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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to alleles in HLA-DRB1. However, debate persists about the identity of ...the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 individuals with seropositive rheumatoid arthritis (cases) and 14,974 unaffected controls, we imputed and tested classical alleles and amino acid polymorphisms in HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1, as well as 3,117 SNPs across the MHC. Conditional and haplotype analyses identified that three amino acid positions (11, 71 and 74) in HLA-DRβ1 and single-amino-acid polymorphisms in HLA-B (at position 9) and HLA-DPβ1 (at position 9), which are all located in peptide-binding grooves, almost completely explain the MHC association to rheumatoid arthritis risk. This study shows how imputation of functional variation from large reference panels can help fine map association signals in the MHC.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide ...polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies). Availability: SNAP server is available at http://www.broad.mit.edu/mpg/snap/. Contact: debakker@broad.mit.edu
Deep sequencing will soon generate comprehensive sequence information in large disease samples. Although the power to detect association with an individual rare variant is limited, pooling variants ...by gene or pathway into a composite test provides an alternative strategy for identifying susceptibility genes. We describe a statistical method for detecting association of multiple rare variants in protein-coding genes with a quantitative or dichotomous trait. The approach is based on the regression of phenotypic values on individuals' genotype scores subject to a variable allele-frequency threshold, incorporating computational predictions of the functional effects of missense variants. Statistical significance is assessed by permutation testing with variable thresholds. We used a rigorous population-genetics simulation framework to evaluate the power of the method, and we applied the method to empirical sequencing data from three disease studies.
With mutations continually occurring in each protein-coding gene (at a rate of ~1 × 10-5 per gene per generation for nonsynonymous variants)36-39 and fitness losses of less than 1% for most novel ...nonsynonymous mutations29-31,34, almost every gene is expected to harbor functionally important variants that can be tested through sequencing, even if these variants are rare. ...the strong interest in exome sequencing stems from three factors: the potential to identify many genes underlying complex traits, straightforward functional annotation of coding variation and a substantially lower cost (approximately five times lower) than that of whole-genome sequencing. ...as sample size increases, the number of observed variants grows much faster than is predicted by the neutral model with constant population size41,42 (Fig. 1).
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
A strong association between the HLA-B*1502 allele and SJS and TEN induced by carbamazepine has been shown. This study involving Europeans implicates a different HLA allele, HLA-A*3101, in conferring ...susceptibility to a broad range of carbamazepine-induced reactions.
Carbamazepine is one of the most commonly prescribed drugs for the treatment of epilepsy, as well as trigeminal neuralgia and bipolar disorder. A minority of treated persons have hypersensitivity reactions that vary in prevalence and severity,
1
with some forms associated with substantial morbidity and mortality. The mildest form, maculopapular exanthema, occurs in 5 to 10% of treated persons of European ancestry and resolves spontaneously after drug discontinuation. More severe reactions, such as the hypersensitivity syndrome, are associated with mortality of up to 10%
2
and include symptoms such as rash, fever, eosinophilia, hepatitis, and nephritis. The most severe reactions, such as . . .
Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets ...in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted ...complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.
We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.
We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.
We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Susceptibility to rheumatoid arthritis and systemic lupus erythematosus has been linked to a region on chromosome 2q. Fine-mapping of this locus, in patients with rheumatoid arthritis and in patients ...with lupus, traces the association to a single variant of the gene
STAT4,
which encodes a transcription factor activated by cytokines.
Fine mapping of a region on chromosome 2q, in patients with rheumatoid arthiritis and in patients with lupus, traces an association to a single variant of the gene
STAT4,
which encodes a transcription factor activated by cytokines.
Rheumatoid arthritis is the most common cause of adult inflammatory arthritis and is associated with considerable disability and early mortality.
1
Studies of twins clearly show a genetic contribution to disease susceptibility,
2
and the siblings of patients with seropositive, erosive rheumatoid arthritis have an estimated risk of developing the disease of between 5 and 10 times that of the general population.
3
The highly polymorphic
HLA
region is a major contributor to genetic risk of rheumatoid arthritis.
4
Several other genes associated with more modest risks have recently been identified, including the Arg620→Trp variant of the intracellular phosphatase gene
PTPN22
.
5
,
6
However, . . .
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