Cost-effective analysis of candidate genes using htSNPs: a staged approach

Association analysis of PRNP gene region with chronic wasting disease in Rocky Mountain elk

Background

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy (TSE) of cervids including white-tailed (Odocoileus virginianus) and mule deer (Odocoileus hemionus), Rocky Mountain elk (Cervus elaphus nelsoni), and moose (Alces alces). A leucine variant at position 132 (132L) in prion protein of Rocky Mountain elk confers a long incubation time with CWD, but not complete resistance. However, variants in regulatory regions outside the open reading frame of PRNP have been associated with varying degrees of susceptibility to prion disease in other species, and some variants have been observed in similar regions of Rocky Mountain elk PRNP. Thus, additional genetic variants might provide increased protection, either alone or in combination with 132L.

Findings

This study provided genomic sequence of all exons for PRNP of Rocky Mountain elk. Many functional sites in and around the PRNP gene region were sequenced, and this report approximately doubled (to 75) the number of known variants in this region. A haplotype-tagging approach was used to reduce the number of genetic variants required to survey this variation in the PRNP gene region of 559 Rocky Mountain elk. Eight haplotypes were observed with frequencies over 1.0%, and one haplotype was present at 71.2% frequency, reflecting limited genetic diversity in the PRNP gene region.

Conclusions

The presence of 132L cut odds of CWD by more than half (Odds Ratio = 0.43; P = 0.0031), which was similar to a previous report. However after accounting for 132L, no association with CWD was found for any additional variants in the PRNP region (P > 0.05).

L2-Boosting algorithm applied to high-dimensional problems in genomic selection

The L(2)-Boosting algorithm is one of the most promising machine-learning techniques that has appeared in recent decades. It may be applied to high-dimensional problems such as whole-genome studies, and it is relatively simple from a computational point of view. In this study, we used this algorithm in a genomic selection context to make predictions of yet to be observed outcomes. Two data sets were used: (1) productive lifetime predicted transmitting abilities from 4702 Holstein sires genotyped for 32 611 single nucleotide polymorphisms (SNPs) derived from the Illumina BovineSNP50 BeadChip, and (2) progeny averages of food conversion rate, pre-corrected by environmental and mate effects, in 394 broilers genotyped for 3481 SNPs. Each of these data sets was split into training and testing sets, the latter comprising dairy or broiler sires whose ancestors were in the training set. Two weak learners, ordinary least squares (OLS) and non-parametric (NP) regression were used for the L2-Boosting algorithm, to provide a stringent evaluation of the procedure. This algorithm was compared with BL [Bayesian LASSO (least absolute shrinkage and selection operator)] and BayesA regression. Learning tasks were carried out in the training set, whereas validation of the models was performed in the testing set. Pearson correlations between predicted and observed responses in the dairy cattle (broiler) data set were 0.65 (0.33), 0.53 (0.37), 0.66 (0.26) and 0.63 (0.27) for OLS-Boosting, NP-Boosting, BL and BayesA, respectively. The smallest bias and mean-squared errors (MSEs) were obtained with OLS-Boosting in both the dairy cattle (0.08 and 1.08, respectively) and broiler (-0.011 and 0.006) data sets, respectively. In the dairy cattle data set, the BL was more accurate (bias=0.10 and MSE=1.10) than BayesA (bias=1.26 and MSE=2.81), whereas no differences between these two methods were found in the broiler data set. L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time.

Genetic underpinnings of tardive dyskinesia: passing the baton to pharmacogenetics

Manifestation of tardive dyskinesia (TD) among schizophrenia subjects on long-term antipsychotic treatment with typical drugs has been a clinical concern. Despite its association with extrapyramidal symptoms, typical drugs are still routinely prescribed globally though marginally superior atypical drugs have long been available. The genetic component in the etiology of TD is well documented. Search for these determinants has led to a few consensus associations of CYP2D6 *10, CYP1A2*1F, DRD2 Taq1A (rs1800497), DRD3 Ser9Gly (rs6280) and MnSOD Ala9Val (rs4880) variants with TD. However, translation of these observations into the clinic has not been achieved so far. This review discusses the salient features of TD etiopathology, current status of TD genetics, interactions between genetic and nongenetic factors, some major drawbacks, challenges and expected focus in TD research over the next decade, with emphasis on pharmacogenetics.

