Population structure, differential bias and genomic control in a large-scale, case-control association study.

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.

Blockade of orexin receptors with Almorexant reduces cardiorespiratory responses evoked from the hypothalamus but not baro- or chemoreceptor reflex responses.

Orexin neurons form a restricted group in the dorsal hypothalamus. The group is centered on the perifornical area within the classic hypothalamic defense area, an area which when activated produces marked cardiovascular and respiratory effects. Central administration of orexin can produce cardiorespiratory effects, but the extent to which orexin contributes to such responses evoked from the perifornical hypothalamus is not clear. To determine this, we used the dual orexin receptor antagonist Almorexant to challenge the cardiorespiratory effects evoked by disinhibition of the perifornical hypothalamus. Bicuculline (10 and 20 pmol) was microinjected in the perifornical area before and after administration of Almorexant (15 mg/kg iv) or vehicle in urethane-anesthetized rats. Almorexant significantly reduced the pressor, tachycardic, renal sympathoexcitatory, and tachypneic responses to bicuculline (10 pmol, by 55%, 53%, 28%, 77%; 20 pmol, by 54%, 27%, 51%, 72%, respectively). Reductions of similar magnitude were observed with bicuculline microinjections centered on more caudal sites just peripheral to the orexin neuron group, which would likely have activated fewer orexin neurons. In contrast, Almorexant had no effect on the cardiorespiratory response of the chemoreflex (sodium cyanide injection) or the sympathetic component of the baroreflex. Thus orexin makes a major contribution to the cardiorespiratory response evoked from the perifornical area even though orexin neurons represent only a fraction of the output of this area. Orexin neurons may also mediate cardiorespiratory responses from non-orexin neurons in the caudal hypothalamus. However, under resting conditions, blockade of orexin receptors does not affect the chemo- and baroreflexes.

Optimal design of genetic studies of gene expression with two-color microarrays in outbred crosses.

Combining global gene-expression profiling and genetic analysis of natural allelic variation (genetical genomics) has great potential in dissecting the genetic pathways underlying complex phenotypes. Efficient use of microarrays is paramount in experimental design as the cost of conducting this type of study is high. For those organisms where recombinant inbred lines are available for mapping, the "distant pair design" maximizes the number of informative contrasts over all marker loci. Here, we describe an extension of this design, named the "optimal pair design," for use with F2 crosses between outbred lines. The performance of this design is investigated by simulation and compared to several other two-color microarray designs. We show that, for a given number of microarrays, the optimal pair design outperforms all other designs considered for detection of expression quantitative trait loci (eQTL) with additive effects by linkage analysis. We also discuss the suitability of this design for outbred crosses in organisms with large genomes and for detection of dominance.

Interaction analysis of the CBLB and CTLA4 genes in type 1 diabetes.

Gene-gene interaction analyses have been suggested as a potential strategy to help identify common disease susceptibility genes. Recently, evidence of a statistical interaction between polymorphisms in two negative immunoregulatory genes, CBLB and CTLA4, has been reported in type 1 diabetes (T1D). This study, in 480 Danish families, reported an association between T1D and a synonymous coding SNP in exon 12 of the CBLB gene (rs3772534 G>A; minor allele frequency, MAF=0.24; derived relative risk, RR for G allele=1.78; P=0.046). Furthermore, evidence of a statistical interaction with the known T1D susceptibility-associated CTLA4 polymorphism rs3087243 (laboratory name CT60, G>A) was reported (P<0.0001), such that the CBLB SNP rs3772534 G allele was overtransmitted to offspring with the CTLA4 rs3087243 G/G genotype. We have, therefore, attempted to obtain additional support for this finding in both large family and case-control collections. In a primary analysis, no evidence for an association of the CBLB SNP rs3772534 with disease was found in either sample set (2162 parent-child trios, P=0.33; 3453 cases and 3655 controls, P=0.69). In the case-only statistical interaction analysis between rs3772534 and rs3087243, there was also no support for an effect (1994 T1D affected offspring, and 3215 cases, P=0.92). These data highlight the need for large, well-characterized populations, offering the possibility of obtaining additional support for initial observations owing to the low prior probability of identifying reproducible evidence of gene-gene interactions in the analysis of common disease-associated variants in human populations.

T1DBase, a community web-based resource for type 1 diabetes research.

T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.

Analysis of polymorphisms in 16 genes in type 1 diabetes that have been associated with other immune-mediated diseases.

