H A Erlich

Children's Hospital Oakland Research Institute, Oakland, California, United States

Are you H A Erlich?

Claim your profile

Publications (467)3405.34 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: NK cells are innate immune cells known for their cytolytic activities toward tumors and infections. They are capable of expressing diverse killer Ig-like receptors (KIRs), and KIRs are implicated in susceptibility to Crohn's disease (CD), a chronic intestinal inflammatory disease. However, the cellular mechanism of this genetic contribution is unknown. In this study, we show that the "licensing" of NK cells, determined by the presence of KIR2DL3 and homozygous HLA-C1 in host genome, results in their cytokine reprogramming, which permits them to promote CD4(+) T cell activation and Th17 differentiation ex vivo. Microfluidic analysis of thousands of NK single cells and bulk secretions established that licensed NK cells are more polarized to proinflammatory cytokine production than unlicensed NK cells, including production of IFN-γ, TNF-α, CCL-5, and MIP-1β. Cytokines produced by licensed NK augmented CD4(+) T cell proliferation and IL-17A/IL-22 production. Ab blocking indicated a primary role for IFN-γ, TNF-α, and IL-6 in the augmented T cell-proliferative response. In conclusion, NK licensing mediated by KIR2DL2/3 and HLA-C1 elicits a novel NK cytokine program that activates and induces proinflammatory CD4(+) T cells, thereby providing a potential biologic mechanism for KIR-associated susceptibility to CD and other chronic inflammatory diseases.
    Journal of immunology (Baltimore, Md. : 1950). 06/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The high-resolution human leukocyte antigen (HLA) genotyping assay that we developed using 454 sequencing and Conexio software uses generic polymerase chain reaction (PCR) primers for DRB exon 2. Occasionally, we observed low abundance DRB amplicon sequences that resulted from in vitro PCR 'crossing over' between DRB1 and DRB3/4/5. These hybrid sequences, revealed by the clonal sequencing property of the 454 system, were generally observed at a read depth of 5%-10% of the true alleles. They usually contained at least one mismatch with the IMGT/HLA database, and consequently, were easily recognizable and did not cause a problem for HLA genotyping. Sometimes, however, these artifactual sequences matched a rare allele and the automatic genotype assignment was incorrect. These observations raised two issues: (1) could PCR conditions be modified to reduce such artifacts? and (2) could some of the rare alleles listed in the IMGT/HLA database be artifacts rather than true alleles? Because PCR crossing over occurs during late cycles of PCR, we compared DRB genotypes resulting from 28 and (our standard) 35 cycles of PCR. For all 21 cell line DNAs amplified for 35 cycles, crossover products were detected. In 33% of the cases, these hybrid sequences corresponded to named alleles. With amplification for only 28 cycles, these artifactual sequences were not detectable. To investigate whether some rare alleles in the IMGT/HLA database might be due to PCR artifacts, we analyzed four samples obtained from the investigators who submitted the sequences. In three cases, the sequences were generated from true alleles. In one case, our 454 sequencing revealed an error in the previously submitted sequence.
