Whole-genome scan, in a complex disease, using 11,245 single-nucleotide polymorphisms: comparison with microsatellites

ABCD of IA: A multi-scale agent-based model of T cell activation in inflammatory arthritis

Biomaterial-based agents have been demonstrated to regulate the function of immune cells in models of autoimmunity. However, the complexity of the kinetics of immune cell activation can present a challenge in optimizing the dose and frequency of administration. Here, we report a model of autoreactive T cell activation, which are key drivers in autoimmune inflammatory joint disease. The model is termed a multi-scale Agent-Based, Cell-Driven model of Inflammatory Arthritis (ABCD of IA). Using kinetic rate equations and statistical theory, ABCD of IA simulated the activation and presentation of autoantigens by dendritic cells, interactions with cognate T cells and subsequent T cell proliferation in the lymph node and IA-affected joints. The results, validated with in vivo data from the T cell driven SKG mouse model, showed that T cell proliferation strongly correlated with the T cell receptor (TCR) affinity distribution (TCR-ad), with a clear transition state from homeostasis to an inflammatory state. T cell proliferation was strongly dependent on the amount of antigen in antigenic stimulus event (ASE) at low concentrations. On the other hand, inflammation driven by Th17-inducing cytokine mediated T cell phenotype commitment was influenced by the initial level of Th17-inducing cytokines independent of the amount of arthritogenic antigen. The introduction of inhibitory artificial antigen presenting cells (iaAPCs), which locally suppress T cell activation, reduced T cell proliferation in a dose-dependent manner. The findings in this work set up a framework based on theory and modeling to simulate personalized therapeutic strategies in IA.

The Augmented Classical Twin Design: Incorporating Genome-Wide Identity by Descent Sharing Into Twin Studies in Order to Model Violations of the Equal Environments Assumption

The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science. The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum. In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance components and valid tests of the EEA under strong assumptions (e.g. no epistatic variance, IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power. Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50,000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Genome-wide single nucleotide polymorphisms (SNPs) for a model invasive ascidian Botryllus schlosseri

Invasive species cause huge damages to ecology, environment and economy globally. The comprehensive understanding of invasion mechanisms, particularly genetic bases of micro-evolutionary processes responsible for invasion success, is essential for reducing potential damages caused by invasive species. The golden star tunicate, Botryllus schlosseri, has become a model species in invasion biology, mainly owing to its high invasiveness nature and small well-sequenced genome. However, the genome-wide genetic markers have not been well developed in this highly invasive species, thus limiting the comprehensive understanding of genetic mechanisms of invasion success. Using restriction site-associated DNA (RAD) tag sequencing, here we developed a high-quality resource of 14,119 out of 158,821 SNPs for B. schlosseri. These SNPs were relatively evenly distributed at each chromosome. SNP annotations showed that the majority of SNPs (63.20%) were located at intergenic regions, and 21.51% and 14.58% were located at introns and exons, respectively. In addition, the potential use of the developed SNPs for population genomics studies was primarily assessed, such as the estimate of observed heterozygosity (H _O ), expected heterozygosity (H _E ), nucleotide diversity (π), Wright's inbreeding coefficient (F _IS ) and effective population size (Ne). Our developed SNP resource would provide future studies the genome-wide genetic markers for genetic and genomic investigations, such as genetic bases of micro-evolutionary processes responsible for invasion success.

MMP-2 gene polymorphisms are associated with type A aortic dissection and aortic diameters in patients

Matrix metalloproteinases-2 (MMP-2) plays an important role in the pathogenesis of type A aortic dissection (AD). The aim of this study was to evaluate the association of 3 single nucleotide polymorphisms (SNPs) in the MMP-2 gene with type A AD risk and aortic diameters in patients. We performed a case-control study with 172 unrelated type A AD patients and 439 controls. Three SNPs rs11644561, rs11643630, and rs243865 were genotyped through the MassARRAY platform. Allelic associations of SNPs and SNP haplotypes with type A AD and aortic diameters in patients were evaluated. The frequency of the G allele of the rs11643630 polymorphism was significantly lower in type A AD patients than in control subjects (odds ratio 0.705, 95% confidence interval 0.545-0.912, P = 0.008). The association remained significant after adjusting for clinical covariates (P = 0.008). Carriers of the GG genotype of the rs11643630 polymorphism had significantly smaller aortic diameters than those with GT genotype or TT genotype (P = 0.02). Further haplotype analysis identified 1 protective haplotype (GC; P = 0.008) for development of type A AD. Again, a significant correlation was observed between haplotype GC and AD size (P = 0.020). Our results suggest that MMP-2 gene polymorphisms contribute to type A AD susceptibility. In addition, MMP-2 gene SNPs are associated with AD size, which could be used as a target for the development of new drug therapy.