BACKGROUND: The identification of the HLA class II, insulin (INS), CTLA-4 and PTPN22 genes as determinants of type 1 diabetes (T1D) susceptibility indicates that fine tuning of the immune system is centrally involved in disease development. Some genes have been shown to affect several immune-mediated diseases. Therefore, we tested the hypothesis that alleles of susceptibility genes previously associated with other immune-mediated diseases might perturb immune homeostasis, and hence also associate with predisposition to T1D. METHODS: We resequenced and genotyped tag single nucleotide polymorphisms (SNPs) from two genes, CRP and FCER1B, and genotyped 27 disease-associated polymorphisms from thirteen gene regions, namely FCRL3, CFH, SLC9A3R1, PADI4, RUNX1, SPINK5, IL1RN, IL1RA, CARD15, IBD5-locus (including SLC22A4), LAG3, ADAM33 and NFKB1. These genes have been associated previously with susceptibility to a range of immune-mediated diseases including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Graves' disease (GD), psoriasis, psoriatic arthritis (PA), atopy, asthma, Crohn disease and multiple sclerosis (MS). Our T1D collections are divided into three sample subsets, consisting of set 1 families (up to 754 families), set 2 families (up to 743 families), and a case-control collection (ranging from 1,500 to 4,400 cases and 1,500 to 4,600 controls). Each SNP was genotyped in one or more of these subsets. Our study typically had approximately 80% statistical power for a minor allele frequency (MAF) >5% and odds ratios (OR) of 1.5 with the type 1 error rate, alpha = 0.05. RESULTS: We found no evidence of association with T1D at most of the loci studied 0.02

Discovery, linkage disequilibrium and association analyses of polymorphisms of the immune complement inhibitor, decay-accelerating factor gene (DAF/CD55) in type 1 diabetes.

BACKGROUND: Type 1 diabetes (T1D) is a common autoimmune disease resulting from T-cell mediated destruction of pancreatic beta cells. Decay accelerating factor (DAF, CD55), a glycosylphosphatidylinositol-anchored membrane protein, is a candidate for autoimmune disease susceptibility based on its role in restricting complement activation and evidence that DAF expression modulates the phenotype of mice models for autoimmune disease. In this study, we adopt a linkage disequilibrium (LD) mapping approach to test for an association between the DAF gene and T1D. RESULTS: Initially, we used HapMap II genotype data to examine LD across the DAF region. Additional resequencing was required, identifying 16 novel polymorphisms. Combining both datasets, a LD mapping approach was adopted to test for association with T1D. Seven tag SNPs were selected and genotyped in case-control (3,523 cases and 3,817 controls) and family (725 families) collections. CONCLUSION: We obtained no evidence of association between T1D and the DAF region in two independent collections. In addition, we assessed the impact of using only HapMap II genotypes for the selection of tag SNPs and, based on this study, found that HapMap II genotypes may require additional SNP discovery for comprehensive LD mapping of some genes in common disease.

Polymorphism discovery and association analyses of the interferon genes in type 1 diabetes.

BACKGROUND: The aetiology of the autoimmune disease type 1 diabetes (T1D) involves many genetic and environmental factors. Evidence suggests that innate immune responses, including the action of interferons, may also play a role in the initiation and/or pathogenic process of autoimmunity. In the present report, we have adopted a linkage disequilibrium (LD) mapping approach to test for an association between T1D and three regions encompassing 13 interferon alpha (IFNA) genes, interferon omega-1 (IFNW1), interferon beta-1 (IFNB1), interferon gamma (IFNG) and the interferon consensus-sequence binding protein 1 (ICSBP1). RESULTS: We identified 238 variants, most, single nucleotide polymorphisms (SNPs), by sequencing IFNA, IFNB1, IFNW1 and ICSBP1, 98 of which where novel when compared to dbSNP build 124. We used polymorphisms identified in the SeattleSNP database for INFG. A set of tag SNPs was selected for each of the interferon and interferon-related genes to test for an association between T1D and this complex gene family. A total of 45 tag SNPs were selected and genotyped in a collection of 472 multiplex families. CONCLUSION: We have developed informative sets of SNPs for the interferon and interferon related genes. No statistical evidence of a major association between T1D and any of the interferon and interferon related genes tested was found.

Haplotype tag single nucleotide polymorphism analysis of the human orthologues of the rat type 1 diabetes genes Ian4 (Lyp/Iddm1) and Cblb.

The diabetes-prone BioBreeding (BB) and Komeda diabetes-prone (KDP) rats are both spontaneous animal models of human autoimmune, T-cell-associated type 1 diabetes. Both resemble the human disease, and consequently, susceptibility genes for diabetes found in these two strains can be considered as potential candidate genes in humans. Recently, a frameshift deletion in Ian4, a member of the immune-associated nucleotide (Ian)-related gene family, has been shown to map to BB rat Iddm1. In the KDP rat, a nonsense mutation in the T-cell regulatory gene, Cblb, has been described as a major susceptibility locus. Following a strategy of examining the human orthologues of susceptibility genes identified in animal models for association with type 1 diabetes, we identified single nucleotide polymorphisms (SNPs) from each gene by resequencing PCR product from at least 32 type 1 diabetic patients. Haplotype tag SNPs (htSNPs) were selected and genotyped in 754 affected sib-pair families from the U.K. and U.S. Evaluation of disease association by a multilocus transmission/disequilibrium test (TDT) gave a P value of 0.484 for IAN4L1 and 0.692 for CBLB, suggesting that neither gene influences susceptibility to common alleles of human type 1 diabetes in these populations.

Association analysis of the lymphocyte-specific protein tyrosine kinase (LCK) gene in type 1 diabetes.

