A39 Human exome sequencing to evaluate the impact of rare coding variation on HIV-1 control
Bibliographic record
Abstract
Common variants (>5% frequency) in the MHC and CCR5 regions are known to influence set point HIV-1 viral load (spVL) yet explain only a portion of the total trait variance. The impact of rare coding variation on HIV-1 disease progression has not been as thoroughly investigated. Here we utilize exome sequencing in 392 HIV-1 infected individuals with stable spVL to look for rare and functional variants that mediate control of HIV-1 infection. Set point HIV-1 viral load was calculated as the average of at least 3 measurements obtained during the chronic phase of infection. We captured and sequenced all coding exons in 392 HIV-1 infected individuals of the Swiss HIV Cohort Study using the Illumina Truseq 65Mb enrichment kit and the Illumina HiSeq2000. Quality control and variant calling were performed using the GATK and variant functional annotation was performed using snpEff version 3.3. Individual variants were tested for association using linear regression. Testing of the combined effects of multiple low frequency variants across each of >18,000 genes was performed using SCORE-Seq and SKAT. Consistent with previous results, single marker variant tests showed a strong signal of association in the MHC. The top association was observed between spVL and rs1131446 (P = 2.3 × 10−11) in exon 3 of HLA-B. Conditioning on this SNP, residual association was observed at rs2308622 (conditional P = 2.2 × 10−6) in HLA-C. Accounting for these two SNPs, no other variants showed evidence for association. Analyses aimed at detecting the combined effect of multiple low-frequency variants within a gene showed no significant associations. Restricting this analysis to only those variants that result in a change in protein sequence did not reveal further signals. Outside of the MHC, no significant impact of rare variation on spVL was detected by exome sequencing in 392 individuals. Larger samples are likely required to fully explore the role of rare coding variation on this phenotype. Additional classes of variation not detected by GWAS or current sequencing technologies may also contribute to host HIV-1 control.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".