Xiaoming Jia

Integrating common and rare genetic variation in diverse human populations.

Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called ‘HapMap 3’, includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of ≤5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.

Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci

We report the results of a meta-analysis of genome-wide association scans for multiple sclerosis (MS) susceptibility that includes 2,624 subjects with MS and 7,220 control subjects. Replication in an independent set of 2,215 subjects with MS and 2,116 control subjects validates new MS susceptibility loci at TNFRSF1A (combined P = 1.59 × 10−11), IRF8 (P = 3.73 × 10−9) and CD6 (P = 3.79 × 10−9). TNFRSF1A harbors two independent susceptibility alleles: rs1800693 is a common variant with modest effect (odds ratio = 1.2), whereas rs4149584 is a nonsynonymous coding polymorphism of low frequency but with stronger effect (allele frequency = 0.02; odds ratio = 1.6). We also report that the susceptibility allele near IRF8, which encodes a transcription factor known to function in type I interferon signaling, is associated with higher mRNA expression of interferon-response pathway genes in subjects with MS.

HLA-A*3101 and Carbamazepine-Induced Hypersensitivity Reactions in Europeans

BACKGROUND Carbamazepine causes various forms of hypersensitivity reactions, ranging from maculopapular exanthema to severe blistering reactions. The HLA-B*1502 allele has been shown to be strongly correlated with carbamazepine-induced Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS-TEN) in the Han Chinese and other Asian populations but not in European populations. METHODS We performed a genomewide association study of samples obtained from 22 subjects with carbamazepine-induced hypersensitivity syndrome, 43 subjects with carbamazepine-induced maculopapular exanthema, and 3987 control subjects, all of European descent. We tested for an association between disease and HLA alleles through proxy single-nucleotide polymorphisms and imputation, confirming associations by high-resolution sequence-based HLA typing. We replicated the associations in samples from 145 subjects with carbamazepine-induced hypersensitivity reactions. RESULTS The HLA-A*3101 allele, which has a prevalence of 2 to 5% in Northern European populations, was significantly associated with the hypersensitivity syndrome (P=3.5×10(-8)). An independent genomewide association study of samples from subjects with maculopapular exanthema also showed an association with the HLA-A*3101 allele (P=1.1×10(-6)). Follow-up genotyping confirmed the variant as a risk factor for the hypersensitivity syndrome (odds ratio, 12.41; 95% confidence interval [CI], 1.27 to 121.03), maculopapular exanthema (odds ratio, 8.33; 95% CI, 3.59 to 19.36), and SJS-TEN (odds ratio, 25.93; 95% CI, 4.93 to 116.18). CONCLUSIONS The presence of the HLA-A*3101 allele was associated with carbamazepine-induced hypersensitivity reactions among subjects of Northern European ancestry. The presence of the allele increased the risk from 5.0% to 26.0%, whereas its absence reduced the risk from 5.0% to 3.8%. (Funded by the U.K. Department of Health and others.).

Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis

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.

Practical aspects of imputation-driven meta-analysis of genome-wide association studies

Motivated by the overwhelming success of genome-wide association studies, droves of researchers are working vigorously to exchange and to combine genetic data to expediently discover genetic risk factors for common human traits. The primary tools that fuel these new efforts are imputation, allowing researchers who have collected data on a diversity of genotype platforms to share data in a uniformly exchangeable format, and meta-analysis for pooling statistical support for a genotype-phenotype association. As many groups are forming collaborations to engage in these efforts, this review collects a series of guidelines, practical detail and learned experiences from a variety of individuals who have contributed to the subject.

Imputing amino Acid polymorphisms in human leukocyte antigens.

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.

Risk Factors for ARDS in Patients Receiving Mechanical Ventilation for > 48 h

BACKGROUND Low tidal volume (Vt) ventilation for ARDS is a well-accepted concept. However, controversy persists regarding the optimal ventilator settings for patients without ARDS receiving mechanical ventilation. This study tested the hypothesis that ventilator settings influence the development of new ARDS. METHODS Retrospective analysis of patients from the Multi Parameter Intelligent Monitoring of Intensive Care-II project database who received mechanical ventilation for > or = 48 h between 2001 and 2005. RESULTS A total of 2,583 patients required > 48 h of ventilation. Of 789 patients who did not have ARDS at hospital admission, ARDS developed in 152 patients (19%). Univariate analysis revealed high peak inspiratory pressure (odds ratio [OR], 1.53 per SD; 95% confidence interval [CI], 1.28 to 1.84), increasing positive end-expiratory pressure (OR, 1.35 per SD; 95% CI, 1.15 to 1.58), and Vt (OR, 1.36 per SD; 95% CI, 1.12 to 1.64) to be significant risk factors. Major nonventilator risk factors for ARDS included sepsis, low pH, elevated lactate, low albumin, transfusion of packed RBCs, transfusion of plasma, high net fluid balance, and low respiratory compliance. Multivariable logistic regression showed that peak pressure (OR, 1.31 per SD; 95% CI, 1.08 to 1.59), high net fluid balance (OR, 1.3 per SD; 95% CI, 1.09 to 1.56), transfusion of plasma (OR, 1.26 per SD; 95% CI, 1.07 to 1.49), sepsis (OR, 1.57; 95% CI, 1.00 to 2.45), and Vt (OR, 1.29 per SD; 95% CI, 1.02 to 1.52) were significantly associated with the development of ARDS. CONCLUSIONS The associations between the development of ARDS and clinical interventions, including high airway pressures, high Vt, positive fluid balance, and transfusion of blood products, suggests that ARDS may be a preventable complication in some cases.