Prior data associating the expression of lymphocyte-specific protein tyrosine kinase (LCK) with type 1 diabetes, its critical function in lymphocytes, and the linkage of the region to diabetes in the nonobese diabetic (NOD) mouse model make LCK a premier candidate for a susceptibility gene. Resequencing of LCK in 32 individuals detected seven single nucleotide polymorphisms (SNPs) with allele frequencies >3%, including four common SNPs previously reported. These and six other SNPs from dbSNP were genotyped in a two-stage strategy using 2,430 families and were all shown not to be significantly associated with type 1 diabetes. We conclude that a major role for the common LCK polymorphisms in type 1 diabetes is unlikely. However, we cannot rule out the possibility of there being a causal variant outside the exonic, intronic, and untranslated regions studied.

Investigating the utility of combining phi29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray genotyping.

BACKGROUND: Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated phi29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates. RESULTS: Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA. CONCLUSIONS: We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.

A combined strategy for quantitative trait loci detection by genome-wide association.

BACKGROUND: We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy. RESULTS: A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes pre-corrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiple-marker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance. CONCLUSION: The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.

Rapid and robust association mapping of expression quantitative trait loci.

We applied a simple and efficient two-step method to analyze a family-based association study of gene expression quantitative trait loci (eQTL) in a mixed model framework. This two-step method produces very similar results to the full mixed model method, with our method being significantly faster than the full model. Using the Genetic Analysis Workshop 15 (GAW15) Problem 1 data, we demonstrated the value of data filtering for reducing the number of tests and controlling the number of false positives. Specifically, we showed that removing non-expressed genes by filtering on expression variability effectively reduced the number of tests by nearly 50%. Furthermore, we demonstrated that filtering on genotype counts substantially reduced spurious detection. Finally, we restricted our analysis to the markers and transcripts that were closely located. We found five times more signals in close proximity (cis-) to transcripts than in our genome-wide analysis. Our results suggest that careful pre-filtering and partitioning of data are crucial for controlling false positives and allowing detection of genuine effects in genetic analysis of gene expression.

Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15.

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

No evidence for association of the TATA-box binding protein glutamine repeat sequence or the flanking chromosome 6q27 region with type 1 diabetes.

Susceptibility to the autoimmune disease type 1 diabetes has been linked to human chromosome 6q27 and, moreover, recently associated with one of the genes in the region, TATA box-binding protein (TBP). Using a much larger sample of T1D families than those studied by others, and by extensive re-sequencing of nine other genes in the proximity, in which we identified 279 polymorphisms, 83 of which were genotyped in up to 725 T1D multiplex and simplex families, we obtained no evidence for association of the TBP CAG/CAA (glutamine) microsatellite repeat sequence with disease, or for nine other genes, PDCD2, PSMB1, KIAA1838, DLL1, dJ894D12.4, FLJ25454, FLJ13162, FLJ11152, PHF10 and CCR6. This study also provides an exon-based tag single nucleotide polymorphism map for these 10 genes that can be used for analysis of other diseases.

Development of an integrated genome informatics, data management and workflow infrastructure: a toolbox for the study of complex disease genetics.

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.

Comparative high-resolution analysis of linkage disequilibrium and tag single nucleotide polymorphisms between populations in the vitamin D receptor gene.

A genome-wide map of single nucleotide polymorphisms (SNP) and a pattern of linkage disequilibrium (LD) between their alleles are being established in three main ethnic groups. An important question is the applicability of such maps to different populations within a main ethnic group. Therefore, we have developed high-resolution SNP, haplotype and LD maps of vitamin D receptor gene region in large samples from five populations. Comparative analysis reveals that the LD patterns are identical in all four European populations tested with two small regions of 1.3 and 5.7 kb at which LD is disrupted completely resulting in three block-like regions over which there is significant and extensive LD. In an African population the pattern is similar, but two additional LD-breaking spots are also apparent. This LD pattern suggests combined action of recombination hotspots and founder effects, but cannot be explained by random recombination and genetic drift alone. Direct comparison indicates that the tag SNPs selected in one European population effectively predict the non-tag SNPs in the other Europeans, but not in the Gambians, for this region.

Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease.

Genes and mechanisms involved in common complex diseases, such as the autoimmune disorders that affect approximately 5% of the population, remain obscure. Here we identify polymorphisms of the cytotoxic T lymphocyte antigen 4 gene (CTLA4)--which encodes a vital negative regulatory molecule of the immune system--as candidates for primary determinants of risk of the common autoimmune disorders Graves' disease, autoimmune hypothyroidism and type 1 diabetes. In humans, disease susceptibility was mapped to a non-coding 6.1 kb 3' region of CTLA4, the common allelic variation of which was correlated with lower messenger RNA levels of the soluble alternative splice form of CTLA4. In the mouse model of type 1 diabetes, susceptibility was also associated with variation in CTLA-4 gene splicing with reduced production of a splice form encoding a molecule lacking the CD80/CD86 ligand-binding domain. Genetic mapping of variants conferring a small disease risk can identify pathways in complex disorders, as exemplified by our discovery of inherited, quantitative alterations of CTLA4 contributing to autoimmune tissue destruction.