Drug resistance and genetic mapping in Plasmodium falciparum

Drug resistance in malaria parasites is a serious public health burden, and resistance to most of the antimalarial drugs currently in use has been reported. A better understanding of the molecular mechanisms of drug resistance is urgently needed to slow or circumvent the spread of resistance, to allow local treatments to be deployed more effectively to prolong the life span of the current drugs, and to develop new drugs. Although mutations in genes determining resistance to drugs such as chloroquine and the antifolates have been identified, we still do not have a full understanding of the resistance mechanisms, and genes that contribute to resistance to many other drugs remain to be discovered. Genetic mapping is a powerful tool for the identification of mutations conferring drug resistance in malaria parasites because most drug-resistant phenotypes were selected within the past 60 years. High-throughput methods for genotyping large numbers of single nucleotide polymorphisms (SNPs) and microsatellites (MSs) are now available or are being developed, and genome-wide association studies for malaria traits will soon become a reality. Here we discuss strategies and issues related to mapping genes contributing to drug resistance in the human malaria parasite Plasmodium falciparum.

Catechol-O-methyltransferase contributes to genetic susceptibility shared among anxiety spectrum phenotypes

BACKGROUND

Catechol-O-methyltransferase (COMT) has been investigated for its possible role in a wide range of psychiatric phenotypes. In particular, several studies support association of this gene with panic disorder and other anxiety-related traits.

METHODS

We examined the COMT gene for association with genetic risk across a range of anxiety spectrum phenotypes. We used multivariate structural equation modeling to select twin pairs scoring at the extremes of a latent genetic risk factor shared by neuroticism, several anxiety disorders, and major depression from a large population-based twin sample. With one member from each of these pairs, the resulting sample of 589 cases and 539 control subjects were entered into a two-stage association study in which genetic markers were screened in stage 1, the positive results of which were tested for replication in stage 2.

RESULTS

The functional val158met polymorphism (rs4680) plus nine other single nucleotide polymorphism markers selected to capture the major allelic variation across the COMT locus were analyzed for differences between cases and control subjects. Although the val (G) allele of rs4680 showed marginally significant association in our combined stage 1 plus stage 2 sample, a high-risk haplotype of this allele with the A allele of rs165599 was significantly over-represented in cases (p = 1.97e-5, odds ratio = 1.95). This haplotype also predicted individual differences in neuroticism and risk for several anxiety disorders and major depression. Consistent with prior studies, our findings are female-specific.

CONCLUSIONS

Variations in the COMT gene contribute to genetic risk shared across a range of anxiety-related phenotypes.

Controlling false discoveries in genetic studies

A false discovery occurs when a researcher concludes that a marker is involved in the etiology of the disease whereas in reality it is not. In genetic studies the risk of false discoveries is very high because only few among the many markers that can be tested will have an effect on the disease. In this article, we argue that it may be best to use methods for controlling false discoveries that would introduce the same ratio of false discoveries divided by all rejected tests into the literature regardless of systematic differences between studies. After a brief discussion of traditional "multiple testing" methods, we show that methods that control the false discovery rate (FDR) may be more suitable to achieve this goal. These FDR methods are therefore discussed in more detail. Instead of merely testing for main effects, it may be important to search for gene-environment/covariate interactions, gene-gene interactions or genetic variants affecting disease subtypes. In the second section, we point out the challenges involved in controlling false discoveries in such searches. The final section discusses the role of replication studies for eliminating false discoveries and the complexities associated with the definition of what constitutes a replication and the design of these studies.