    Tissue Antigens 01/2014; 83(1):32-40. · 2.93 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Historically, association of disease with the major histocompatibility complex (HLA) genes has been tested with HLA alleles that encode antigen-binding affinity. The association with Parkinson disease (PD), however, was discovered with noncoding SNPs in a genome-wide association study (GWAS). We show here that several HLA-region SNPs that have since been associated with PD form two blocks tagged by rs3129882 (p = 9 × 10(-11)) and by rs9268515 and/or rs2395163 (p = 3 × 10(-11)). We investigated whether these SNP-associations were driven by HLA-alleles at adjacent loci. We imputed class I and class II HLA-alleles for 2000 PD cases and 1986 controls from the NeuroGenetics Research Consortium GWAS and sequenced a subset of 194 cases and 204 controls. We were therefore able to assess accuracy of two imputation algorithms against next-generation-sequencing while taking advantage of the larger imputed data sets for disease study. Additionally, we imputed HLA alleles for 843 cases and 856 controls from another GWAS for replication. PD risk was positively associated with the B(∗)07:02_C(∗)07:02_DRB5(∗)01_DRB1(∗)15:01_DQA1(∗)01:02_DQB1(∗)06:02 haplotype and negatively associated with the C(∗)03:04, DRB1(∗)04:04 and DQA1(∗)03:01 alleles. The risk haplotype and DQA1(∗)03:01 lost significance when conditioned on the SNPs, but C(∗)03:04 (OR = 0.72, p = 8 × 10(-6)) and DRB1(∗)04:04 (OR = 0.65, p = 4 × 10(-5)) remained significant. Similarly, rs3129882 and the closely linked rs9268515 and rs2395163 remained significant irrespective of HLA alleles. rs3129882 and rs2395163 are expression quantitative trait loci (eQTLs) for HLA-DR and HLA-DQ (9 × 10(-5) ≥ PeQTL ≥ 2 × 10(-79)), suggesting that HLA gene expression might influence PD. Our data suggest that PD is associated with both structural and regulatory elements in HLA. Furthermore, our study demonstrates that noncoding SNPs in the HLA region can be associated with disease irrespective of HLA alleles, and that observed associations with HLA alleles can sometimes be secondary to a noncoding variant.
    The American Journal of Human Genetics 10/2013; · 11.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Aim Next generation sequencing of HLA exonic amplicons with the 454 Life Sciences GS FLX System and Conexio AssignTM-ATF software provides high resolution, high throughput HLA genotyping for 8 class I and class II loci (Bentley et al., 20091, Holcomb et al., 20112). HLA typing of potential donors for Unrelated Bone Marrow Donor Registries requires using a subset of these loci at high sample throughput and low cost per sample. To maximize the throughput, we have incorporated the Fluidigm® Access ArrayTM system to simplify amplicon library preparation and allow the efficient introduction of MIDs (Multiplex Identifiers) or barcodes. Methods For this application, a streamlined amplicon sequencing workflow employing many different Multiplex Identifiers (MIDs) enables multiplexed sequencing of a large number of samples, allowing researchers to meet these cost targets. The Fluidigm® 48 x 48 Access ArrayTM system and the “4 primer” approach provides an efficient way to achieve parallel semi-automated genomic PCRs with simultaneous incorporation of 48 different MIDs corresponding to 48 genomic DNA samples, and up to 48 different primer pairs, making possible 2,304 reactions in one amplification run. Minimal volumes (calculated to be about 45% less cost per run) of reagents are used in this micro- fluidics-based system. During genomic PCR, the outer set of primers containing the MIDs and the 454 adaptor sequences are incorporated into the amplicons generated by the inner set of HLA specific primers containing a complementary “universal” Fluidigm tag. Pools of the resulting amplicons are used for emulsion PCR and clonal sequencing on the 454 GS FLX System, as well as genotyping with Conexio AssignTM-ATF software. Results Using the Access Array system, we have successfully (100% concordance with known genotypes) genotyped 192 samples with 8 primer pairs (covering exons 2 and 3 in HLA-A, B, C and Exon 2 in DRB1, DRB3/4/5 and DQB1) using 96 MIDs per region in a single GS FLX run on a 2 region PicoTiterPlate™ (PTP) and 96 samples using 48 MIDs per region in a 4 region PTP. An average of 166 and 137 sequence reads per amplicon were recovered respectively. We have also genotyped, in a single run, 96 samples at high resolution (14 primer pairs covering exons 2, 3, and 4 of the class I loci and exons 2 of DRB1,3/4/5, DQA1, DQB1, DPB1, and exon 3 of DQB1) recovering an average of 175 sequence reads per amplicon at 100% concordance. The system reduces the overall time for the entire process (192 samples) from 8 days for manual processing to about 4 days with 1 FTE.
    Human Immunology. 05/2013; 74(5):498.
