Pharmacogenetics-based population pharmacokinetic analysis of etravirine in HIV-1 infected individuals.

OBJECTIVES: Etravirine (ETV) is metabolized by cytochrome P450 (CYP) 3A, 2C9, and 2C19. Metabolites are glucuronidated by uridine diphosphate glucuronosyltransferases (UGT). To identify the potential impact of genetic and non-genetic factors involved in ETV metabolism, we carried out a two-step pharmacogenetics-based population pharmacokinetic study in HIV-1 infected individuals. MATERIALS AND METHODS: The study population included 144 individuals contributing 289 ETV plasma concentrations and four individuals contributing 23 ETV plasma concentrations collected in a rich sampling design. Genetic variants [n=125 single-nucleotide polymorphisms (SNPs)] in 34 genes with a predicted role in ETV metabolism were selected. A first step population pharmacokinetic model included non-genetic and known genetic factors (seven SNPs in CYP2C, one SNP in CYP3A5) as covariates. Post-hoc individual ETV clearance (CL) was used in a second (discovery) step, in which the effect of the remaining 98 SNPs in CYP3A, P450 cytochrome oxidoreductase (POR), nuclear receptor genes, and UGTs was investigated. RESULTS: A one-compartment model with zero-order absorption best characterized ETV pharmacokinetics. The average ETV CL was 41 (l/h) (CV 51.1%), the volume of distribution was 1325 l, and the mean absorption time was 1.2 h. The administration of darunavir/ritonavir or tenofovir was the only non-genetic covariate influencing ETV CL significantly, resulting in a 40% [95% confidence interval (CI): 13-69%] and a 42% (95% CI: 17-68%) increase in ETV CL, respectively. Carriers of rs4244285 (CYP2C19*2) had 23% (8-38%) lower ETV CL. Co-administered antiretroviral agents and genetic factors explained 16% of the variance in ETV concentrations. None of the SNPs in the discovery step influenced ETV CL. CONCLUSION: ETV concentrations are highly variable, and co-administered antiretroviral agents and genetic factors explained only a modest part of the interindividual variability in ETV elimination. Opposing effects of interacting drugs effectively abrogate genetic influences on ETV CL, and vice-versa.

Ip-10 Is Associated With Il28b Variation And Predicts The First Phase Decline Of Hcv Rna And Outcome Of Therapy In Chronic Hepatitis C

High systemic levels of IP-10 at onset of combination therapy for chronic hepatitis C mirror intrahepatic mRNA levels and predict a slower first phase decline in HCV RNA as well as poor outcome. Recently several genome wide association studies have revealed that single nucleotide polymorphisms (SNPs) on chromosome19 within proximity of IL28B predict spontaneous clearance of HCV infection and as therapeutic outcome among patients infected with HCV genotype 1, with three such SNPs being highly predictive: rs12979860, rs12980275, and rs8099917. In the present study, we correlated genetic variations in these SNPs from 253 Caucasian patients with pretreatment plasma levels of IP-10 and HCV RNA throughout therapy within a phase III treatment trial (HCV-DITTO). The favorable genetic variations in all three SNPs (CC, AA, and TT respectively) was significantly associated with lower baseline IP-10 (CC vs. CT/TT at rs12979860: median 189 vs. 258 pg/mL, P=0.02, AA vs. AG/GG at rs12980275: median 189 vs. 258 pg/mL, P=0.01, TT vs. TG/GG at rs8099917: median 224 vs. 288 pg/mL, P=0.04), were significantly less common among HCV genotype 1 infected patients than genotype 2/3 (P<0.0001, P<0.0001, and P=0.01 respectively) and had significantly higher baseline viral load than carriers of the SNP genotypes (6.3 vs. 5.9 log 10 IU/mL, P=0.0012, 6.3 vs. 6.0 log 10 IU/mL, P=0.026, and 6.3 vs. 5.8 log 10 IU/mL, P=0.0003 respectively). Among HCV genotype 1 infected homozygous or heterogeneous carriers of the favorable C, A, and T genotypes, lower baseline IP-10 was significantly associated with greater decline in HCV-RNA day 0-4, which translated into increased rates of achieving SVR among homozygous patients with baseline IP-10 below 150 pg/mL (85%, 75%, and 75% respectively). In a multivariate analysis among genotype 1 infected patients, both baseline IP-10 and the SNPs were significant independent predictors of SVR. Conclusion: Baseline plasma IP-10 is significantly associated with IL28B variations, and augments the predictiveness of the first phase decline in HCV RNA and final treatment outcome.