A network of dopaminergic gene variations implicated as risk factors for schizophrenia

We evaluated the hypothesis that dopaminergic polymorphisms are risk factors for schizophrenia (SZ). In stage I, we screened 18 dopamine-related genes in two independent US Caucasian samples: 150 trios and 328 cases/501 controls. The most promising associations were detected with SLC6A3 (alias DAT), DRD3, COMT and SLC18A2 (alias VMAT2). In stage II, we comprehensively evaluated these four genes by genotyping 68 SNPs in all 478 cases and 501 controls from stage I. Fifteen (23.1%) significant associations were found (p < or = 0.05). We sought epistasis between pairs of SNPs providing evidence of a main effect and observed 17 significant interactions (169 tests); 41.2% of significant interactions involved rs3756450 (5' near promoter) or rs464049 (intron 4) at SLC6A3. In stage III, we confirmed our findings by genotyping 65 SNPs among 659 Bulgarian trios. Both SLC6A3 variants implicated in the US interactions were overtransmitted in this cohort (rs3756450, p = 0.035; rs464049, p = 0.011). Joint analyses from stages II and III identified associations at all four genes (p(joint) < 0.05). We tested 29 putative interactions from stage II and detected replication between seven locus pairs (p < or = 0.05). Simulations suggested our stage II and stage III interaction results were unlikely to have occurred by chance (p = 0.008 and 0.001, respectively). In stage IV we evaluated rs464049 and rs3756450 for functional effects and found significant allele-specific differences at rs3756450 using electrophoretic mobility shift assays and dual-luciferase promoter assays. Our data suggest that a network of dopaminergic polymorphisms increase risk for SZ.

Genome-wide association scans identify multiple confirmed susceptibility loci for Crohn's disease: lessons for study design

The last 12 months have seen unprecedented progress in identifying the susceptibility genes that predispose to common disease in general and Crohn's disease in particular. Success has derived from the new technique of genome-wide association scanning in large panels of cases and controls. This has itself been made possible by the sequencing of the human genome,12 development of a map of common human haplotype structure (HapMap),3 and advances in genotyping technologies permitting ascertainment of hundreds of thousands of genetic variants in multiple individuals. Several previously unsuspected pathways particularly relating to innate immunity and the early host response to bacteria have been revealed as key determinants of Crohn's disease susceptibility. These will provide a solid foundation for directed functional and translational research. Further, the wealth of confirmed association data coming from unbiased surveys of the genome provided by genome-wide association scans provide several key pointers regarding design and analysis of inflammatory bowel disease genetics studies in the future.

The candidate genes TAF5L, TCF7, PDCD1, IL6 and ICAM1 cannot be excluded from having effects in type 1 diabetes

BACKGROUND

As genes associated with immune-mediated diseases have an increased prior probability of being associated with other immune-mediated diseases, we tested three such genes, IL23R, IRF5 and CD40, for an association with type 1 diabetes. In addition, we tested seven genes, TAF5L, PDCD1, TCF7, IL12B, IL6, ICAM1 and TBX21, with published marginal or inconsistent evidence of an association with type 1 diabetes.

METHODS

We genotyped reported polymorphisms of the ten genes, nonsynonymous SNPs (nsSNPs) and, for the IL12B and IL6 regions, tag SNPs in up to 7,888 case, 8,858 control and 3,142 parent-child trio samples. In addition, we analysed data from the Wellcome Trust Case Control Consortium genome-wide association study to determine whether there was any further evidence of an association in each gene region.

RESULTS

We found some evidence of associations between type 1 diabetes and TAF5L, PDCD1, TCF7 and IL6 (ORs = 1.05 - 1.13; P = 0.0291 - 4.16 x 10-4). No evidence of an association was obtained for IL12B, IRF5, IL23R, ICAM1, TBX21 and CD40, although there was some evidence of an association (OR = 1.10; P = 0.0257) from the genome-wide association study for the ICAM1 region.

CONCLUSION

We failed to exclude the possibility of some effect in type 1 diabetes for TAF5L, PDCD1, TCF7, IL6 and ICAM1. Additional studies, of these and other candidate genes, employing much larger sample sizes and analysis of additional polymorphisms in each gene and its flanking region will be required to ascertain their contributions to type 1 diabetes susceptibility.

Simulating association studies: a data-based resampling method for candidate regions or whole genome scans

MOTIVATION

Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realistic patterns of linkage disequilibrium and allele frequencies for typed SNPs.

METHODS

We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project.