Whole-genome single nucleotide polymorphism-based linkage analysis in spondyloarthritis multiplex families reveals a new susceptibility locus in 13q13

OBJECTIVE

Spondyloarthritis (SpA) is a chronic inflammatory disorder with high heritability but with complex genetics. Apart from HLA-B27, most of the underlying genetic components remain to be identified. We conducted a whole-genome high-density non-parametric linkage analysis to identify new genetic factors of susceptibility to SpA.

METHODS

914 subjects including 462 with SpA from 143 multiplex families were genotyped using Affymetrix 250K microarrays. After quality control, 189 368 single nucleotide polymorphisms (SNPs) were kept for further analyses. Both non-parametric and parametric linkage analyses were performed using Merlin software. Association was tested with Unphased.

RESULTS

Non-parametric linkage analysis identified two regions significantly linked to SpA: the major histocompatibility complex (LODmax=24.77) and a new 13q13 locus (LODmax=5.03). Additionally, eight loci achieved suggestive LOD scores, including the previously identified SPA2 locus at 9q33 (LODmax=3.51). Parametric analysis supported a codominant model in 13q13 with a maximum heterogeneity LOD, 'HLOD' score of 3.084 (α=0.28). Identification of meiotic recombination events around the 13q13 linkage peak in affected subjects from the 43 best-linked families allowed us to map the disease interval between 38.753 and 40.040 Mb. Family-based association analysis of the SNPs inside this interval in the best-linked families identified a SNP near FREM2 (rs1945502) which reached a p value close to statistical significance (corrected p=0.08).

CONCLUSION

We report here for the first time a significant linkage between 13q13 and SpA. Identification of susceptibility factor inside this chromosomal region through targeted sequencing in linked families is underway.

Clinical applications of whole-genome association studies: future applications at the bedside

Until now, performing whole-genome association studies has been an unattainable, but highly desirable, goal for geneticists. With the recent advent of high-throughput genotyping platforms, this goal is now a reality for geneticists today and for clinicians in the not-so-distant future. This review will cover a broad range of topics to provide an overview of this emerging branch of genetics, and will provide references to more specific sources. Specifically, this review will cover the technologies available today and in the near future, the specific types of whole-genome association studies, the benefits and limitations of these studies, the applications to complex disease-gene interactions, diagnostic devices, therapeutics, and finally, we will describe the 5-year perspective and key issues.

Identification of a glutamic acid repeat polymorphism of ALMS1 as a novel genetic risk marker for early-onset myocardial infarction by genome-wide linkage analysis

BACKGROUND

Myocardial infarction (MI) is a leading cause of death worldwide. Given that a family history is an independent risk factor for coronary artery disease, genetic variants are thought to contribute directly to the development of this condition. The identification of susceptibility genes for coronary artery disease or MI may thus help to identify high-risk individuals and offer the opportunity for disease prevention.

METHODS AND RESULTS

We designed a 5-step protocol, consisting of a genome-wide linkage study followed by association analysis, to identify novel genetic variants that confer susceptibility to coronary artery disease or MI. A genome-wide affected sib-pair linkage study with 221 Japanese families with coronary artery disease yielded a statistically significant logarithm of the odds score of 3.44 for chromosome 2p13 and MI. Further association analysis implicated Alström syndrome 1 gene (ALMS1) as a candidate gene within the linkage region. Validation association analysis revealed that representative single-nucleotide polymorphisms of the ALMS1 promoter region were significantly associated with early-onset MI in both Japanese and Korean populations. Moreover, direct sequencing of the ALMS1 coding region identified a glutamic acid repeat polymorphism in exon 1, which was subsequently found to be associated with early-onset MI.

