Abstract 4629: Multiple myeloma susceptibility loci examined in African and European ancestry populations
Bibliographic record
Abstract
Abstract Genome-wide association studies (GWAS) of multiple myeloma (MM) in Northern Europeans have identified seven novel risk loci (2p23.3, 3p22.1, 3q26.2, 6p21.33, 7p15.3, 17p11.2, 22q13.1). We performed a multiethnic meta-analysis of these regions in 1,274 MM patients and 1,486 controls of European ancestry (EA) and 1,049 MM patients and 7,080 controls of African ancestry (AA), leveraging the differential linkage-disequilibrium of these populations in order to better localize the putative functional variants. We observed directionally consistent effects for all seven index SNPs in both populations, with four significantly associated (p<0.05) with risk in EAs (3p22.1, 7p15.3, 17p11.2, 22q13.1), and two significantly associated with risk in AAs (7p15.3 and 22q13.1). In a fixed effects meta-analysis of six regions (excluding the HLA region on chromosome 6), variation in five of the regions (2p33.3, 3p22.1, 7p15.3, 17p11.2, 22q13.1) had statistically significant associations with risk (Table 1). In one region, the index variant had the strongest association [rs4487645 at 7p15.3, (OR = 1.30, p = 8.7×10−8)]. Five of the six most significantly associated variants identified in the multiethnic analyses overlapped with biologically relevant features indicating regulatory activity based on CD20+ (B lymphocyte) cells, showing evidence of potential function; those included a missense variant in (17p11.2, rs34562254, Pro251Leu) in TNFRSF13B, which encodes a lymphocyte-specific protein in the tumor necrosis factor receptor family that interacts with the NF-kb pathway. Our study shows that these regions are important in MM risk across ethnicities and further supports the use of multiple ethnic groups in genetic studies to enhance identification of risk variants. Table 1.Most significant associations for each region in the multiethnic meta-analysis.Individuals of European AncestryIndividuals of African AncestryMulitethnic Metar2 with IndexcCHRSNPRAaFreqbORP-valueFreqbORP-valueORP-valueP-het2rs732075G0.591.222.0×10−30.621.122.0×10−21.162.6×10−40.280.09/0.283rs73069394A0.191.243.0×10−30.621.181.5×10−21.201.3×10−50.550.77/0.963rs12637184G0.761.136.0×10−20.921.192.7×10−11.151.0×10−20.640.94/1.007rs4487645C0.671.237.0×10−40.891.485.5×10−51.308.7×10−80.07-d17rs34562254A0.121.452.4×10−50.131.212.2×10−31.312.5×10−60.120.33/0.9022rs139400T0.491.224.0×10−40.531.172.1×10−31.191.2×10−60.630.63/0.96aRisk allelebFrequency of the risk allele in European and African ancestry studiescr2 metrics based on 1000 Genomes Project AFR/EUR populationsdIndex SNP Citation Format: Kristin A. Rand, Chi Song, Eric Dean, Daniel Serie, Karen Curtin, Dennis Hazelett, Amie E. Hwang, Xin Sheng, Alex Stram, David J. Van Den Berg, Carol Ann Huff, Leon Bernal-Mizrachi, Michael H. Tomasson, Sikander Ailawadhi, Anneclaire De Roos, Seema Singhal, Karen Pawlish, Edward Peters, Catherine Bock, David V. Conti, Graham Colditz, Todd Zimmerman, Scott Huntsman, John Graff, African Ancestry Prostate Cancer GWAS Consortium,African Ancestry Breast Cancer GWAS Consortium, Stephen J. Chanock, Michael Lieber, Jayesh Mehta, Eric A. Klein, Nalini Janakiraman, Richard K. Severson, Angela R. Brooks-Wilson, Vincent Rajkumar, Elizabeth E. Brown, Laurence Kolonel, Susan Slager, Brian E. Henderson, Graham G. Giles, John J. Spinelli, Brian Chiu, Kenneth C. Anderson, Jeffrey Zonder, Robert Z. Orlowski, Sagar Lonial, Nicola Camp, Celine Vachon, Elad Ziv, Dan O. Stram, Christopher A. Haiman, Wendy Cozen. Multiple myeloma susceptibility loci examined in African and European ancestry populations. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4629. doi:10.1158/1538-7445.AM2015-4629
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".