  • [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE This study assessed the ability to distinguish between type 1 diabetes-affected individuals and their unaffected relatives using HLA and single nucleotide polymorphism (SNP) genotypes.RESEARCH DESIGN AND METHODS Eight models, ranging from only the high-risk DR3/DR4 genotype to all significantly associated HLA genotypes and two SNPs mapping to the cytotoxic T-cell-associated antigen-4 gene (CTLA4) and insulin (INS) genes, were fitted to high-resolution class I and class II HLA genotyping data for patients from the Type 1 Diabetes Genetics Consortium collection. Pairs of affected individuals and their unaffected siblings were divided into a "discovery" (n = 1,015 pairs) and a "validation" set (n = 318 pairs). The discriminating performance of various combinations of genetic information was estimated using receiver operating characteristic (ROC) curve analysis.RESULTSThe use of only the presence or absence of the high-risk DR3/DR4 genotype achieved very modest discriminating ability, yielding an area under the curve (AUC) of 0.62 in the discovery set and 0.59 in the validation set. The full model, which included HLA information from the class II loci DPB1, DRB1, DQB1, selected alleles from HLA class I loci A and B, and SNPs from the CTLA4 and INS genes, increased the AUC to 0.74 in the discovery set and to 0.71 in the validation set. A cost-effective alternative is proposed, using genotype information equivalent to typing four SNPs (DR3, DR4-DQB1*03:02, CTLA-4, and INS), which achieved an AUC of 0.72 in the discovery set and 0.69 in the validation set.CONCLUSIONS Genotyping data sufficient to tag DR3, DR4-DQB1*03:02, CTLA4, and INS was shown to distinguish between subjects with type 1 diabetes and their unaffected siblings adequately to achieve clinically utility to identify children in multiplex families to be considered for early intervention.
    Diabetes care 04/2013; · 7.74 Impact Factor
  • Henry A Erlich, Ana M Valdes, Janelle A Noble
    Diabetes 04/2013; 62(4):1020-1. · 7.90 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The primary associations of the HLA class II genes, HLA-DRB1 and HLA-DQB1, and the class I genes, HLA-A and HLA-B, with type 1 diabetes (T1D) are well established. However, the role of polymorphism at the HLA-DRB3, HLA-DRB4, and HLA-DRB5 loci remains unclear. In two separate studies, one of 500 cases and 500 controls and one of 366 DRB1*03:01-positive samples from selected multiplex T1D families, we used Roche 454 sequencing with Conexio Genomics ASSIGN™ATF HLA genotyping software analysis to analyze sequence variation at these three HLA-DRB loci. Association analyses were performed on the two HLA-DRB locus (DRB1-DRB3, -DRB4, or -DRB5) haplotypes. Three common HLA-DRB3 alleles (*01:01, *02:02, *03:01) were observed. DRB1*03:01 haplotypes carrying DRB3*02:02 conferred a higher (p=0.033) T1D risk than did DRB1*03:01 haplotypes carrying DRB3*01:01, primarily in DRB1*03:01/*03:01 homozygotes with two DRB3*01:01 alleles (OR = 3.4 95% CI= 1.46-8.09), compared with those carrying one or two DRB3*02:02 alleles (OR = 25.5; 95% CI - 3.43-189.2). For DRB1*03:01/*04:01 heterozygotes, however, the HLA-DRB3 allele did not significantly modify the T1D risk of the DRB1*03:01 haplotype (OR = 7.7 for *02:02; OR = 6.8 for *01:01). These observations were confirmed by sequence analysis of HLA-DRB3 exon 2 in a targeted replication study of 280 informative T1D family members and 86 Affected Family Based Control (AFBAC) haplotypes. The frequency of DRB3*02:02 was 42.9% in the DRB1*03:01/*03:01 patients and 27.6% in the DRB1*03:01/*04 (p = 0.005), compared to 22.6% in AFBAC DRB1*03:01 chromosomes (p = 0.001). Analysis of T1D associated alleles at other HLA loci (HLA-A, HLA-B and HLA-DPB1) on DRB1*03:01 haplotypes suggests that DRB3*02:02 on the DRB1*03:01 haplotype can contribute to T1D risk.
