ESPE Yearbook of Paediatric Endocrinology

N Engl J Med. 2021;385(20):1868-80. doi: 10.1056/NEJMoa2035790. PubMed ID: 34758253

Brief summary: This paper describes the pilot data for the UK 100 000 Genomes Project, including Whole Genome Sequencing (WGS) data on 4660 participants from 2183 families, and covering 161 disorders. They made a genetic diagnosis in 25% of probands; diagnoses were more likely for probands who presented with intellectual disability, hearing, or visual disorders (range 40% to 55%) and in cases who had WGS data on family trios (both their parents) or larger family pedigrees. Of the genetic diagnoses made, 25% had immediate implications for clinical decisions for the probands or their relatives.

The last year has seen a rapid increase in the numbers of publications of rare genetic variants associated with complex diseases, due to the recent availability of Whole Exome Sequence (WES) data in large-scale population-based studies. For example, WES data on over 650K people from the UK Biobank and FinnGen cohorts identified 975 associations between rare, protein-coding variants with 148 disease clusters (1).

The current paper leaps ahead to describe what will be the additional value of massively extending the scope of DNA sequences covered by moving from WES (which focusses on gene exons) to WGS. While it is expected that most disease-causing mutations would be located in/near gene exons, WES coverage of exons is imperfect and a notable finding is that 8% of the coding (amino acid changing) variants detected by the broader WGS approach had been missed on WES. Furthermore, 14% of rare diagnoses made by WGS involved noncoding, structural, or mitochondrial genome variants. This is an exciting era for human genetics, with the increasing availability of data on rare dysfunctional variants as well as large scale GWAS array data for common variants. This study shows that the use of WGS, providing more comprehensive genome coverage, will enable significant new insights into the genetic determinants of disease.

Reference: 1. Sun BB, et al. Genetic associations of protein-coding variants in human disease. Nature Feb 2022. 603; 95–102.