Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA–viral peptide interaction as the major factor modulating durable control of HIV infection.
Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with ‘true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05–0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r2, increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r2 improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r2 increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results.
genotype imputation; GWAS; GoNL; rare variants; reference sets; reference panel
Genome-wide association studies (GWAS) have identified thousands of loci associated wtih complex traits, but it is challenging to pinpoint causal genes in these loci and to exploit subtle association signals. We used tissue-specific quantitative interaction proteomics to map a network of five genes involved in the Mendelian disorder long QT syndrome (LQTS). We integrated the LQTS network with GWAS loci from the corresponding common complex trait, QT interval variation, to identify candidate genes that were subsequently confirmed in Xenopus laevis oocytes and zebrafish. We used the LQTS protein network to filter weak GWAS signals by identifying single nucleotide polymorphisms (SNPs) in proximity to genes in the network supported by strong proteomic evidence. Three SNPs passing this filter reached genome-wide significance after replication genotyping. Overall, we present a general strategy to propose candidates in GWAS loci for functional studies and to systematically filter subtle association signals using tissue-specific quantitative interaction proteomics.
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal Mendelian Long QT Syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals we identified 35 common variant QT interval loci, that collectively explain ∼8-10% of QT variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 novel QT loci in 298 unrelated LQTS probands identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode for proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies novel candidate genes for ventricular arrhythmias, LQTS,and SCD.
genome-wide association study; QT interval; Long QT Syndrome; sudden cardiac death; myocardial repolarization; arrhythmias
Marginal zone lymphoma (MZL) is the third most common subtype of B-cell non-Hodgkin lymphoma. Here we perform a two-stage GWAS of 1,281 MZL cases and 7,127 controls of European ancestry and identify two independent loci near BTNL2 (rs9461741, P=3.95 × 10−15) and HLA-B (rs2922994, P=2.43 × 10−9) in the HLA region significantly associated with MZL risk. This is the first evidence that genetic variation in the major histocompatibility complex influences MZL susceptibility.
Marginal zone lymphoma (MZL) is a common subtype of B-cell non-Hodgkin lymphoma. Here the authors carry out a two-stage genome-wide association study in over 8,000 Europeans and identify two new MZL risk loci at chromosome 6p, implicating the major histocompatibility complex in the disease for the first time.
Motivation: Recently, investigators have proposed state-of-the-art Identity-by-descent (IBD) mapping methods to detect IBD segments between purportedly unrelated individuals. The IBD information can then be used for association testing in genetic association studies. One approach for this IBD association testing strategy is to test for excessive IBD between pairs of cases (‘pairwise method’). However, this approach is inefficient because it requires a large number of permutations. Moreover, a limited number of permutations define a lower bound for P-values, which makes fine-mapping of associated regions difficult because, in practice, a much larger genomic region is implicated than the region that is actually associated.
Results: In this article, we introduce a new pairwise method ‘Fast-Pairwise’. Fast-Pairwise uses importance sampling to improve efficiency and enable approximation of extremely small P-values. Fast-Pairwise method takes only days to complete a genome-wide scan. In the application to the WTCCC type 1 diabetes data, Fast-Pairwise successfully fine-maps a known human leukocyte antigen gene that is known to cause the disease.
Availability: Fast-Pairwise is publicly available at: http://genetics.cs.ucla.edu/graphibd.
Supplementary data are available at Bioinformatics online.
To identify novel genetic risk factors for rheumatoid arthritis (RA), we conducted a genome-wide association study (GWAS) meta-analysis of 5,539 autoantibody positive RA cases and 20,169 controls of European descent, followed by replication in an independent set of 6,768 RA cases and 8,806 controls. Of 34 SNPs selected for replication, 7 novel RA risk alleles were identified at genome-wide significance (P<5×10−8) in analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5, and PXK. We also refined the risk alleles at two established RA risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed RA risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P<0.05, many of which are validated autoimmune risk alleles, suggesting that most represent bona fide RA risk alleles.