    Diabetes 03/2013; · 7.90 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The human leukocyte antigen (HLA) class I and class II loci are the most polymorphic genes in the human genome; distinguishing the thousands of HLA alleles is challenging. Next generation sequencing of exonic amplicons with the 454 genome sequence (GS) FLX System and Conexio Assign ATF 454 software provides high resolution, high throughput HLA genotyping for eight class I and class II loci. HLA typing of potential donors for unrelated bone marrow donor registries typically uses a subset of these loci at high sample throughput and low cost per sample. The Fluidigm Access Array System enables the incorporation of 48 different multiplex identifiers (MIDs) corresponding to 48 genomic DNA samples with up to 48 different primer pairs in a microfluidic device generating 2304 parallel polymerase chain reactions (PCRs). Minimal volumes of reagents are used. During genomic PCR, in this 4-primer system, the outer set of primers containing the MID and the 454 adaptor sequences are incorporated into an amplicon generated by the inner HLA target-specific primers each containing a common sequence tag at the 5' end of the forward and reverse primers. Pools of the resulting amplicons are used for emulsion PCR and clonal sequencing on the 454 Life Sciences GS FLX System, followed by genotyping with Conexio software. We have genotyped 192 samples with 100% concordance to known genotypes using 8 primer pairs (covering exons 2 and 3 of HLA-A, B and C, and exon 2 of DRB1, 3/4/5 and DQB1) and 96 MIDs in a single GS FLX run. An average of 166 reads per amplicon was obtained. We have also genotyped 96 samples at high resolution (14 primer pairs covering exons 2, 3, and 4 of the class I loci and exons 2 of DRB1, 3/4/5, DQA1, DQB1, DPB1, and exon 3 of DQB1), recovering an average of 173 sequence reads per amplicon.
    Tissue Antigens 03/2013; 81(3):141-9. · 2.93 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Human leucocyte antigen (HLA) genes play an important role in the success of organ transplantation and are associated with autoimmune and infectious diseases. Current DNA-based genotyping methods, including Sanger sequence-based typing (SSBT), have identified a high degree of polymorphism. This level of polymorphism makes high-resolution HLA genotyping challenging, resulting in ambiguous typing results due to an inability to resolve phase and/or defining polymorphisms lying outside the region amplified. Next-generation sequencing (NGS) may resolve the issue through the combination of clonal amplification, which provides phase information, and the ability to sequence larger regions of genes, including introns, without the additional effort or cost associated with current methods. The NGS HLA sequencing project of the 16IHIW aimed to discuss the different approaches to (i) template preparation including short- and long-range PCR amplicons, exome capture and whole genome; (ii) sequencing platforms, including GS 454 FLX, Ion Torrent PGM, Illumina MiSeq/HiSeq and Pacific Biosciences SMRT; (iii) data analysis, specifically allele-calling software. The pilot studies presented at the workshop demonstrated that although individual sequencers have very different performance characteristics, all produced sequence data suitable for the resolution of HLA genotyping ambiguities. The developments presented at this workshop clearly highlight the potential benefits of NGS in the HLA laboratory.
    International Journal of Immunogenetics 02/2013; 40(1):72-6. · 1.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Balancing selection has maintained human leukocyte antigen (HLA) allele diversity, but it is unclear whether this selection is symmetric (all heterozygotes are comparable and all homozygotes are comparable in terms of fitness) or asymmetric (distinct heterozygote genotypes display greater fitness than others). We tested the hypothesis that HLA is under asymmetric balancing selection in populations by estimating allelic branch lengths from genetic sequence data encoding peptide-binding domains. Significant deviations indicated changes in the ratio of terminal to internal branch lengths. Such deviations could arise even if no individual alleles present a strikingly altered branch length (e.g. if there is an overall distortion, with all or many terminal branches being longer than expected). DQ and DP loci were also analyzed as haplotypes. Using allele frequencies for 419 distinct populations in 10 geographical regions, we examined population differentiation in alleles within and between regions, and the relationship between allelic branch length and frequency. The strongest evidence for asymmetrical balancing selection was observed for HLA-DRB1, HLA-B and HLA-DPA1, with significant deviation (P ≤ 1.1 × 10(-4)) in about half of the populations. There were significant results at all loci except HLA-DQB1/DQA1. We observed moderate genetic variation within and between geographic regions, similar to the rest of the genome. Branch length was not correlated with allele frequency. In conclusion, sequence data suggest that balancing selection in HLA is asymmetric (some heterozygotes enjoy greater fitness than others). Because HLA polymorphism is crucial for pathogen resistance, this may manifest as a frequency-dependent selection with fluctuation in the fitness of specific heterozygotes over time.