RESULTS

Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.

Efficacy assessment of SNP sets for genome-wide disease association studies

The power of a genome-wide disease association study depends critically upon the properties of the marker set used, particularly the number and physical spacing of markers, and the level of inter-marker association due to linkage disequilibrium. Extending our previously devised theoretical framework for the entropy-based selection of genetic markers, we have developed a local measure of the efficacy of a marker set, relative to including a maximally polymorphic single nucleotide polymorphism (SNP) at the map position of interest. Using this quantitative criterion, we evaluated five currently available SNP sets, namely Affymetrix 100K and 500K, and Illumina 100K, 300K and 550K in the CEU, YRI and JPT + CHB HapMap populations. At 50% relative efficacy, the commercial marker sets cover between 19 and 68% of the human genome, depending upon the population under study. An optimal technology-independent 500K marker set constructed from HapMap for Caucasians, in contrast, would achieve 73% coverage at the same relative efficacy.

A Bayesian measure of the probability of false discovery in genetic epidemiology studies

In light of the vast amounts of genomic data that are now being generated, we propose a new measure, the Bayesian false-discovery probability (BFDP), for assessing the noteworthiness of an observed association. BFDP shares the ease of calculation of the recently proposed false-positive report probability (FPRP) but uses more information, has a noteworthy threshold defined naturally in terms of the costs of false discovery and nondiscovery, and has a sound methodological foundation. In addition, in a multiple-testing situation, it is straightforward to estimate the expected numbers of false discoveries and false nondiscoveries. We provide an in-depth discussion of FPRP, including a comparison with the q value, and examine the empirical behavior of these measures, along with BFDP, via simulation. Finally, we use BFDP to assess the association between 131 single-nucleotide polymorphisms and lung cancer in a case-control study.

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at P < 5 x 10(-7) between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (P(follow-up) Collapse

Key Words Collapse

MESH Headings

• Adolescent
• Case-Control Studies
• Chromosome Mapping
• Diabetes Mellitus, Type 1/genetics
• Genetic Predisposition to Disease
• Genome, Human
• Humans
• Polymorphism, Single Nucleotide

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Grants

• 061859 Wellcome Trust
• 089989 Wellcome Trust
• G0000934 Medical Research Council
• Wellcome Trust
• 061858 Wellcome Trust

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Affiliation(s)

John A Todd Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0XY, UK.
Neil M Walker
Jason D Cooper
Deborah J Smyth
Kate Downes
Vincent Plagnol
Rebecca Bailey
Sergey Nejentsev
Sarah F Field
Felicity Payne
Christopher E Lowe
Jeffrey S Szeszko
Jason P Hafler
Lauren Zeitels
Jennie H M Yang
Adrian Vella
Sarah Nutland
Helen E Stevens
Helen Schuilenburg
Gillian Coleman
Meeta Maisuria
William Meadows
Luc J Smink
Barry Healy
Oliver S Burren
Alex A C Lam
Nigel R Ovington
James Allen
Ellen Adlem
Hin-Tak Leung
Chris Wallace
Joanna M M Howson
Cristian Guja
Constantin Ionescu-Tîrgovişte
Matthew J Simmonds
Joanne M Heward
Stephen C L Gough
David B Dunger
Linda S Wicker
David G Clayton

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One degree of freedom for dominance in indirect association studies

Usual tests of association using tag single nucleotide polymorphisms (SNPs) assume that the alleles of the causal locus act additively and that these alleles are then predicted indirectly via a set of tag SNPs. In the presence of strong dominance effects this model is not correct and an extra term needs to be included, which uses the tag SNPs to predict the heterozygosity of the causal locus. Assuming this scenario of a strong dominance effect, we present an appropriate test statistic and investigate how much power, if any, we gain by adding this single degree of freedom for dominance.

Gene discovery in diabetic nephropathy

Diabetic nephropathy affects approximately one third of diabetic patients and is the leading cause of end-stage renal disease. Identification of genetic susceptibility factors contributing to the development of diabetic nephropathy could facilitate prediction, development of improved treatments, and prevention of this devastating complication of diabetes. Although investigations to identify the causal genetic variants associated with diabetic nephropathy have been inconclusive, new approaches, including whole genome association scanning, offer hope for the future.