Background and Purpose
Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The NINDS Stroke Genetics Network (SiGN) contributes substantially to meta-analyses that focus on specific subtypes of stroke.
The NINDS Stroke Genetics Network (SiGN) includes ischemic stroke cases from 24 Genetic Research Centers (GRCs), 13 from the US and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the web-based Causative Classification of Stroke (CCS) system, with data entered by trained and certified adjudicators at participating GRCs. Through the Center for Inherited Diseases Research (CIDR), SiGN plans to genotype 10,296 carefully phenotyped stroke cases using genome-wide SNP arrays, and add to these another 4,253 previously genotyped cases for a total of 14,549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. CIDR-generated genotypes and corresponding phenotypic data will be shared with the scientific community through dbGaP, and brain MRI studies will be centrally archived.
The SiGN consortium, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.
ischemic stroke; genetics; genomics
Genome-wide association studies (GWAS) have begun to identify the common genetic component to ischaemic stroke (IS). However, IS has considerable phenotypic heterogeneity. Where clinical covariates explain a large fraction of disease risk, covariate informed designs can increase power to detect associations. As prevalence rates in IS are markedly affected by age, and younger onset cases may have higher genetic predisposition, we investigated whether an age-at-onset informed approach could detect novel associations with IS and its subtypes; cardioembolic (CE), large artery atherosclerosis (LAA) and small vessel disease (SVD) in 6,778 cases of European ancestry and 12,095 ancestry-matched controls. Regression analysis to identify SNP associations was performed on posterior liabilities after conditioning on age-at-onset and affection status. We sought further evidence of an association with LAA in 1,881 cases and 50,817 controls, and examined mRNA expression levels of the nearby genes in atherosclerotic carotid artery plaques. Secondly, we performed permutation analyses to evaluate the extent to which age-at-onset informed analysis improves significance for novel loci. We identified a novel association with an MMP12 locus in LAA (rs660599; p = 2.5×10−7), with independent replication in a second population (p = 0.0048, OR(95% CI) = 1.18(1.05–1.32); meta-analysis p = 2.6×10−8). The nearby gene, MMP12, was significantly overexpressed in carotid plaques compared to atherosclerosis-free control arteries (p = 1.2×10−15; fold change = 335.6). Permutation analyses demonstrated improved significance for associations when accounting for age-at-onset in all four stroke phenotypes (p<0.001). Our results show that a covariate-informed design, by adjusting for age-at-onset of stroke, can detect variants not identified by conventional GWAS.
Ischaemic stroke places an enormous burden on global healthcare. However, the disease processes that lead to stroke are not fully understood. Genome-wide association studies have recently established that common genetic variants can increase risk of ischaemic stroke and its subtypes. In this study, we aimed to identify novel genetic associations with ischaemic stroke and its subtypes by addressing the fact that younger onset cases may have a stronger genetic component, and using this information in our analyses. We identify a novel genetic variant on chromosome 11 (rs660599), which is associated with increased risk of large artery stroke. We also show that mRNA expression of the nearest gene (MMP12) is higher in arteries with the disease process underlying large artery stroke (atherosclerosis). Finally, we evaluate our novel analysis approach, and show that our method is likely to identify further associations with ischaemic stroke.
Single nucleotide polymorphisms in the APOA5-A4-C3-A1 gene complex are associated with elevated plasma triglycerides and elevated vascular risk in healthy populations. In patients with clinically manifest vascular disease, hypertriglyceridemia and metabolic syndrome are frequently present, but the contribution of these single nucleotide polymorphisms to plasma triglycerides, effect modification by obesity and risk of recurrent vascular events is unknown in these patients.
Prospective cohort study of 5547 patients with vascular disease. Rs964184 (APOA5-A4-C3-A1 gene complex) was genotyped, and we evaluated the relation with plasma lipid levels, presence of metabolic syndrome and the risk for new vascular events.