    Human Molecular Genetics 10/2012; · 7.69 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The present study investigated the association between variants in the vitamin D receptor gene (VDR) and protein tyrosine phosphatase, non-receptor type 2 gene (PTPN2), as well as an interaction between VDR and PTPN2 and the risk of islet autoimmunity (IA) and progression to type 1 diabetes (T1D). The Diabetes Autoimmunity Study in the Young (DAISY) has followed children at increased risk of T1D since 1993. Of the 1692 DAISY children genotyped for VDR rs1544410, VDR rs2228570, VDR rs11568820, PTPN2 rs1893217, and PTPN2 rs478582, 111 developed IA, defined as positivity for GAD, insulin or IA-2 autoantibodies on 2 or more consecutive visits, and 38 IA positive children progressed to T1D. Proportional hazards regression analyses were conducted. There was no association between IA development and any of the gene variants, nor was there evidence of a VDR*PTPN2 interaction. Progression to T1D in IA positive children was associated with the VDR rs2228570 GG genotype (HR: 0.49, 95% CI: 0.26-0.92) and there was an interaction between VDR rs1544410 and PTPN2 rs1893217 (p(interaction)=0.02). In children with the PTPN2 rs1893217 AA genotype, the VDR rs1544410 AA/AG genotype was associated with a decreased risk of T1D (HR: 0.24, 95% CI: 0.11-0.53, p=0.0004), while in children with the PTPN2 rs1893217 GG/GA genotype, the VDR rs1544410 AA/AG genotype was not associated with T1D (HR: 1.32, 95% CI: 0.43-4.06, p=0.62). These findings should be replicated in larger cohorts for confirmation. The interaction between VDR and PTPN2 polymorphisms in the risk of progression to T1D offers insight concerning the role of vitamin D in the etiology of T1D.
    The Journal of steroid biochemistry and molecular biology 09/2012; 133C:51-57. · 3.98 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The human leukocyte antigen (HLA) genes are candidate genetic susceptibility loci for childhood acute lymphoblastic leukemia (ALL). We examined the effect of HLA-DP genetic variation on risk and evaluated its potential interaction with 4 proxies for early immune modulation, including measures of infectious exposures in infancy (presence of older siblings, daycare attendance, ear infections) and breastfeeding. A total of 585 ALL cases and 848 controls were genotyped at the HLA-DPA1 and DPB1 loci. Because of potential heterogeneity in effect by race/ethnicity, we included only non-Hispanic white (47%) and Hispanic (53%) children and considered these 2 groups separately in the analysis. Logistic regression analyses showed an increased risk of ALL associated with HLA-DPB1*01:01 (odds ratio [OR] = 1.43, 95% CI, 1.01-2.04) with no heterogeneity by Hispanic ethnicity (P = .969). Analyses of DPB1 supertypes showed a marked childhood ALL association with DP1, particularly for high-hyperdiploid ALL (OR = 1.83; 95% CI, 1.20-2.78). Evidence of interaction was found between DP1 and older sibling (P = .036), and between DP1 and breastfeeding (P = .094), with both showing statistically significant DP1 associations within the lower exposure categories only. These findings support an immune mechanism in the etiology of childhood ALL involving the HLA-DPB1 gene in the context of an insufficiently modulated immune system.