Genetic associations in preterm birth: a primer of marker selection, study design, and data analysis

Spontaneous preterm birth (PTB; delivery before 37 weeks gestation) is a primary risk factor for infant morbidity and mortality. The etiology is unclear, but there is evidence that there is a genetic predisposition to PTB. Armed with the suggestion of genetic risk factors and the failure to identify useful biomarkers, investigators are starting to actively pursue the role of genetic predisposition in PTB. Several studies have been done to date assessing the role of single gene variants. However, positive findings have failed to replicate. We argue that heterogeneity in study designs, definition of phenotype, single-nucleotide polymorphism (SNP) selection, population selection, and sample size makes data interpretation difficult in complex phenotypes such as PTB. In this review, we introduce general concepts of study designs in genetic epidemiology, selection of candidate genes and markers for analysis, and analytical methodologies. We also introduce how the concept of gene-gene interactions (biologic epistasis) and gene-environment interactions may affect the predisposition to PTB.

Association between glutamic acid decarboxylase genes and anxiety disorders, major depression, and neuroticism

Abnormalities in the gamma-aminobutyric acid (GABA) neurotransmitter system have been noted in subjects with mood and anxiety disorders. Glutamic acid decarboxylase (GAD) enzymes synthesize GABA from glutamate, and, thus, are reasonable candidate susceptibility genes for these conditions. In this study, we examined the GAD1 and GAD2 genes for their association with genetic risk across a range of internalizing disorders. We used multivariate structural equation modeling to identify common genetic risk factors for major depression, generalized anxiety disorder, panic disorder, agoraphobia, social phobia and neuroticism (N) in a sample of 9270 adult subjects from the population-based Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. One member from each twin pair for whom DNA was available was selected as a case or control based on scoring at the extremes of the genetic factor extracted from the analysis. The resulting sample of 589 cases and 539 controls was entered into a two-stage association study in which candidate loci were screened in stage 1, the positive results of which were tested for replication in stage 2. Several of the six single-nucleotide polymorphisms tested in the GAD1 region demonstrated significant association in both stages, and a combined analysis in all 1128 subjects indicated that they formed a common high-risk haplotype that was significantly over-represented in cases (P=0.003) with effect size OR=1.23. Out of 14 GAD2 markers screened in stage 1, only one met the threshold criteria for follow-up in stage 2. This marker, plus three others that formed significant haplotype combinations in stage 1, did not replicate their association with the phenotype in stage 2. Subject to confirmation in an independent sample, our study suggests that variations in the GAD1 gene may contribute to individual differences in N and impact susceptibility across a range of anxiety disorders and major depression.

Integrated analysis of genetic data with R

Genetic data are now widely available. There is, however, an apparent lack of concerted effort to produce software systems for statistical analysis of genetic data compared with other fields of statistics. It is often a tremendous task for end-users to tailor them for particular data, especially when genetic data are analysed in conjunction with a large number of covariates. Here, R http://www.r-project.org, a free, flexible and platform-independent environment for statistical modelling and graphics is explored as an integrated system for genetic data analysis. An overview of some packages currently available for analysis of genetic data is given. This is followed by examples of package development and practical applications. With clear advantages in data management, graphics, statistical analysis, programming, internet capability and use of available codes, it is a feasible, although challenging, task to develop it into an integrated platform for genetic analysis; this will require the joint efforts of many researchers.

Statistical false positive or true disease pathway?

Three very recent reports provide convincing statistical evidence (P < 10(-8)), at a genome-wide level, of the association of common polymorphisms with three different common diseases: systemic lupus erythematosus (IRF5), prostate cancer and type 1 diabetes (IFIH1 region). This adds to the trickle--soon to be a flood--of disease association results that are highly unlikely to be false positives. There are other convincing examples in the last 12 months: age-related macular degeneration (CFH), type 1 diabetes (IL2RA, also known as CD25) and type 2 diabetes (TCF7L2). Given 20 years of a literature full of irreproducible results, what has changed?