Absence of association between specific common variants of the obesity-related FTO gene and psychological and behavioral eating disorder phenotypes.

Extensive population-based genome-wide association studies have identified an association between the FTO gene and BMI; however, the mechanism of action is still unknown. To determine whether FTO may influence weight regulation through psychological and behavioral factors, seven single-nucleotide polymorphisms (SNPs) of the FTO gene were genotyped in 1,085 individuals with anorexia nervosa (AN) and 677 healthy weight controls from the international Price Foundation Genetic Studies of Eating Disorders. Each SNP was tested in association with eating disorder phenotypes and measures that have previously been associated with eating behavior pathology: trait anxiety, harm-avoidance, novelty seeking, impulsivity, obsessionality, compulsivity, and concern over mistakes. After appropriate correction for multiple comparisons, no significant associations between individual FTO gene SNPs and eating disorder phenotypes or related eating behavior pathology were identified in cases or controls. Thus, this study found no evidence that FTO gene variants associated with weight regulation in the general population are associated with eating disorder phenotypes in AN participants or matched controls. © 2011 Wiley-Liss, Inc.

Polymorphisms in Toll-like receptor 9 influence the clinical course of HIV-1 infection.

BACKGROUND: The clinical course of HIV-1 infection is highly variable among individuals, at least in part as a result of genetic polymorphisms in the host. Toll-like receptors (TLRs) have a key role in innate immunity and mutations in the genes encoding these receptors have been associated with increased or decreased susceptibility to infections. OBJECTIVES: To determine whether single-nucleotide polymorphisms (SNPs) in TLR2-4 and TLR7-9 influenced the natural course of HIV-1 infection. METHODS: Twenty-eight SNPs in TLRs were analysed in HAART-naive HIV-positive patients from the Swiss HIV Cohort Study. The SNPs were detected using Sequenom technology. Haplotypes were inferred using an expectation-maximization algorithm. The CD4 T cell decline was calculated using a least-squares regression. Patients with a rapid CD4 cell decline, less than the 15th percentile, were defined as rapid progressors. The risk of rapid progression associated with SNPs was estimated using a logistic regression model. Other candidate risk factors included age, sex and risk groups (heterosexual, homosexual and intravenous drug use). RESULTS: Two SNPs in TLR9 (1635A/G and +1174G/A) in linkage disequilibrium were associated with the rapid progressor phenotype: for 1635A/G, odds ratio (OR), 3.9 [95% confidence interval (CI),1.7-9.2] for GA versus AA and OR, 4.7 (95% CI,1.9-12.0) for GG versus AA (P = 0.0008). CONCLUSION: Rapid progression of HIV-1 infection was associated with TLR9 polymorphisms. Because of its potential implications for intervention strategies and vaccine developments, additional epidemiological and experimental studies are needed to confirm this association.

Genetic variants influencing circulating lipid levels and risk of coronary artery disease.

OBJECTIVE: Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides. METHODS AND RESULTS: We combined genome-wide association data from 8 studies, comprising up to 17 723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37 774 participants from 8 populations and also in a population of Indian Asian descent. We also assessed the association between single-nucleotide polymorphisms (SNPs) at lipid loci and risk of CAD in up to 9 633 cases and 38 684 controls. We identified 4 novel genetic loci that showed reproducible associations with lipids (probability values, 1.6×10(-8) to 3.1×10(-10)). These include a potentially functional SNP in the SLC39A8 gene for HDL-C, an SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-C, and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with 1 or more lipid traits at the CELSR2, APOB, APOE-C1-C4-C2 cluster, LPL, ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (probability values, 1.1×10(-3) to 1.2×10(-9)). CONCLUSIONS: We have identified 4 novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-C, genetic loci mainly associated with circulating triglycerides and HDL-C are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.

Overlap Between Common Genetic Polymorphisms Underpinning Kidney Traits and Cardiovascular Disease Phenotypes: The CKDGen Consortium.