Next-generation sequencing for HLA typing of class I loci

BackgroundComprehensive sequence characterization across the MHC is important for successful organ transplantation and genetic association studies. To this end, we have developed an automated sample preparation, molecular barcoding and multiplexing protocol for the amplification and sequence-determination of class I HLA loci. We have coupled this process to a novel HLA calling algorithm to determine the most likely pair of alleles at each locus.ResultsWe have benchmarked our protocol with 270 HapMap individuals from four worldwide populations with 96.4% accuracy at 4-digit resolution. A variation of this initial protocol, more suitable for large sample sizes, in which molecular barcodes are added during PCR rather than library construction, was tested on 95 HapMap individuals with 98.6% accuracy at 4-digit resolution.ConclusionsNext-generation sequencing on the 454 FLX Titanium platform is a reliable, efficient, and scalable technology for HLA typing.

HLA-B*13:01 and the dapsone hypersensitivity syndrome.

BACKGROUND Dapsone is used in the treatment of infections and inflammatory diseases. The dapsone hypersensitivity syndrome, which is associated with a reported mortality of 9.9%, develops in about 0.5 to 3.6% of persons treated with the drug. Currently, no tests are available to predict the risk of the dapsone hypersensitivity syndrome. METHODS We performed a genomewide association study involving 872 participants who had received dapsone as part of multidrug therapy for leprosy (39 participants with the dapsone hypersensitivity syndrome and 833 controls), using log-additive tests of single-nucleotide polymorphisms (SNPs) and imputed HLA molecules. For a replication analysis, we genotyped 24 SNPs in an additional 31 participants with the dapsone hypersensitivity syndrome and 1089 controls and performed next-generation sequencing for HLA-B and HLA-C typing at four-digit resolution in an independent series of 37 participants with the dapsone hypersensitivity syndrome and 201 controls. RESULTS Genomewide association analysis showed that SNP rs2844573, located between the HLA-B and MICA loci, was significantly associated with the dapsone hypersensitivity syndrome among patients with leprosy (odds ratio, 6.18; P=3.84×10(-13)). HLA-B*13:01 was confirmed to be a risk factor for the dapsone hypersensitivity syndrome (odds ratio, 20.53; P=6.84×10(-25)). The presence of HLA-B*13:01 had a sensitivity of 85.5% and a specificity of 85.7% as a predictor of the dapsone hypersensitivity syndrome, and its absence was associated with a reduction in risk by a factor of 7 (from 1.4% to 0.2%). HLA-B*13:01 is present in about 2 to 20% of Chinese persons, 1.5% of Japanese persons, 1 to 12% of Indians, and 2 to 4% of Southeast Asians but is largely absent in Europeans and Africans. CONCLUSIONS HLA-B*13:01 was associated with the development of the dapsone hypersensitivity syndrome among patients with leprosy. (Funded by the National Natural Science Foundation of China and others.).

Gut microbiota from multiple sclerosis patients enables spontaneous autoimmune encephalomyelitis in mice

Significance Studies using experimental models have indicated that multiple sclerosis (MS)-like disease can be triggered in the gut following interactions of brain autoimmune T lymphocytes with local microbiota. Here we studied the gut microbiota from monozygotic human twin pairs discordant for multiple sclerosis. When we transferred human-derived microbiota into transgenic mice expressing a myelin autoantigen-specific T cell receptor, we found that gut microbiota from multiple sclerosis-affected twins induced CNS-specific autoimmunity at a higher incidence than microbiota from healthy co-twins. Our results offer functional evidence that human microbiome components contribute to CNS-specific autoimmunity. There is emerging evidence that the commensal microbiota has a role in the pathogenesis of multiple sclerosis (MS), a putative autoimmune disease of the CNS. Here, we compared the gut microbial composition of 34 monozygotic twin pairs discordant for MS. While there were no major differences in the overall microbial profiles, we found a significant increase in some taxa such as Akkermansia in untreated MS twins. Furthermore, most notably, when transplanted to a transgenic mouse model of spontaneous brain autoimmunity, MS twin-derived microbiota induced a significantly higher incidence of autoimmunity than the healthy twin-derived microbiota. The microbial profiles of the colonized mice showed a high intraindividual and remarkable temporal stability with several differences, including Sutterella, an organism shown to induce a protective immunoregulatory profile in vitro. Immune cells from mouse recipients of MS-twin samples produced less IL-10 than immune cells from mice colonized with healthy-twin samples. IL-10 may have a regulatory role in spontaneous CNS autoimmunity, as neutralization of the cytokine in mice colonized with healthy-twin fecal samples increased disease incidence. These findings provide evidence that MS-derived microbiota contain factors that precipitate an MS-like autoimmune disease in a transgenic mouse model. They hence encourage the detailed search for protective and pathogenic microbial components in human MS.