Optimization of Two-Stage Genetic Designs Where Data Are Combined Using an Accurate and Efficient Approximation for Pearson's Statistic

There is an increasing interest in the use of two-stage case-control studies to reduce genotyping costs in the search for genes underlying common disorders. Instead of analyzing the data from the second stage separately, a more powerful test can be performed by combining the data from both stages. However, standard tests cannot be used because only the markers that are significant in the first stage are selected for the second stage and the test statistics at both stages are dependent because they partly involve the same data. Theoretical approximations are not available for commonly used test statistics and in this specific context simulations can be problematic because of the computational burden. We therefore derived a cost-effective, that is, accurate but fast in terms of central processing unit (CPU) time, approximation for the distribution of Pearson's statistic on 2 xm contingency tables in two-stage design with combined data. We included this approximation in an iterative method for designing optimal two-stage studies. Simulations supported the accuracy of our approximation. Numerical results confirmed that the use of two-stage designs reduces the genotyping burden substantially. Compared to not combining data, combining the data decreases the required sample sizes on average by 15% and the genotyping burden by 5%.

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.

Detecting multiple associations in genome-wide studies

Recent developments in the statistical analysis of genome-wide studies are reviewed. Genome-wide analyses are becoming increasingly common in areas such as scans for disease-associated markers and gene expression profiling. The data generated by these studies present new problems for statistical analysis, owing to the large number of hypothesis tests, comparatively small sample size and modest number of true gene effects. In this review, strategies are described for optimising the genotyping cost by discarding promising genes at an earlier stage, saving resources for the genes that show a trend of association. In addition, there is a review of new methods of analysis that combine evidence across genes to increase sensitivity to multiple true associations in the presence of many non-associated genes. Some methods achieve this by including only the most significant results, whereas others model the overall distribution of results as a mixture of distributions from true and null effects. Because genes are correlated even when having no effect, permutation testing is often necessary to estimate the overall significance, but this can be very time consuming. Efficiency can be improved by fitting a parametric distribution to permutation replicates, which can be re-used in subsequent analyses. Methods are also available to generate random draws from the permutation distribution. The review also includes discussion of new error measures that give a more reasonable interpretation of genome-wide studies, together with improved sensitivity. The false discovery rate allows a controlled proportion of positive results to be false, while detecting more true positives; and the local false discovery rate and false-positive report probability give clarity on whether or not a statistically significant test represents a real discovery.

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.

A genome-wide DNA microsatellite association screen to identify chromosomal regions harboring candidate genes in diabetic nephropathy

In an effort to accelerate the identification of susceptibility genes in diabetic nephropathy, the first genome-wide fluorescence-based DNA microsatellite (n=6000) association screen was performed, using pools of genomic DNA derived from Irish patients with (cases; n=200) and without (controls; n=200) type 1 diabetic nephropathy. Allele image profiles were generated for 5353 (89.2%) microsatellite markers for both case and control pools. Allele counts (estimated from allele image profiles) were compared in case versus control groups, and empirical P values were generated. Markers then were ranked on the basis of their empirical P values (lowest to highest). Repeat PCR amplification and electrophoresis of pooled samples were performed systematically on ranked markers until the 50 most associated markers with consistent results were identified. DNA samples that composed the pools then were genotyped individually for these markers. Two markers on chromosome 10, D10S558 (Pcorrected=0.005) and D10S1435 (Pcorrected=0.016), revealed statistically significant associations with diabetic nephropathy. An additional four markers (D6S281, D4S2937, D2S291, and D17S515) also are worthy of further investigation. Relevant functional candidate genes have been identified in the vicinity of these markers, demonstrating the feasibility of low-resolution genome-wide microsatellite association screening to identify possible candidate genes for diabetic nephropathy.

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.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

Genetic association studies

We review the rationale behind and discuss methods of design and analysis of genetic association studies. There are similarities between genetic association studies and classic epidemiological studies of environmental risk factors but there are also issues that are specific to studies of genetic risk factors such as the use of particular family-based designs, the need to account for different underlying genetic mechanisms, and the effect of population history. Association differs from linkage (covered elsewhere in this series) in that the alleles of interest will be the same across the whole population. As with other types of genetic epidemiological study, issues of design, statistical analysis, and interpretation are very important.

