1. Genetics and Genomics
  2. Microbiology and Infectious Disease

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control

  1. István Bartha
  2. Jonathan M Carlson
  3. Chanson J Brumme
  4. Paul J McLaren
  5. Zabrina L Brumme
  6. Mina John
  7. David W Haas
  8. Javier Martinez-Picado
  9. Judith Dalmau
  10. Cecilio López-Galíndez
  11. Concepción Casado
  12. Andri Rauch
  13. Huldrych F Günthard
  14. Enos Bernasconi
  15. Pietro Vernazza
  16. Thomas Klimkait
  17. Sabine Yerly
  18. Stephen J O’Brien
  19. Jennifer Listgarten
  20. Nico Pfeifer
  21. Christoph Lippert
  22. Nicolo Fusi
  23. Zoltán Kutalik
  24. Todd M Allen
  25. Viktor Müller
  26. P Richard Harrigan
  27. David Heckerman
  28. Amalio Telenti  Is a corresponding author
  29. Jacques Fellay  Is a corresponding author
  30. for the HIV Genome-to-Genome Study and the Swiss HIV Cohort Study
  1. École Polytechnique Fédérale de Lausanne, Switzerland
  2. University Hospital and University of Lausanne, Switzerland
  3. Eötvös Loránd University and the Hungarian Academy of Sciences, Hungary
  4. Swiss Institute of Bioinformatics, Switzerland
  5. Microsoft Research, United States
  6. BC Centre for Excellence in HIV/AIDS, Canada
  7. Simon Fraser University, Canada
  8. Murdoch University, Australia
  9. Vanderbilt University Medical Center, United States
  10. Universitat Autònoma de Barcelona, Spain
  11. Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain
  12. Instituto de Salud Carlos III, Spain
  13. University of Bern & Inselspital, Switzerland
  14. University Hospital and University of Zürich, Switzerland
  15. Regional Hospital of Lugano, Switzerland
  16. Cantonal Hospital, Switzerland
  17. University of Basel, Switzerland
  18. Geneva University Hospitals, Switzerland
  19. St. Petersburg State University, Russia
  20. Massachusetts General Hospital, United States
  21. University of British Columbia, Canada
https://doi.org/10.7554/eLife.01123
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Cite this article
  1. István Bartha
  2. Jonathan M Carlson
  3. Chanson J Brumme
  4. Paul J McLaren
  5. Zabrina L Brumme
  6. Mina John
  7. David W Haas
  8. Javier Martinez-Picado
  9. Judith Dalmau
  10. Cecilio López-Galíndez
  11. Concepción Casado
  12. Andri Rauch
  13. Huldrych F Günthard
  14. Enos Bernasconi
  15. Pietro Vernazza
  16. Thomas Klimkait
  17. Sabine Yerly
  18. Stephen J O’Brien
  19. Jennifer Listgarten
  20. Nico Pfeifer
  21. Christoph Lippert
  22. Nicolo Fusi
  23. Zoltán Kutalik
  24. Todd M Allen
  25. Viktor Müller
  26. P Richard Harrigan
  27. David Heckerman
  28. Amalio Telenti
  29. Jacques Fellay
  30. for the HIV Genome-to-Genome Study and the Swiss HIV Cohort Study
(2013)
A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control
eLife 2:e01123.
https://doi.org/10.7554/eLife.01123
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  3. Download .RIS
3 figures and 2 tables

Figures

Figure 1
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A triangle of association testing.

The following association analyses were performed: [Study A] human SNPs vs plasma viral load (1 GWAS); [Study B] human SNPs vs variable HIV-1 amino acids (3007 GWAS); and [Study C] variable HIV-1 amino acids vs plasma viral load (1 proteome-wide association study).

https://doi.org/10.7554/eLife.01123.003
Figure 2
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Results of the genome-wide association analyses.

(A) Associations between human SNPs and HIV-1 plasma viral load. The dotted line shows the Bonferroni-corrected significance threshold (p-value < 7.25 × 10−9). (B) Associations between human SNPs and HIV-1 amino acid variants, with 3007 GWAS collapsed in a single Manhattan plot. The dotted line shows the Bonferroni-corrected significance threshold (p-value < 2.4 × 10−12). (C) Schematic representation of the HLA class I genes and of the SNPs associated with HIV-1 amino acid variants in the region. (D) Same association results as in panel B, projected on the HIV-1 proteome. Only the strongest association is shown for each amino acid. Significant associations are indicated by a blue dot. The gp120 part of the HIV-1 proteome was not tested. The colored bar below the plot area shows the positions of the optimally defined CD8+ T cell epitopes. An interactive version of this figure can be found at http://g2g.labtelenti.org (which is also available to download from Zenodo, http://dx.doi.org/10.5281/zenodo.7138).

https://doi.org/10.7554/eLife.01123.004
Figure 3
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Association of HIV-1 amino acid variants with plasma viral load.

