42599 base pairs of sequence data covering Gag, Pol and Nef were exchanged and analyzed using site-specific software and proce- dures in a blinded fashion. Overall inter-laboratory concordance was 42429 out of 42599 calls, or 99.6%. Of the 170 discordant calls, 169 (99.4%) were due to the presence of a nucleotide mixture called by one laboratory but not the other, with a tendency of the Vancouver lab to call more mixtures than the Perth lab. After verification of inter-laboratory concordance, Gag, Pol and Nef sequences were extracted from the nearly-full genome WAHCS/ ACTG sequences using GeneCutter (http://www.hiv.lanl.gov/ content/sequence/GENE_CUTTER/cutter.html) before merging with the BC HOMER sequence data. The merged sequence datasets were aligned to HIV-1 subtype B reference strain HXB2 (GenBank Accession No. K03455) using a modified NAP algorithm [40]. HLA class I types were summarized to two-digit resolution. Final HLA/ HIV sequence datasets comprised N = 1294, 1383 and 1299 for Gag, Pol and Nef, respectively. Sequence subtypes verified by comparison to subtype references in the Los Alamos HIV Database (http://www. hiv.lanl.gov); .95% of sequences in this study were subtype B. GenBank Accession Numbers Gag, protease/RT (codons 1–400 only) and nef sequences from the HOMER cohort were previously deposited in GenBank [15,38]. Accession numbers of additional HOMER protease/RT (codon 1–400 only) sequences included in the present study are GQ303719-GQ303727; full-length protease/RT sequences are GQ303728-GQ303867 and HOMER RT codon 401–560 sequences are GQ303868-GQ304249. HOMER integrase se- quences are FJ812899-FJ813480. Linked HLA/HIV datasets from the BC HOMER cohort are available for sharing with individual researchers following application to, and approval by the UBC/ Providence Health Care Research Ethics Board; please contact the corresponding author for more information. GenBank Accession Numbers for ACTG 5142/5128 cohort sequences are GQ371216- GQ371763 (Gag), GQ371764-GQ372317 (Pol) and GQ372318- GQ372824 plus GQ398382-GQ398387 (Nef). GenBank Acces- sion numbers for the full/partial HIV genome sequences from WAHCS, from which Gag, Pol and Nef were extracted and used here, are AY856956-AY857186. Identification of HLA-associated polymorphisms: Overview The identification of HLA-associated polymorphisms in popu- lation-based datasets is complicated by three potential confound- ing factors: HIV phylogeny, HIV codon covariation, and Linkage Disequilibrium between HLA alleles [27]. HIV phylogeny acts as a confounder because HIV sequences are related to one another through descent from a common ancestor, with sequences displaying greater or lesser similarity to one another depending on the length of time since divergence. Thus, statistical tests that assume independent and identically distributed (iid) observations, such as chi-squared or Fisher’s exact tests, may lead to inflated false-negative and false-positive rates if applied directly [27,37]. Similarly, Linkage Disequilibrium (LD) between HLA alleles also exerts confounding effects. If LD is not addressed, linked HLA alleles may appear associated with the same mutational patterns, when in reality, escape is driven by one allele only [14,15,27,28,29]. For this reason, analytical methods have been developed to account for both HIV phylogeny and HLA LD [27]. HIV codon covariation acts as an additional, albeit more subtle, confounder [27]. Although the phylogenetic tree adjusts for the underlying evolutionary relationships between HIV sequences, immune selection pressures may lead to reproducible patterns of mutations at linked sites, even in sequences located far apart in the HIV-1 Immune Escape Maps tree. An example of this may be an escape pattern where a primary mutation is first selected in context of a specific HLA allele, followed by a compensatory mutation at a secondary site [24,35,36]. If codon covariation is not accounted for, both the primary and secondary sites may be identified as being associated with the HLA allele in question. Technically, this result would not be incorrect (as both primary and secondary mutations are selected by the HLA allele). However, if the goal is to discriminate between HLA-associated polymorphisms selected directly (for example, mutations that compromise epitope processing, peptide- HLA binding and/or T-cell recognition), from those that are selected indirectly (such as compensatory or secondary mutations), correction for codon covariation is necessary. A method that simultaneously accounts for HIV phylogeny and codon covaria- tion, in addition to HLA linkage disequilibrium, has recently been developed by Carlson et al [27], and we have applied it in this study. Identification of HLA-associated polymorphisms: statistical methods A detailed description of the HIV Phylogeny, HLA LD and HIV codon-covariation-corrected method is published in [27]. Briefly, a maximum likelihood phylogenetic tree is constructed for each gene and a model of conditional adaptation is inferred for each observed amino acid at each codon. In this model, the amino acid is assumed to evolve independently down the phylogeny, until it reaches the observed hosts. In each host, the selection pressure arising from HLA-mediated T-cell responses and amino acid covariation is directly modeled using a stochastic additive process. To identify which factors contribute to the selection pressure, a forward selection procedure is employed, in which the most significant association is iteratively added to the model, with p- values computed using the likelihood ratio test. To increase our statistical power, each codon is divided into a set of binary variables, one for each observed amino acid. In addition, we only consider pairs of variables for which the observed or expected value for each value of the contingency table is at least three. Definition of Statistical Significance Statistical significance is reported using q-values, the p-value analogue of the false discovery rate (FDR) for each p-value threshold [43]. The FDR is the expected proportion of false positives among results deemed significant at a given threshold. For example, at a q#0.2, we expect a false-positive proportion of 20% among identified associations. The q-value threshold used for constructing the immune escape maps was q#0.05, meaning that we would expect only a 5% false-positive proportion among associations displayed on our maps. Tables S1 and S2 list all HLA- and covariation associations, respectively, with q#0.2 (see supporting information). Classification and Nomenclature of HLA-associated polymorphisms Using this method, HLA-associated polymorphisms are grouped into two categories: (1) amino acids significantly enriched in the presence of the HLA allele in question (and vice versa), and (2) amino acids significantly depleted in the presence of the HLA allele in question (and vice versa). We refer to these two categories as the ‘‘adapted’’ and ‘‘nonadapted’’ forms, respectively. Previous studies, including some by our group, have employed various nomenclature systems for these polymorphisms: the ‘‘adapted’’ forms may also be referred to as ‘‘escape mutants’’ or ‘‘resistant forms’’, while the ‘‘nonadapted’’ forms have been referred to as PLoS ONE | www.plosone.org 3 August 2009 | Volume 4 | Issue 8 | e6687