Recent developments in genomewide association scans: a workshop summary and review

With the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.

A comparison of tagging methods and their tagging space

Single-nucleotide polymorphism (SNP) tagging is widely used as a way of saving genotyping costs in association studies. A number of different tagging methods have been developed to reduce the number of markers to be genotyped while maintaining power for detecting effects on non-assayed SNPs. How the different methods perform in different settings, the degree to which they overlap and share common tags and how they differ are important questions. We investigated these questions by comparing three widely used tagging methods/algorithms--one haplotype r2-based method, one pair-wise r2-based method and one method which was based on haplotype diversity but focused on major haplotypes. Tagging efficiency was defined as the number of genotyped markers divided by the number of tagging SNPs. Tagging effectiveness was defined as the proportion of un-genotyped or 'hidden' SNPs being detected (having a pair-wise or haplotype r2 with a set of tagging SNPs over a threshold, e.g. haplotype r2> or =0.80). The ENCODE regions genotyped on the HapMap CEPH individuals were examined in this study. Tagging effectiveness was generally poor for rare SNPs than for common SNPs, for all three tagging methods. Inclusion of rare SNPs into initial HapMap scheme could enhance the performance of tags on rare hidden SNPs at the expense of increased genotyping cost. At a moderate tagging efficiency, more than 90% of hidden SNPs detected by tagging SNPs selected by one method were also detected by tagging SNPs selected by another method, and this figure could be increased to 100% if tagging efficiency was allowed to drop. These results indicate that the tagging space is highly concordant between different tagging methods, despite the fact that they often involve different sets of tagging SNPs.

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.

Comparison of population- and family-based methods for genetic association analysis in the presence of interacting loci

We compared different ascertainment schemes for genetic association analysis: affected sib-pairs (ASPs), case-parent trios, and unrelated cases and controls. We found, with empirical type 1 diabetes data at four known disease loci, that studies based on case-parent trios and on unmatched cases and controls often gave higher odds ratio estimates and stronger significance test values than ASP designs. We used simulations and a simplified disease model involving two interacting loci, one of large effect and one smaller, to examine interaction models that could cause such an effect. The different ascertainment schemes were compared for power to detect an effect when only the locus of smaller effect was genotyped. ASPs showed the greatest power for association testing under most models of interaction except under additive and certain epistatic crossover models, for which case/controls and case-parent trios did better. All ascertainment schemes gave an unbiased estimation of log genotype relative risks (GRRs) under a multiplicative model. Under nonmultiplicative interactions, GRRs at the minor locus as estimated from ASPs could be biased upwards or downwards, resulting in either an increase or decrease in power compared to the case/control or trio design. For the four known type 1 diabetes loci, we observed decreased risks with ASPs, which could be due to additive interactions with the remaining susceptibility loci. Thus, the optimal ascertainment strategy in genetic association studies depends on the unknown underlying multilocus genetic model, and on whether the goal of the study is to detect an effect or to accurately estimate the resulting disease risks.

Localization of a type 1 diabetes locus in the IL2RA/CD25 region by use of tag single-nucleotide polymorphisms

As part of an ongoing search for genes associated with type 1 diabetes (T1D), a common autoimmune disease, we tested the biological candidate gene IL2RA (CD25), which encodes a subunit (IL-2R alpha) of the high-affinity interleukin-2 (IL-2) receptor complex. We employed a tag single-nucleotide polymorphism (tag SNP) approach in large T1D sample collections consisting of 7,457 cases and controls and 725 multiplex families. Tag SNPs were analyzed using a multilocus test to provide a regional test for association. We found strong statistical evidence in the case-control collection (P=6.5x10(-8)) for a T1D locus in the CD25 region of chromosome 10p15 and replicated the association in the family collection (P=7.3x10(-3); combined P=1.3x10(-10)). These results illustrate the utility of tag SNPs in a chromosome-regional test of disease association and justify future fine mapping of the causal variant in the region.

