Variants in Host Viral Replication Cycle Genes Are Associated With Heterosexual HIV-1 Acquisition in Africans
Bibliographic record
Abstract
OBJECTIVE: We evaluated genetic variants in 51 candidate genes encoding proteins that interact with HIV-1 during the virus life cycle for association with HIV-1 outcomes in an African cohort. METHODS: Using a nested case-control study within a cohort of heterosexual HIV-1-serodiscordant couples, we genotyped 475 haplotype-tagging single-nucleotide polymorphisms (tagSNPs) and 18 SNPs previously associated with HIV-1 transmission and/or progression (candidate SNPs) in 51 host genes. We used logistic and Cox proportional hazard regression with adjustment for sex, age, and population stratification to detect SNP associations with HIV-1 acquisition, plasma HIV-1 set point, and a composite measure of HIV-1 disease progression. Significant thresholds for tagSNP, but not candidate SNP, associations were subjected to Bonferroni correction for multiple testing. RESULTS: We evaluated 491 HIV-1-infected and 335 HIV-1-uninfected individuals for 493 SNPs, 459 of which passed quality control filters. Candidate SNP PPIA rs8177826 and tagSNP SMARCB1 rs6003904 were significantly associated with HIV-1 acquisition risk (odds ratio = 0.14, P = 0.03, and odds ratio = 2.11, Pcorr = 0.01, respectively). Furthermore, the TT genotype for CCR5 rs1799988 was associated with a mean 0.2 log10 copies per milliliter lower plasma HIV-1 RNA set point (P = 0.04). We also identified significant associations with HIV-1 disease progression for variants in FUT2 and MBL2. CONCLUSIONS: Using a targeted gene approach, we identified variants in host genes whose protein products interact with HIV-1 during the virus replication cycle and were associated with HIV-1 outcomes in this African cohort.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".