    Blood 08/2012; 120(15):3039-47. · 9.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The common genetic loci that independently influence the risk of type 1 diabetes have largely been determined. Their interactions with age-at-diagnosis of type 1 diabetes, sex, or the major susceptibility locus, HLA class II, remain mostly unexplored. A large collection of more than 14,866 type 1 diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression models. The alleles that confer susceptibility to type 1 diabetes at interleukin-2 (IL-2), IL2/4q27 (rs2069763) and renalase, FAD-dependent amine oxidase (RNLS)/10q23.31 (rs10509540), were associated with a lower age-at-diagnosis (P = 4.6 × 10(-6) and 2.5 × 10(-5), respectively). For both loci, individuals carrying the susceptible homozygous genotype were, on average, 7.2 months younger at diagnosis than those carrying the protective homozygous genotypes. In addition to protein tyrosine phosphatase nonreceptor type 22 (PTPN22), evidence of statistical interaction between HLA class II genotypes and rs3087243 at cytotoxic T-lymphocyte antigen 4 (CTLA4)/2q33.2 was obtained (P = 7.90 × 10(-5)). No evidence of differential risk by sex was obtained at any loci (P ≥ 0.01). Statistical interaction effects can be detected in type 1 diabetes although they provide a relatively small contribution to our understanding of the familial clustering of the disease.
    Diabetes 08/2012; 61(11):3012-7. · 7.90 Impact Factor
  • H Erlich
    [Show abstract] [Hide abstract]
    ABSTRACT: The human leukocyte antigen (HLA) class I and class II loci are the most polymorphic genes in the human genome, with a highly clustered and patchwork pattern of sequence motifs. In the three decades since the first HLA gene was isolated by molecular cloning (a cDNA clone of HLA-B7), thousands of alleles have been identified and the names and sequences of all known alleles have been curated in the IMGT/HLA database. This extensive allelic diversity made and continues to make high-resolution HLA DNA typing very challenging. The first attempt at HLA DNA typing involved restriction fragment length polymorphism (RFLP) analysis, but this approach had many limitations. The development of PCR in 1985 allowed for the amplification of the polymorphic exons of the HLA class I and class II genes and the analysis of the polymorphic sequence motifs with sequence-specific oligonucleotide (SSO) hybridization probes. The immobilization of these probes on membranes and later on beads, along with primer sets for sequence-specific priming (SSP), gave rise to the current set of HLA typing reagents. Sanger sequencing has provided high-resolution typing but, in many cases, genotyping 'ambiguity' remains an issue. In the past few years, the introduction of next-generation sequencing, with the critical properties of massively parallel and clonal sequencing, has significantly reduced HLA genotyping ambiguity. Here, our lab's efforts to develop high-resolution and high-throughput HLA DNA typing using the 454 Sequencing System are reviewed, and the potential future developments and applications of HLA DNA typing are discussed.
    Tissue Antigens 07/2012; 80(1):1-11. · 2.93 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The study aimed to assess, in multiple populations, the role of HLA alleles on early and late age at onset of type 1 diabetes. Stepwise linear regression models were used to determine which HLA class I and class II risk alleles to include. High-resolution genotyping data for patients from the Type 1 Diabetes Genetics Consortium (T1DGC) collection (n = 2,278) and four independent cohorts from Denmark, Sardinia and the USA (Human Biological Data Interchange [HBDI] and Joslin Diabetes Center) (n = 1,324) (total n = 3,602) were used to assess the role of HLA variation on age of onset and predict early onset (age ≤ 5 years) and late onset (age ≥ 15 years) of type 1 diabetes. In addition to carriage of HLA class I alleles A*24:02, B*39:06, B*44:03 and B*18:01, HLA class II DRB1-DQB1 loci significantly contributed to age at onset, explaining 3.4% of its variance in the combined data. HLA genotypes, together with sex, were able to predict late onset in all cohorts studied, with AUC values ranging from 0.58 to 0.63. Similar AUC values (0.59-0.70) were obtained for early onset for most cohorts, except in the Sardinian study, in which none of the models tested had significant predictive power. HLA associations with age of onset are consistent across most white populations and HLA information can predict some of the risk of early and late onset of type 1 diabetes. Considerable heterogeneity was observed between Sardinian and other populations, particularly with regard to early age of onset.
