Host genetic variation at a locus near CHD1L impacts HIV sequence diversity in a South African population
Bibliographic record
Abstract
ABSTRACT There is variability in viral load (VL) among individuals with untreated human immunodeficiency virus (HIV) infection, and this variability can be partly attributed to host genetics. HIV is known to develop escape mutations to evade host immune pressure, particularly from HLA alleles and, in some cases, counteracts the protective effect of host alleles. A recent genome-wide association study (GWAS) of HIV VL in individuals of African ancestry identified a locus on chromosome 1, near the protein-coding gene chromodomain helicase DNA-binding protein 1 like ( CHD1L ), that has a novel association with control of HIV replication. However, not all individuals carrying the protective alleles maintain low VL, and the region’s impact on viral evolution has not been investigated. To address this, we conducted a host-virus regional association analysis in 147 people living with HIV (PLWH) from South Africa with both human and viral genome data available. We observed significant associations between the CHD1L variants rs77029719 (G) ( P = 1.6 × 10 −2 ), rs7519713 (T) ( P = 2.3 × 10 −2 ), and rs59784663 (G) and 73004025 (T) ( P = 1.4 × 10 −2 ) with codon 248 of HIV reverse transcriptase (RT) and between CHD1L variant rs7519713 (T) and codon 18 ( P = 3.2 × 10 −2 ) and 147 ( P = 3.9 × 10 −2 ) of HIV gag. These associations are consistent with viral escape from CHD1L pressure. In addition, we observed significant associations between HLA B*81 ( P = 1.5 × 10 −5 ) and HLA C*18 ( P = 7.0 × 10 −4 ) with RT codon 4 and HLAB*58 with RT codon 196 ( P = 9.0 × 10 −4 ). This study reveals new evidence of host genetic variation impacting viral evolution in a population highly affected by HIV. IMPORTANCE It has been previously shown that genetic variants near CHD1L on chromosome 1 are associated with reduced HIV VL in African populations. However, the impact of these variants on viral diversity and how they restrict viral replication are unknown. We report on a regional association analysis in a South African population and show evidence of selective pressure by variants near CHD1L on HIV RT and gag. Our findings provide further insight into how genetic variability at this locus contributes to host control of HIV in a South African population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".