The minor allele of rs964184 was strongly associated with log plasma triglycerides (β 0.12; 95%CI 0.10-0.15, p = 1.1*10−19), and was also associated with 0.03 mmol/L lower high-density lipoprotein-cholesterol (95%CI 0.01–0.04), and 0.14 mmol/L higher non-high-density lipoprotein-cholesterol (95%CI 0.09–0.20). The minor allele frequency increased from 10.9% in patients with plasma triglycerides <1 mmol/L to 24.6% in patients with plasma triglycerides between 4 and 10 mmol/L. The relation between rs964184 and plasma triglycerides was modified by body mass index in patients with one minor allele (β 0.02; (95%CI −0.04–0.09) if body mass index <24 kg/m2, β 0.17 (95%CI 0.12–0.22) if body mass index >27 kg/m2, p for interaction = 0.02). The prevalence of the metabolic syndrome increased from 52% for patients with two copies of the major allele to 62% for patients with two copies of the minor allele (p = 0.01). Rs964184 was not related with recurrent vascular events (HR 0.99; 95%CI 0.86–1.13).
The single nucleotide polymorphism rs964184 (APOA5-A4-C3-A1) is associated with elevated plasma triglycerides concentrations in patients with clinically manifest vascular disease. In carriers of one minor allele, the effect on plasma triglycerides was modified by body mass index. There is no relation between rs964184 and recurrent vascular events in these patients.
Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate–increasing and heart rate–decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA). In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of “public controls” and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects). The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.
genome-wide association study; case-control study; genetic association study; population stratification; power
Coronary artery disease (CAD) is a complex disease driven by myriad interactions of genetics and environmental factors. Traditionally, studies have analyzed only 1 disease factor at a time, providing useful but limited understanding of the underlying etiology. Recent advances in cost-effective and high-throughput technologies, such as single nucleotide polymorphism (SNP) genotyping, exome/genome/RNA sequencing, gene expression microarrays, and metabolomics assays have enabled the collection of millions of data points in many thousands of individuals. In order to make sense of such 'omics' data, effective analytical methods are needed. We review and highlight some of the main results in this area, focusing on integrative approaches that consider multiple modalities simultaneously. Such analyses have the potential to uncover the genetic basis of CAD, produce genomic risk scores (GRS) for disease prediction, disentangle the complex interactions underlying disease, and predict response to treatment.
Coronary artery disease; Coronary heart disease; Genomics; Systems biology; Mendelian randomization; Metabolites; Network analysis; Molecular systems model
Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped ∼50 000 single-nucleotide polymorphisms (SNPs) that capture variation in ∼2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P < 2.4 × 10−6). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.
Since the completion of the Human Genome Project, the field of human genetics has been in great flux, largely due to technological advances in studying DNA sequence variation. Although community-wide adoption of statistical standards was key to the success of genome-wide association studies, similar standards have not yet been globally applied to the processing and interpretation of sequencing data. It has proven particularly challenging to pinpoint unequivocally disease variants in sequencing studies of polygenic traits. Here, we comment on a number of factors that may contribute to irreproducible claims of association in scientific literature and discuss possible steps that we can take towards cultural change.
association; GWAS; next-generation sequencing; significance; bias; complex traits
Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent–offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910–1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14–15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.