    Diabetologia 06/2012; 55(9):2394-401. · 6.49 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from 2,319 families. Here, we, the Type 1 Diabetes Genetics Consortium (T1DGC), aimed to exclude the possibility that any of the 18 loci were false-positives due to population stratification by significantly increasing the statistical power of our family study. We genotyped the most disease-predicting single-nucleotide polymorphisms at the 18 susceptibility loci in 3,108 families and used existing genotype data for 2,319 families from the original study, providing 7,013 parent-child trios for analysis. We tested for association using the transmission disequilibrium test. Seventeen of the 18 susceptibility loci reached nominal levels of significance (p < 0.05) in the expanded family collection, with 14q24.1 just falling short (p = 0.055). When we allowed for multiple testing, ten of the 17 nominally significant loci reached the required level of significance (p < 2.8 × 10(-3)). All susceptibility loci had consistent direction of effects with the original study. The results for the novel GWA study-identified loci are genuine and not due to population stratification. The next step, namely correlation of the most disease-associated genotypes with phenotypes, such as RNA and protein expression analyses for the candidate genes within or near each of the susceptibility regions, can now proceed.
    Diabetologia 04/2012; 55(4):996-1000. · 6.49 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Follicular lymphoma (FL) is an indolent, sometimes, fatal disease characterized by recurrence at progressively shorter intervals and is frequently refractive to therapy. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) in the human leukocyte antigen (HLA) region on chromosome 6p21.32-33 that are statistically significantly associated with FL risk. Low to medium resolution typing of single or multiple HLA genes has provided an incomplete picture of the total genetic risk imparted by this highly variable region. To gain further insight into the role of HLA alleles in lymphomagenesis and to investigate the independence of validated SNPs and HLA alleles with FL risk, high-resolution HLA typing was conducted using next-generation sequencing in 222 non-Hispanic White FL cases and 220 matched controls from a larger San Francisco Bay Area population-based case-control study of lymphoma. A novel protective association was found between the DPB1*03:01 allele and FL risk [odds ratio (OR) = 0.39, 95% confidence interval (CI) = 0.21-0.68]. Extended haplotypes DRB1*01:01-DQA1*01:01-DQB1*05:01 (OR = 2.01, 95% CI = 1.22-3.38) and DRB1*15-DQA1*01-DQB1*06 (OR = 0.55, 95% CI = 0.36-0.82) also influenced FL risk. Moreover, DRB1*15-DQA1*01-DQB1*06 was highly correlated with an established FL risk locus, rs2647012. These results provide further insight into the critical roles of HLA alleles and SNPs in FL pathogenesis that involve multi-locus effects across the HLA region.
    Tissue Antigens 04/2012; 79(4):279-86. · 2.93 Impact Factor
  • Source
    Journal of Investigative Dermatology 03/2012; 132(6):1730-3. · 6.19 Impact Factor
  • Source
    Janelle A Noble, Henry A Erlich
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic susceptibility to type 1 diabetes (T1D) has been a subject of intensive study for nearly four decades. This article will present the history of these studies, beginning with observations of the Human Leukocyte Antigen (HLA) association in the 1970s, through the advent of DNA-based genotyping methodologies, through recent large, international collaborations and genome-wide association studies. More than 40 genetic loci have been associated with T1D in multiple studies; however, the HLA region, with its multiple genes and extreme polymorphism at those loci, remains by far the greatest contributor to the genetic susceptibility to T1D. Even after decades of study, the complete story has yet to unfold, and exact mechanisms by which HLA and other associated loci confer T1D susceptibility remain elusive.
    Cold Spring Harbor perspectives in medicine. 01/2012; 2(1):a007732.
