genetic variation on transcriptional (expression levels) and post-transcriptional (3' RNA processing) regulation across multiple stages of metazoan development, using 80 inbred Drosophila wild isolates, identifying thousands of developmental-stage-specific and shared QTL. Given the small blocks of linkage disequilibrium in Drosophila, we obtain near base-pair resolution, resolving causal mutations in developmental enhancers, validated transcription-factor-binding sites and RNA motifs. This fine-grain mapping uncovered extensive allelic interactions within enhancers that have opposite effects, thereby buffering their impact on enhancer activity. QTL affecting 3' RNA processing identify new functional motifs leading to transcript isoform diversity and changes in the lengths of 3' untranslated regions. These results highlight how developmental stage influences the effects of genetic variation and uncover multiple mechanisms that regulate and buffer expression variation during embryogenesis.