whole-genome sequence; trio-design; population genetics
3% of the population develops saccular intracranial aneurysms (sIAs), a complex trait, with a sporadic and a familial form. Subarachnoid hemorrhage from sIA (sIA-SAH) is a devastating form of stroke. Certain rare genetic variants are enriched in the Finns, a population isolate with a small founder population and bottleneck events. As the sIA-SAH incidence in Finland is >2× increased, such variants may associate with sIA in the Finnish population. We tested 9.4 million variants for association in 760 Finnish sIA patients (enriched for familial sIA), and in 2,513 matched controls with case-control status and with the number of sIAs. The most promising loci (p<5E-6) were replicated in 858 Finnish sIA patients and 4,048 controls. The frequencies and effect sizes of the replicated variants were compared to a continental European population using 717 Dutch cases and 3,004 controls. We discovered four new high-risk loci with low frequency lead variants. Three were associated with the case-control status: 2q23.3 (MAF 2.1%, OR 1.89, p 1.42×10-9); 5q31.3 (MAF 2.7%, OR 1.66, p 3.17×10-8); 6q24.2 (MAF 2.6%, OR 1.87, p 1.87×10-11) and one with the number of sIAs: 7p22.1 (MAF 3.3%, RR 1.59, p 6.08×-9). Two of the associations (5q31.3, 6q24.2) replicated in the Dutch sample. The 7p22.1 locus was strongly differentiated; the lead variant was more frequent in Finland (4.6%) than in the Netherlands (0.3%). Additionally, we replicated a previously inconclusive locus on 2q33.1 in all samples tested (OR 1.27, p 1.87×10-12). The five loci explain 2.1% of the sIA heritability in Finland, and may relate to, but not explain, the increased incidence of sIA-SAH in Finland. This study illustrates the utility of population isolates, familial enrichment, dense genotype imputation and alternate phenotyping in search for variants associated with complex diseases.
Genome-wide association studies (GWAS) have been extensively used to identify common genetic variants associated with complex diseases. As common genetic variants have explained only a small fraction of the heritability of most complex diseases, there is a growing interest in the role of how low frequency and rare variants contribute to the susceptibility. Low frequency variants are more often specific to populations of distinct ancestries. Saccular intracranial aneurysms (sIA) are balloon-like dilatations in the arteries on the surface of the brain. The rupture of sIA causes life-threatening intracranial bleeding. sIA is a complex disease, which is known to sometimes run in families. Here, we utilize the recent advancements in knowledge of genetic variation in different populations to examine the role of low-frequency variants in sIA disease in the isolated population of Finland where sIA related strokes are more common than in most other populations. By studying >8000 Finns we identify four low-frequency variants associated with the sIA disease. We also show that the association of two of the variants are seen in other European populations as well. Our findings demonstrate that multiple study designs are needed to uncover more comprehensively their genetic background, including population isolates.
The objective of this study is to comprehensively define the genetic basis of Early Onset Myasthenia Gravis.
We have carried out a two-stage genome-wide association study on a total of 649 North European EOMG patients. Cases were matched 1:4 with controls of European ancestry. We performed imputation and conditional analyses across the major histocompatibility complex, as well as in the top regions of association outside the HLA region.
We observed the strongest association in the HLA class I region at rs7750641 (p = 1.2 × 10−92, OR = 6.25). By imputation and conditional analyses, HLA-B*08 proves to be the major associated allele (p = 2.87 × 10−113, OR = 6.41). In addition to the expected association with PTPN22 (rs2476601, OR =1.71, p = 8.2 ×10−10), an imputed coding variant (rs2233290) at position 151 (Pro→Ala) in the TNFAIP3-interacting protein 1, TNIP1, confers even stronger risk than PTPN22 (OR = 1.91, p = 3.2 × 10−10).
The association at TNIP1 in EOMG implies disease mechanisms involving ubiquitin-dependent dysregulation of NF-κB signaling. The localization of the major HLA signal to the HLA-B*08 allele suggests that CD8+ T-cells may play a key role in disease initiation or pathogenesis.
The major histocompatibility complex (MHC) region is strongly associated with multiple sclerosis (MS) susceptibility. HLA-DRB1*15:01 has the strongest effect, and several other alleles have been reported at different levels of validation. Using SNP data from genome-wide studies, we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen (HLA) genes in 5,091 cases and 9,595 controls. We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II, one HLA-A and two B alleles in class I, and one signal in a region spanning from MICB to LST1. This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC. Interestingly, this region contains the TNF gene, the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene. The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves. This study dissects the independent effects in the MHC, a critical region for MS susceptibility that harbors multiple risk alleles.
Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease with a heritable component. Although it has been known for a long time that the strongest MS risk factor maps to the major histocompatibility complex (MHC) on chromosome 6, there are still many unresolved questions as to the identity and the nature of the risk variants within the MHC. Because the MHC has a complex structure, systematic investigation across this region has been challenging. In this study, we used state-of-the-art imputation methods coupled to statistical regression to query variants in the human leukocyte antigen (HLA) class I and II genes for a role in MS risk. Starting from available SNP genotype data, we replicated the strongest risk factor, the HLA-DRB1*15:01 allele, and were able to identify 11 independent effects in total. Functional studies are now needed to understand their mechanism in MS etiology.
The major histocompatibility complex (MHC) region on the short arm of chromosome 6 harbors the largest number of replicated associations across the human genome for a wide range of diseases, but the functional basis for these associations is still poorly understood. One fundamental challenge in fine-mapping associations to functional alleles is the enormous sequence diversity and broad linkage disequilibrium of the MHC, both of which hamper the cost-effective interrogation in large patient samples and the identification of causal variants. In this review, we argue that there is now a valuable opportunity to leverage existing genome-wide association study (GWAS) datasets for in-depth investigation to identify independent effects in the MHC. Application of imputation to GWAS data facilitates comprehensive interrogation of the classical human leukocyte antigen (HLA) loci. These datasets are, in many cases, sufficiently large to give investigators the ability to disentangle effects at different loci. We also explain how querying variation at individual amino acid positions for association can be powerful and expand traditional analyses that focus only on the classical HLA types.
A small proportion of human immunodeficiency virus-1 (HIV-1) infected individuals, termed HIV-1 controllers, suppress viral replication to very low levels in the absence of therapy. Genetic investigations of this phenotype have strongly implicated variation in the class I major histocompatibility complex (MHC) region as key to HIV-1 control. We collected sequence-based classical class I HLA genotypes at 4-digit resolution in HIV-1-infected African American controllers and progressors (n = 1107), and tested them for association with host control using genome-wide single nucleotide polymorphism data to account for population structure. Several classical alleles at HLA-B were associated with host control, including B*57:03 [odds ratio (OR) = 5.1; P= 3.4 × 10–18] and B*81:01 (OR = 4.8; P= 1.3 × 10−9). Analysis of variable amino acid positions demonstrates that HLA-B position 97 is the most significant association with host control in African Americans (omnibus P = 1.2 × 10−21) and explains the signal of several HLA-B alleles, including B*57:03. Within HLA-B, we also identified independent effects at position 116 (omnibus P= 2.8 × 10−15) in the canonical F pocket, position 63 in the B pocket (P= 1.5 × 10−3) and the non-pocket position 245 (P= 8.8 × 10−10), which is thought to influence CD8-binding kinetics. Adjusting for these HLA-B effects, there is evidence for residual association in the MHC region. These results underscore the key role of HLA-B in affecting HIV-1 replication, likely through the molecular interaction between HLA-B and viral peptides presented by infected cells, and suggest that sites outside the peptide-binding pocket also influence HIV-1 control.
Exome sequencing is emerging as a popular approach to study the effect of rare coding variants on complex phenotypes. The promise of exome sequencing is grounded in theoretical population genetics and in empirical successes of candidate gene sequencing studies. Many projects aimed at common diseases are underway, and their results are eagerly anticipated. In this Perspective, using exome sequencing data from 438 individuals, we discuss several aspects of exome sequencing studies that we view as particularly important. We review processing and quality control of raw sequence data, evaluate the statistical properties of exome sequencing studies, discuss rare variant burden tests to detect association to phenotypes, and demonstrate the importance of accounting for population stratification in the analysis of rare variants. We conclude that enthusiasm for exome sequencing studies of complex traits should be combined with the caution that thousands of samples may be required to reach sufficient statistical power.