BACKGROUND: Chronic kidney disease is associated with cardiovascular disease. We tested for evidence of a shared genetic basis to these traits. STUDY DESIGN: We conducted 2 targeted analyses. First, we examined whether known single-nucleotide polymorphisms (SNPs) underpinning kidney traits were associated with a series of vascular phenotypes. Additionally, we tested whether vascular SNPs were associated with markers of kidney damage. Significance was set to 1.5×10(-4) (0.05/325 tests). SETTING & PARTICIPANTS: Vascular outcomes were analyzed in participants from the AortaGen (20,634), CARDIoGRAM (86,995), CHARGE Eye (15,358), CHARGE IMT (31,181), ICBP (69,395), and NeuroCHARGE (12,385) consortia. Tests for kidney outcomes were conducted in up to 67,093 participants from the CKDGen consortium. PREDICTOR: We used 19 kidney SNPs and 64 vascular SNPs. OUTCOMES & MEASUREMENTS: Vascular outcomes tested were blood pressure, coronary artery disease, carotid intima-media thickness, pulse wave velocity, retinal venular caliber, and brain white matter lesions. Kidney outcomes were estimated glomerular filtration rate and albuminuria. RESULTS: In general, we found that kidney disease variants were not associated with vascular phenotypes (127 of 133 tests were nonsignificant). The one exception was rs653178 near SH2B3 (SH2B adaptor protein 3), which showed direction-consistent association with systolic (P = 9.3 ×10(-10)) and diastolic (P = 1.6 ×10(-14)) blood pressure and coronary artery disease (P = 2.2 ×10(-6)), all previously reported. Similarly, the 64 SNPs associated with vascular phenotypes were not associated with kidney phenotypes (187 of 192 tests were nonsignificant), with the exception of 2 high-correlated SNPs at the SH2B3 locus (P = 1.06 ×10(-07) and P = 7.05 ×10(-08)). LIMITATIONS: The combined effect size of the SNPs for kidney and vascular outcomes may be too low to detect shared genetic associations. CONCLUSIONS: Overall, although we confirmed one locus (SH2B3) as associated with both kidney and cardiovascular disease, our primary findings suggest that there is little overlap between kidney and cardiovascular disease risk variants in the overall population. The reciprocal risks of kidney and cardiovascular disease may not be genetically mediated, but rather a function of the disease milieu itself.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

ADME pharmacogenetics: investigation of the pharmacokinetics of the antiretroviral agent lopinavir coformulated with ritonavir.

BACKGROUND: An ADME (absorption, distribution, metabolism and excretion)-pharmacogenetics association study may identify functional variants relevant to the pharmacokinetics of lopinavir co-formulated with ritonavir (LPV/r), a first-line anti-HIV agent. METHODS: An extensive search of literature and web resources helped select ADME genes and single nucleotide polymorphisms (SNPs, functional and HapMap tagging SNPs) with a proven or potentially relevant role in LPV/r pharmacokinetics. The study followed a two-stage design. Stage 1 (discovery) considered a Caucasian population (n=638) receiving LPV/r, where we selected 117 individuals with low LPV clearance (cases) and 90 individuals with high clearance (controls). Genotyping was performed by a 1536-SNP customized GoldenGate Illumina BeadArray. Stage 2 (confirmation) represented a replication study of candidate SNPs from the stage 1 in 148 individuals receiving LPV/r. The analysis led to formal population pharmacokinetic-pharmacogenetic modeling of demographic, environmental and candidate SNP effects. RESULTS: One thousand three hundred and eighty SNPs were successfully genotyped. Nine SNPs prioritized by the stage 1 analysis were brought to replication. Stage 2 confirmed the contribution of two functional SNPs in SLCO1B1, one functional SNP in ABCC2 and a tag SNP of the CYP3A locus in addition to body weight effect and ritonavir coadministration. According to the population pharmacokinetic-pharmacogenetic model, genetic variants explained 5% of LPV variability. Individuals homozygous rs11045819 (SLCO1B1*4) had a clearance of 12.6 l/h, compared with 5.4 l/h in the reference group, and 3.9 l/h in individuals with two or more variant alleles of rs4149056 (SLCO1B1*5), rs717620 (ABCC2) or rs6945984 (CYP3A). A subanalysis confirmed that although a significant part of the variance in LPV clearance was attributed to fluctuation in ritonavir levels, genetic variants had an additional effect on LPV clearance. CONCLUSION: The two-stage strategy successfully identified genetic variants affecting LPV/r pharmacokinetics. Such a general approach of ADME pharmacogenetics should be generalized to other drugs.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Genetic association analyses reveal a role for vitamin D insufficiency in hepatitis C virus-associated hepatocellular carcinoma development

