medRxivGenetic & Genomic MedicinePreprint
Proteomic and clinical impact of human knockouts in British South Asians
Julia Carrasco-ZaniniiD, Maik PietzneriD, Mine KopruluiD, Martijn ZoodsmaiD, Alice WilliamsoniD, Karen A. HuntiD, Angelos ManoliasiD, Klaudia WalteriD, Hilary C. MartiniD, Sarah FineriD, David A. van HeeliD, Claudia LangenbergiD
- Citations
- 2
Abstract
Human loss-of-function (LoF) variants affecting both copies of a gene ("human knockouts") provide a unique opportunity to directly study function and clinical impact of genes but are very rare in most populations sequenced to date. Here we study 1,569 British Bangladeshi and -Pakistani adults who were recalled for plasma sampling for proteomic profiling using three distinct technologies (covering >12,000 proteins) from 55k whole exome sequenced Genes & Health adults - a cohort enriched for rare, biallelic (homozygous) variants due to high autozygosity. We identified 199 individuals with rare homozygous predicted LoF genotypes (pLoF) for which the respective cis-protein was measured by at least one technology, and observed extreme (> 3SDs) cis-protein underexpression in 41 individuals (median z-score = -9.72 (range: -19.61 to -4.78) and overexpression in 2 individuals (median z-score = 8.1 (range 4.80 - 11.40)), representing 19% of these variants. For missense homozygotes, we observed 158 individuals with significantly under-expressed cis-protein (median z-score = -6.95 (range: -28.16 to -3.95)) and 62 individuals over-expressed, median z-score = 5.65 (range: 4.57 to 25.08)). The majority (62%) of LoF knockout genes with an identified cis-protein effect had evidence from 2 or more platforms, highlighting the high confidence nature of these discoveries. Systematic clinical assessment of human knockouts with strong evidence of an impact on cis-protein abundance through multi-source electronic health record linkage enabled identification of 1) knockout carriers with rare disease features based on phenotypic similarity, 2) novel rare disease-causing variants, 3) evidence for reclassification of genes and variants of uncertain significance from ClinVar and rare disease panels, and 4) novel gene-phenotype associations in humans. Based on high confidence examples, we developed a machine learning model that predicted 1 in 4 pLOF and 9 in 10 missense variants are likely benign. In summary, our study provides strong human-derived insights into the fundamental biology and clinical relevance of many genes and shows the value of proteogenomic studies of human knockout carriers.
- organism: human
- assay: proteomics
- research focus: variant effect prediction
- approach: machine learning (classical)
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