Analysis of the Frequency of Spontaneous, Functionally-Significant Mutations in Genes Associated with Platelet Disorders in >120,000 Healthy Individuals
Bibliographic record
Abstract
Abstract Inherited platelet disorders (IPD) are increasingly recognized as the cause of clinical bleeding. Advances in genomic technologies have identified a growing number of platelet-associated genes that are currently vetted with phenotypic correlates. These platelet-associated genes are a disparate group, including transcription or related nuclear factors, cytoskeletal proteins, surface receptors, intracellular proteins and granule forming proteins. At present the prevalence of each inherited platelet-associated disorder and the disorders in aggregate are not well defined. We leveraged the recent curating of 123,136 high quality exomes from a cross section of the general population in the form of the genome aggregation database (gnomAD) for analysis. We used the loss-of-function transcript effect estimator (LoFTEE) in conjunction with the gnomAD dataset to study loss of function (LoF) variants in genes of interest. With this set of predicted LoF variants, we generated a LoF frequency for each gene of interest taking into account whether the heterozygote or homozygote state is sufficient and/or necessary for clinical phenotype. These data are analyzed to determine whether each platelet-associated gene is relatively tolerant or intolerant to LoF mutations in the context of their clinical phenotypes. By this analysis, we found approximately 800 novel LoF variants in platelet-related genes in this population of >120,000 individuals. Affected genes known to cause disease phenotype in the heterozygous state (n=33) accounted for 27% of the mutations analyzed. With these data, we calculated the frequency of IPD in the general population secondary to LoF mutations and estimated the relative impact of dominant versus recessive cases of IPD. We demonstrate that the majority of manifest cases of IPD will be due to the dominantly inherited, haploinsufficient IPDs. The transcription factor gene subset (9 of the IPD associated genes) was the most intolerant to LoF variants based on ratio of observed vs. expected number of variants (pLI measurement). Interestingly, the severity of the platelet dysfunction and resultant bleeding from LoF mutations in this subset of genes is not directly related to their intolerance of these mutations. For instance, heterozygous LoF of RUNX1 result in a mild-moderate bleeding disorder; however, a pLI of 0.819 indicates this gene is moderately to very intolerant of LoF variants. These same LoF variants in RUNX1 predispose to myelodysplastic syndromes with a high risk of myeloid leukemia in the form of familial platelet disorder with predisposition to acute myeloid leukemia (FPD/AML), and likely this is driving LoF intolerance. Cytoskeletal protein encoding genes represent another subset of mostly LoF intolerant platelet-related genes. Intracellular protein encoding genes and granule protein genes have varied tolerance and platelet-associated receptor protein genes as a subgroup were most tolerant of haploinsufficiency. There were some genes with similar clinical bleeding phenotypes that had divergent tolerance to LoF. For instance, GP9 and GP1BB both cause Montreal Platelet Syndrome in the haploinsufficient state and had moderate intolerance to LoF mutations (pLI GP9 = 0.804; pLI GP1BB = 0.575). In contrast, while LoF mutations in GP1BA cause the same bleeding phenotype, this gene is much more tolerant to haploinsufficiency (pLI = 0.0002). These data indicate that perhaps there is another unidentified adverse condition associated with GP9 and GP1BB that is driving increased haploinsufficiency intolerance. In summary, we present a comprehensive analysis of known platelet-associated genes, the frequency of LoF mutations in these genes and their relative tolerance of the haploinsufficient state. These data generate an incidence of IPDs of ~0.18% in the general population. Importantly, these data also inform the driving mechanisms of LoF intolerance as there are defective genes resulting in similar bleeding phenotypes, but divergent tolerance to haploinsufficiency, indicating that further investigation is warranted for additional biology. Disclosures Lambert: CSL: Consultancy; Rigel: Consultancy; Sysmex: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Summus: Consultancy; Bayer: Membership on an entity's Board of Directors or advisory committees; Shionogi: Consultancy; Educational Concepts in Medicine: Consultancy. Poncz:Incyte Corporation: Consultancy, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".