Background: V itamin D insufficiency has been associated with the occurrence of various types of cancer, but causal relationships remain elusive. Methods: Associations between t he r isk o f HCV-related HCC development and CYP2R1 , GC, and DHCR7 genotypes, which are genetic determinants of reduced 25-OH-vitamin D3 (25[OH]D3) serum levels, were determined. Results: A t otal of 5604 HCV-infected patients, 1279 with a nd 4325 without progression to HCC, w ere identified. The well-known association between 25(OH)D3 s erum levels and variations in CYP2R1 ( rs1993116, rs10741657), GC ( rs2282679), a nd DHCR7 ( rs7944926, rs12785878) g enotypes was also apparent in patients w ith chronic hepatitis C. The same genotypes of t hese single nucleotide polymorphisms (SNPs), w hich are associated with reduced 25(OH)D3 s erum levels, were significantly associated with HCV-associated HCC (P=0.07 [OR=1.13] for CYP2R1 , P=0.007 [OR=1.56] for GC, P=0.003 [OR=1.42] for DHCR7; ORs for risk genotypes). In contrast, no association between t hese genetic variations and the o utcome of antiviral therapy with pegylated interferon-α and ribavirin ( P>0.2 for e ach SNP) or liver fibrosis progression rate (P>0.2 for each SNP) was observed, s uggesting a specific influence o f the genetic d eterminants of 25(OH)D3 s erum levels o n hepatocarcinogenesis. Conclusions: Our data suggest a relatively weak but functionally relevant role for vitamin D in the prevention of HCV-related HCC development. Controlled clinical trials to assess the benefit of vitamin D supplementation in HCVinfected patients with advanced liver fibrosis or cirrhosis are warranted.

Global Diversity Lines-A Five-Continent Reference Panel of Sequenced Drosophila melanogaster Strains.

Reference collections of multiple Drosophila lines with accumulating collections of "omics" data have proven especially valuable for the study of population genetics and complex trait genetics. Here we present a description of a resource collection of 84 strains of Drosophila melanogaster whose genome sequences were obtained after 12 generations of full-sib inbreeding. The initial rationale for this resource was to foster development of a systems biology platform for modeling metabolic regulation by the use of natural polymorphisms as perturbations. As reference lines, they are amenable to repeated phenotypic measurements, and already a large collection of metabolic traits have been assayed. Another key feature of these strains is their widespread geographic origin, coming from Beijing, Ithaca, Netherlands, Tasmania, and Zimbabwe. After obtaining 12.5× coverage of paired-end Illumina sequence reads, SNP and indel calls were made with the GATK platform. Thorough quality control was enabled by deep sequencing one line to >100×, and single-nucleotide polymorphisms and indels were validated using ddRAD-sequencing as an orthogonal platform. In addition, a series of preliminary population genetic tests were performed with these single-nucleotide polymorphism data for assessment of data quality. We found 83 segregating inversions among the lines, and as expected these were especially abundant in the African sample. We anticipate that this will make a useful addition to the set of reference D. melanogaster strains, thanks to its geographic structuring and unusually high level of genetic diversity.

IL1B and DEFB1 Polymorphisms Increase Susceptibility to Invasive Mold Infection After Solid-Organ Transplantation.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) in immune genes have been associated with susceptibility to invasive mold infection (IMI) among hematopoietic stem cell but not solid-organ transplant (SOT) recipients. METHODS: Twenty-four SNPs from systematically selected genes were genotyped among 1101 SOT recipients (715 kidney transplant recipients, 190 liver transplant recipients, 102 lung transplant recipients, 79 heart transplant recipients, and 15 recipients of other transplants) from the Swiss Transplant Cohort Study. Association between SNPs and the end point were assessed by log-rank test and Cox regression models. Cytokine production upon Aspergillus stimulation was measured by enzyme-linked immunosorbent assay in peripheral blood mononuclear cells (PBMCs) from healthy volunteers and correlated with relevant genotypes. RESULTS: Mold colonization (n = 45) and proven/probable IMI (n = 26) were associated with polymorphisms in the genes encoding interleukin 1β (IL1B; rs16944; recessive mode, P = .001 for colonization and P = .00005 for IMI, by the log-rank test), interleukin 1 receptor antagonist (IL1RN; rs419598; P = .01 and P = .02, respectively), and β-defensin 1 (DEFB1; rs1800972; P = .001 and P = .0002, respectively). The associations with IL1B and DEFB1 remained significant in a multivariate regression model (P = .002 for IL1B rs16944; P = .01 for DEFB1 rs1800972). The presence of 2 copies of the rare allele of rs16944 or rs419598 was associated with reduced Aspergillus-induced interleukin 1β and tumor necrosis factor α secretion by PBMCs. CONCLUSIONS: Functional polymorphisms in IL1B and DEFB1 influence susceptibility to mold infection in SOT recipients. This observation may contribute to individual risk stratification.

Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption

Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91 462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (P<5 × 10-8).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.