(A) Changes in VL (slope coefficients from the univariate regression model and standard error, log10 copies/ml) for the 48 HIV-1 amino acids that are associated with host SNPs in the genome-to-genome analysis. (B) rs2395029, a marker of HLA-B*57:01 is associated with a 0.38 log10 copies/ml lower VL (black bar) in comparison to the population mean. Gray bars represent changes in VL for amino acid variants associated with rs2395029 (p<0.001). In case of multiallelic positions, the change in VL is shown for all minor amino acids combined vs the major amino acid (e.g., GAG147 not I).

https://doi.org/10.7554/eLife.01123.006

Tables

Table 1

Associations between HIV-1 amino acid variants and human polymorphisms

https://doi.org/10.7554/eLife.01123.005
HIV geneHIV positionSNPCTL epitope (codons)Tagging HLA (D’/r2)SNP vs aa (p)SNP vs VL (p)aa vs VL (p)
GAG12chr6:31285512–B*49:01 (1.00/1.00)2.20E-136.70E-015.60E-01
GAG26rs12524487–B*15:01 (1.00/0.82)6.10E-192.10E-011.40E-01
GAG28rs1655912RLRPGGKKK (20–28)A*03:01 (1.00/0.81)2.70E-555.60E-012.00E-02
GAG79chr6:31267544LYNTVATL (78-85)C*14:02 (1.00/0.96)2.40E-123.50E-012.80E-01
GAG147rs1055821–C*06:02 (0.95/0.71)3.10E-173.30E-072.90E-05
GAG242rs73392116TSTLQEQIGW (240–249)B*57:01 (1.00/0.98)2.40E-621.90E-061.70E-05
GAG248rs41557213TSTLQEQIGW (240-249)B*57:01 (1.00/0.97)4.80E-152.00E-065.30E-03
GAG264chr6:31376564KRWIILGLNK (263–272)B*27:05 (1.00/0.92)2.30E-135.50E-023.50E-01
GAG268rs2249935GEIYKRWIIL (259–268)B*08:01 (1.00/0.43)2.20E-145.10E-011.90E-01
GAG340rs11966319–B*15:01 (0.94/0.42)6.70E-144.60E-017.70E-01
–C*03:04 (0.99/0.59)
GAG357rs2523612GPGHKARVL (355-363)B*07:02 (0.99/0.95)2.70E-192.20E-011.20E-01
–C*07:02 (0.99/0.84)
GAG397rs61754472–A*31:01 (0.97/0.83)8.80E-213.50E-018.30E-01
GAG403rs28896571––8.90E-217.90E-018.60E-01
GAG437rs34268928RQANFLGKI (429-437)B*13:02 (1.00/0.96)8.70E-141.80E-026.80E-02
GP41206rs17881210–B*15:01 (1.00/0.88)6.10E-176.10E-013.00E-01
GP41267rs9278477RLRDLLLIVTR (259–269)A*03:01 (1.00/0.01)1.00E-127.80E-012.60E-01
INT11rs2596477–B*44:02 (1.00/0.64)5.10E-331.50E-011.80E-01
INT32rs1050502–B*51:01 (0.97/0.92)4.80E-187.20E-014.00E-01
INT119rs9264954–C*05:01 (1.00/1.00)1.30E-247.90E-011.10E-01
INT122rs9264419–C*05:01 (1.00/0.95)4.50E-228.30E-017.80E-01
INT124chr6:31345421STTVKAACWW (123–132)B*57:01 (1.00/1.00)3.00E-131.10E-069.70E-03
NEF71rs2596488FPVTPQVPLR (68–77)B*07:02 (1.00/0.98)3.80E-552.50E-018.10E-02
–C*07:02 (0.95/0.83)
NEF81rs9295987RPMTYKAAL (77–85)B*07:02 (1.00/0.01)4.80E-362.50E-019.50E-02
–C*04:01 (0.90/0.63)
NEF83rs34768512–B*15:01 (1.00/0.47)2.20E-172.80E-011.50E-02
–C*03:04 (0.96/0.54)
NEF85rs2395475RPMTYKAAL (77–85)B*07:02 (1.00/0.29)1.90E-248.10E-011.30E-03
–B*08:01 (1.00/0.22)
–C*07:02 (0.97/0.30)
NEF92rs16896166AVDLSHFLK (84–92)A*11:01 (1.00/0.99)1.00E-275.30E-011.50E-01
NEF94rs9265972FLKEKGGL (90–97)B*08:01 (1.00/0.97)9.60E-359.80E-011.20E-01
NEF102rs2524277–B*44:03 (0.98/0.96)1.10E-134.40E-012.40E-01
NEF105rs1049709–C*07:01 (1.00/0.98)1.10E-359.00E-012.70E-01
NEF116chr6:31402358HTQGYFPDW (116–124)B*57:01 (1.00/1.00)3.00E-221.90E-063.30E-01
NEF120chr6:31236168-C*14:02 (1.00/1.00)4.40E-163.60E-011.20E-02
NEF126chr6:31102273–B*51:01 (1.00/0.18)1.10E-121.80E-014.90E-02
NEF133chr6:31397689–B*35:01 (0.95/0.89)2.80E-192.50E-013.40E-01
NEF135rs72845950RYPLTFGW (134–141)A*24:02 (1.00/0.88)2.70E-669.10E-025.50E-03
PR35rs2523577EEMNLPGRW (34-42)B*44:02 (1.00/0.64)1.70E-181.60E-015.70E-01
PR93rs2263323–B*15:01 (0.98/0.92)5.60E-304.70E-019.50E-01
RNASE28rs2428481–B*08:01 (1.00/1.00)1.80E-128.10E-016.20E-01
RT135rs1050502TAFTIPSI (128–135)B*51:01 (0.97/0.92)6.70E-457.20E-013.00E-01
RT245chr6:31411714IVLPEKDSW (244–252)B*57:01 (1.00/0.98)2.90E-211.20E-065.40E-02
RT277rs3128902QIYPGIKVR (269–277)A*03:01 (1.00/0.99)1.20E-658.20E-012.70E-01
RT369rs17190134–B*13:02 (0.93/0.86)3.50E-206.40E-021.40E-01
RT395rs17194293–-1.50E-121.20E-017.70E-02
TAT29rs9260615–A*32:01 (0.98/0.95)4.40E-143.90E-011.40E-01
TAT32rs16899214CCFHCQVC (30–37)C*12:03 (0.98/0.96)6.40E-213.40E-014.90E-01
VIF33chr6:31430060ISKKAKGWF (31–39)B*57:01 (1.00/0.98)1.50E-139.90E-079.30E-03
VIF51rs7767850–B*49:01 (1.00/1.00)1.40E-125.20E-012.10E-01
VIF74rs2395029–B*57:01 (1.00/0.98)5.40E-139.70E-072.80E-01
VPR32chr6:31362941VRHFPRIWL (31–39)B*27:05 (1.00/0.94)3.10E-135.40E-026.50E-01