  • Source
    Nature Genetics 12/2011; 44(1):3-5. · 35.21 Impact Factor

Publication Stats

40k Citations
3,405.34 Total Impact Points

Institutions

  • 1996–2014
    • Children's Hospital Oakland Research Institute
      Oakland, California, United States
  • 2013
    • University of Nottingham
      • Division of Academic Rheumatology
      Nottingham, ENG, United Kingdom
  • 1999–2013
    • Roche
      • Department of Human Genetics
      Bâle, Basel-City, Switzerland
    • University of Miami Miller School of Medicine
      • Diabetes Research Institute (DRI)
      Miami, FL, United States
  • 2012
    • King's College London
      • Department of Twin Research and Genetic Epidemiology
      London, ENG, United Kingdom
  • 2009–2012
    • University of Cambridge
      • Department of Medical Genetics
      Cambridge, ENG, United Kingdom
  • 2011
    • University of Western Australia
      • Centre for Diabetes Research
      Perth, Western Australia, Australia
  • 1990–2011
    • Uppsala University
      • • Department of Immunology, Genetics and Pathology
      • • The Rudbeck Laboratory
      Uppsala, Uppsala, Sweden
  • 2010
    • Norwegian Institute of Public Health
      • Division of Epidemiology
      Oslo, Oslo, Norway
    • Columbia University
      • Department of Surgery
      New York City, NY, United States
  • 2008–2010
    • University of Virginia
      • • Center for Public Health Genomics (CPHG)
      • • Department of Biochemistry, Molecular Biology and Genetics
      Charlottesville, Virginia, United States
  • 2001–2010
    • Anthony Nolan Research Institute
      Londinium, England, United Kingdom
    • University of Kansas
      • Department of Anthropology
      Lawrence, KS, United States
  • 1994–2009
    • University of Colorado
      • • Department of Pediatrics
      • • Barbara Davis Center for Childhood Diabetes
      • • Department of Immunology
      Denver, CO, United States
  • 1992–2009
    • University of Geneva
      Genève, Geneva, Switzerland
    • RWTH Aachen University
      Aachen, North Rhine-Westphalia, Germany
    • Kyushu University
      • Faculty of Medical Sciences
      Fukuoka-shi, Fukuoka-ken, Japan
    • Harvard Medical School
      Boston, Massachusetts, United States
  • 2007
    • University of Vermont
      • Department of Mathematics and Statistics
      Burlington, VT, United States
  • 1992–2007
    • Brigham and Women's Hospital
      • Department of Medicine
      Boston, MA, United States
  • 2005
    • Benaroya Research Institute
      Seattle, Washington, United States
  • 2003
    • National Cancer Institute (USA)
      • Division of Cancer Epidemiology and Genetics
      Bethesda, MD, United States
  • 1998–2002
    • University of California, Berkeley
      • • School of Public Health
      • • Department of Integrative Biology
      • • Department of Molecular and Cell Biology
      Berkeley, CA, United States
  • 2000
    • Swiss Tropical and Public Health Institute
      Bâle, Basel-City, Switzerland
    • Seoul National University
      • Department of Pediatrics
      Seoul, Seoul, South Korea
    • University of Missouri - St. Louis
      Saint Louis, Michigan, United States
  • 1997–1999
    • Oxford University Hospitals NHS Trust
      Oxford, England, United Kingdom
  • 1988–1997
    • University of Southern California
      • Department of Biological Sciences
      Los Angeles, California, United States
    • University of Washington Seattle
      • Department of Pediatrics
      Seattle, WA, United States
  • 1990–1995
    • Cancer Research UK
      Londinium, England, United Kingdom
  • 1992–1994
    • Bernhard Nocht Institute for Tropical Medicine
      • Department of Molecular Medicine
      Hamburg, Hamburg, Germany
  • 1993
    • The Scripps Research Institute
      La Jolla, California, United States
  • 1980–1993
    • Stanford University
      • • Department of Neurology and Neurological Sciences
      • • Department of Medicine
      • • Department of Neurobiology
      Palo Alto, CA, United States
  • 1991
    • University of California, San Francisco
      • Department of Laboratory Medicine
      San Francisco, CA, United States
    • Houston Methodist Hospital
      Houston, Texas, United States
    • University of California, Davis
      • Department of Internal Medicine
      Davis, CA, United States
  • 1989
    • University of California, San Diego
      • Department of Medicine
      San Diego, CA, United States
  • 1986
    • Memorial Sloan-Kettering Cancer Center
      New York City, New York, United States
  • 1983
    • Stanford Medicine
      Stanford, California, United States