GWAS of Complete Blood Count (CBC) Measures in 13,403 Blood Donors in the Multi-Racial RBC-Omics Study Reveal Novel Genetic Loci in Minority Populations Which Provide Insights into the Pathways That May Connect Them to Disease
Bibliographic record
Abstract
Abstract Background: Blood cells, which constitute ~85% of cells in the human body, make fundamental contributions to oxygen transport, hemostasis, healing, and innate and acquired immune responses. Abnormalities of blood cell production and physiological/functional properties in healthy individuals are associated with predisposition to disorders including immunodeficiency, anemia, bleeding disorder, and cancer. However, few causal relationships between blood cell indices, genetics and disease risks have been established. The goal of this study is to identify associations between genetic factors and CBC parameters to gain insight into their regulation. Methods: The REDS-III Red Blood Cell Omics (RBC-Omics) study enrolled 13,403 blood donors at 4 major US blood centers. CBC measures including RBC, HGB, HCT, MCV, RDW, WBC, and PLT, were successfully run among 13,036 subjects including 8,225 non-Hispanic Caucasians, 1,581 African American (AA), 1,600 Asians, which were further stratified into East Asians (likely Chinese, Japanese and Korean ancestry) and South Asians (likely Indian ancestry), 1,009 Caucasian Hispanics, and 621 other or multi-racial donors. DNA was isolated and genotyped using a custom Affymetrix Axiom Transfusion Medicine Array, which contained approximately 875,000 SNPs enriched for blood and transfusion related polymorphisms. Results: Genome wide association in RBC-Omics Caucasians revealed multiple genome wide significant hits for every CBC measure. Comparing our results in Caucasians to the UK Biobank and INTERVAL studies of the same phenotypes revealed significant (P< 0.001) replication of 158 of their 1,080 genome wide significant loci. Further, our results in all donors replicated 89 of the 115 GW significant loci reported in the GWAS catalog with p < 0.00001. Analyses of the GWA results and CBC parameters in RBC-Omics minority donors' samples revealed multiple novel significant genome wide hits as well. For WBC counts, AA were genome wide significant for the genes: SLC1A2; East Asians: BLC9; South Asians: VPS45. For HCT in AA: RBM7/REX02; in South Asians: ENTHD1. For RBC concentrations in AA there were genome wide significance for the genes GAB3 and LUC7L; in South Asians: TEF. For PLT, in AA: NEB; in South Asians: JMJD1C. Additionally, a number of genome wide hits were found in between genes some in highly evolutionarily conserved or regulatory regions. Conclusion: Genetics plays a significant role in the control and regulation of WBC, RBC, and PLT counts as well as other blood related measures such as HCT, RDW, and HGB. Our data strongly replicated the UK Biobank results in Caucasians. We have also identified several novel genes in minority populations. Interestingly genes such as JMJD1C, IFI16, AIM2, and BLC9 have been previously associated with various cancers and leukemia and should be studied for variation in susceptibility to cancers in these populations. Studies in minority populations, when combined with studies in Caucasians, will provide greater insight into the regulation of control of blood. Disclosures Mast: Novo Nordisk: 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".