  1. Significant associations (p < 2.4 × 10-12) were observed for 48 HIV-1 amino acid variants. The table shows the major amino acid variants present at each specific HIV-1 position, the strongest associated SNP and its linked HLA class I allele(s), if applicable. The column ‘CTL Epitope (codons)’ lists published, optimally described CTL epitopes (available at http://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html and in [Carlson et al., 2012]) restricted by the tagged HLA class I allele(s) specified, and their positions within the protein. Where multiple overlapping epitopes restricted by the same HLA class I allele have been described, only one is shown. Associations where no relevant CTL epitope has been described are indicated with a dash. The last three columns give association p-values for comparisons between human SNPs and viral amino acids, human SNPs and plasma VL and viral amino acids and plasma VL, respectively. For tests involving viral amino acids accommodating more than 1 alternate allele, the smallest association p-value observed at that position is reported.

Table 2

Distribution of samples across genotyping platforms and cohorts

https://doi.org/10.7554/eLife.01123.007
NGenotyping platformCohort
140Illumina 1MACTG
6Illumina OmniExpress 12v1HCARLOS III
518Affymetrix 6.0HOMER
136Illumina OmniExpress12v1HHOMER
47Illumina 650kIHCS
6Illumina 660W-QuadIRSICAIXA
2Illumina 1MSHCS
79Illumina 550kSHCS
122Illumina OmniExpress12v1HSHCS
15Illumina 550kWAHCS

  1. ACTG = AIDS Clinical Trials Group Network; CARLOS III = Instituto de Salud Carlos III; HOMER = HAART Observational Medical Evaluation and Research Study; IHCS = International HIV Controllers Study; IRSICAIXA = AIDS Research Institute IrsiCaixa; SHCS = Swiss HIV Cohort Study; WAHCS = Western Australian HIV Cohort Study.

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  1. István Bartha
  2. Jonathan M Carlson
  3. Chanson J Brumme
  4. Paul J McLaren
  5. Zabrina L Brumme
  6. Mina John
  7. David W Haas
  8. Javier Martinez-Picado
  9. Judith Dalmau
  10. Cecilio López-Galíndez
  11. Concepción Casado
  12. Andri Rauch
  13. Huldrych F Günthard
  14. Enos Bernasconi
  15. Pietro Vernazza
  16. Thomas Klimkait
  17. Sabine Yerly
  18. Stephen J O’Brien
  19. Jennifer Listgarten
  20. Nico Pfeifer
  21. Christoph Lippert
  22. Nicolo Fusi
  23. Zoltán Kutalik
  24. Todd M Allen
  25. Viktor Müller
  26. P Richard Harrigan
  27. David Heckerman
  28. Amalio Telenti
  29. Jacques Fellay
  30. for the HIV Genome-to-Genome Study and the Swiss HIV Cohort Study
(2013)
A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control
eLife 2:e01123.
https://doi.org/10.7554/eLife.01123