Genome-wide association studies for common diseases and complex traits

Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.

Genome-wide association studies: theoretical and practical concerns

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

Construction and analysis of tag single nucleotide polymorphism maps for six human-mouse orthologous candidate genes in type 1 diabetes

Background

One strategy to help identify susceptibility genes for complex, multifactorial diseases is to map disease loci in a representative animal model of the disorder. The nonobese diabetic (NOD) mouse is a model for human type 1 diabetes. Linkage and congenic strain analyses have identified several NOD mouse Idd (insulin dependent diabetes) loci, which have been mapped to small chromosome intervals, for which the orthologous regions in the human genome can be identified. Here, we have conducted re-sequencing and association analysis of six orthologous genes identified in NOD Idd loci: NRAMP1/SLC11A1 (orthologous to Nramp1/Slc11a1 in Idd5.2), FRAP1 (orthologous to Frap1 in Idd9.2), 4-1BB/CD137/TNFRSF9 (orthologous to 4-1bb/Cd137/Tnrfrsf9 in Idd9.3), CD101/IGSF2 (orthologous to Cd101/Igsf2 in Idd10), B2M (orthologous to B2m in Idd13) and VAV3 (orthologous to Vav3 in Idd18).

Results

Re-sequencing of a total of 110 kb of DNA from 32 or 96 type 1 diabetes cases yielded 220 single nucleotide polymorphisms (SNPs). Sixty-five SNPs, including 54 informative tag SNPs, and a microsatellite were selected and genotyped in up to 1,632 type 1 diabetes families and 1,709 cases and 1,829 controls.

Conclusion

None of the candidate regions showed evidence of association with type 1 diabetes (P values > 0.2), indicating that common variation in these key candidate genes does not play a major role in type 1 diabetes susceptibility in the European ancestry populations studied.

Efficiency and consistency of haplotype tagging of dense SNP maps in multiple samples

Haplotype tagging is a means of retaining most of the information in high density marker maps, while reducing genotyping requirements. Estimates of the numbers of tagging SNPs required to cover the human genome have varied widely, ranging from 100,000 to 1,000,000. Tagging has been applied to a number of gene-based datasets but has not been evaluated in contexts reflecting those of genome-wide association studies--large chromosome regions and multiple samples drawn from the same population. We analysed 5000 common markers across a 10 Mb segment of human chromosome 20 in three samples (UK Caucasian, CEPH Caucasian, African American) to evaluate tagging efficiency and consistency. Overall, the results indicate a high degree of efficiency, yielding 3-5-fold savings in Caucasians and 2-3-fold savings in African Americans. These levels varied according to linkage disequilibrium (LD) levels, tagging thresholds and allele frequencies, but in high LD regions they did not vary markedly due to marker density. However, a strong positive relationship between marker density and tagging was observed, relating to the fact that increasing marker density yields greater sequence coverage in high LD, thus requiring more tag SNPs to cover a greater fraction of the genome. Encouragingly, whatever the density employed, a high level of robustness was observed between UK and CEPH samples, as most of the htSNPs selected in one sample were also appropriate as tags in the other.

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.

The future of association studies: gene-based analysis and replication

Historically, association tests were limited to single variants, so that the allele was considered the basic unit for association testing. As marker density increases and indirect approaches are used to assess association through linkage disequilibrium, association is now frequently considered at the haplotypic level. We suggest that there are difficulties in replicating association findings at the single-nucleotide-polymorphism (SNP) or the haplotype level, and we propose a shift toward a gene-based approach in which all common variation within a candidate gene is considered jointly. Inconsistencies arising from population differences are more readily resolved by use of a gene-based approach rather than either a SNP-based or a haplotype-based approach. A gene-based approach captures all of the potential risk-conferring variations; thus, negative findings are subject only to the issue of power. In addition, chance findings due to multiple testing can be readily accounted for by use of a genewide-significance level. Meta-analysis procedures can be formalized for gene-based methods through the combination of P values. It is only a matter of time before all variation within genes is mapped, at which point the gene-based approach will become the natural end point for association analysis and will inform our search for functional variants relevant to disease etiology.