Multiple genome-wide association studies (GWAS) have been performed in HIV-1 infected individuals, identifying common genetic influences on viral control and disease course. Similarly, common genetic correlates of acquisition of HIV-1 after exposure have been interrogated using GWAS, although in generally small samples. Under the auspices of the International Collaboration for the Genomics of HIV, we have combined the genome-wide single nucleotide polymorphism (SNP) data collected by 25 cohorts, studies, or institutions on HIV-1 infected individuals and compared them to carefully matched population-level data sets (a list of all collaborators appears in Note S1 in Text S1). After imputation using the 1,000 Genomes Project reference panel, we tested approximately 8 million common DNA variants (SNPs and indels) for association with HIV-1 acquisition in 6,334 infected patients and 7,247 population samples of European ancestry. Initial association testing identified the SNP rs4418214, the C allele of which is known to tag the HLA-B*57:01 and B*27:05 alleles, as genome-wide significant (p = 3.6×10−11). However, restricting analysis to individuals with a known date of seroconversion suggested that this association was due to the frailty bias in studies of lethal diseases. Further analyses including testing recessive genetic models, testing for bulk effects of non-genome-wide significant variants, stratifying by sexual or parenteral transmission risk and testing previously reported associations showed no evidence for genetic influence on HIV-1 acquisition (with the exception of CCR5Δ32 homozygosity). Thus, these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size.
Comparing the frequency differences between common DNA variants in disease-affected cases and in unaffected controls has been successful in uncovering the genetic component of multiple diseases. This approach is most effective when large samples of cases and controls are available. Here we combine information from multiple studies of HIV infected patients, including more than 6,300 HIV+ individuals, with data from 7,200 general population samples of European ancestry to test nearly 8 million common DNA variants for an impact on HIV acquisition. With this large sample we did not observe any single common genetic variant that significantly associated with HIV acquisition. We further tested 22 variants previously identified by smaller studies as influencing HIV acquisition. With the exception of a deletion polymorphism in the CCR5 gene (CCR5Δ32) we found no convincing evidence to support these previous associations. Taken together these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size.
DNA sequence variation within human leukocyte antigen (HLA) genes mediate susceptibility to a wide range of human diseases. The complex genetic structure of the major histocompatibility complex (MHC) makes it difficult, however, to collect genotyping data in large cohorts. Long-range linkage disequilibrium between HLA loci and SNP markers across the major histocompatibility complex (MHC) region offers an alternative approach through imputation to interrogate HLA variation in existing GWAS data sets. Here we describe a computational strategy, SNP2HLA, to impute classical alleles and amino acid polymorphisms at class I (HLA-A, -B, -C) and class II (-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) loci. To characterize performance of SNP2HLA, we constructed two European ancestry reference panels, one based on data collected in HapMap-CEPH pedigrees (90 individuals) and another based on data collected by the Type 1 Diabetes Genetics Consortium (T1DGC, 5,225 individuals). We imputed HLA alleles in an independent data set from the British 1958 Birth Cohort (N = 918) with gold standard four-digit HLA types and SNPs genotyped using the Affymetrix GeneChip 500 K and Illumina Immunochip microarrays. We demonstrate that the sample size of the reference panel, rather than SNP density of the genotyping platform, is critical to achieve high imputation accuracy. Using the larger T1DGC reference panel, the average accuracy at four-digit resolution is 94.7% using the low-density Affymetrix GeneChip 500 K, and 96.7% using the high-density Illumina Immunochip. For amino acid polymorphisms within HLA genes, we achieve 98.6% and 99.3% accuracy using the Affymetrix GeneChip 500 K and Illumina Immunochip, respectively. Finally, we demonstrate how imputation and association testing at amino acid resolution can facilitate fine-mapping of primary MHC association signals, giving a specific example from type 1 diabetes.