CONCLUSIONS

The glutamic acid repeat polymorphism of ALMS1 identified in the present study may provide insight into the pathogenesis of early-onset MI.

The IL-33-ST2L pathway is associated with coronary artery disease in a Chinese Han population

The effects of interleukin-33 (IL-33) on the immune system have been clearly demonstrated; however, in cardiovascular diseases, especially in coronary artery disease (CAD), these effects have not yet been clarified. In this study, we investigate the genetic role of the IL-33-ST2L pathway in CAD. We performed three-stage case-control association analyses on a total of 4,521 individuals with CAD and 4,809 controls via tag SNPs in the genes encoding IL-33 and ST2L-IL-1RL1. One tag SNP in each gene was significantly associated with CAD (rs7025417(T) in IL33, padj = 1.19 × 10(-28), OR = 1.39, 95% CI: 1.31-1.47; rs11685424(G) in IL1RL1, padj = 6.93 × 10(-30), OR = 1.40, 95% CI: 1.32-1.48). Combining significant variants in two genes, the risk for CAD increased nearly 5-fold (padj = 8.90 × 10(-21), OR = 4.98, 95% CI: 3.56-6.97). Traditional risk factors for CAD were adjusted for the association studies by SPSS with logistic regression analysis. With the two variants above, both located within the gene promoter regions, reporter gene analysis indicated that the rs7025417 C>T and rs11685424 A>G changes resulted in altered regulation of IL33 and IL1RL1 gene expression, respectively (p < 0.005). Further studies revealed that the rs7025417 genotype was significantly associated with plasma IL-33 levels in the detectable subjects (n = 227, R(2) = 0.276, p = 1.77 × 10(-17)): the level of IL-33 protein increased with the number of rs7025417 risk (T) alleles. Based on genetic evidence in humans, the IL-33-ST2L pathway appears to have a causal role in the development of CAD, highlighting this pathway as a valuable target for the prevention and treatment of CAD.

Hunting human disease genes: lessons from the past, challenges for the future

The concept that a specific alteration in an individual’s DNA can result in disease is central to our notion of molecular medicine. The molecular basis of more than 3,500 Mendelian disorders has now been identified. In contrast, the identification of genes for common disease has been much more challenging. We discuss historical and contemporary approaches to disease gene identification, focusing on novel opportunities such as the use of population extremes and the identification of rare variants. While our ability to sequence DNA has advanced dramatically, assigning function to a given sequence change remains a major challenge, highlighting the need for both bioinformatics and functional approaches to appropriately interpret these data. We review progress in mapping and identifying human disease genes and discuss future challenges and opportunities for the field.

A genome-wide analysis of 'Bounty' descendants implicates several novel variants in migraine susceptibility

Migraine is a common neurological disease with a complex genetic aetiology. The disease affects ~12% of the Caucasian population and females are three times more likely than males to be diagnosed. In an effort to identify loci involved in migraine susceptibility, we performed a pedigree-based genome-wide association study of the isolated population of Norfolk Island, which has a high prevalence of migraine. This unique population originates from a small number of British and Polynesian founders who are descendents of the Bounty mutiny and forms a very large multigenerational pedigree (Bellis et al.; Human Genetics, 124(5):543-5542, 2008). These population genetic features may facilitate disease gene mapping strategies (Peltonen et al.; Nat Rev Genet, 1(3):182-90, 2000. In this study, we identified a high heritability of migraine in the Norfolk Island population (h (2) = 0.53, P = 0.016). We performed a pedigree-based GWAS and utilised a statistical and pathological prioritisation approach to implicate a number of variants in migraine. An SNP located in the zinc finger protein 555 (ZNF555) gene (rs4807347) showed evidence of statistical association in our Norfolk Island pedigree (P = 9.6 × 10(-6)) as well as replication in a large independent and unrelated cohort with >500 migraineurs. In addition, we utilised a biological prioritisation to implicate four SNPs, in within the ADARB2 gene, two SNPs within the GRM7 gene and a single SNP in close proximity to a HTR7 gene. Association of SNPs within these neurotransmitter-related genes suggests a disrupted serotoninergic system that is perhaps specific to the Norfolk Island pedigree, but that might provide clues to understanding migraine more generally.

Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip

BACKGROUND

The traditional strategy to map QTL is to use linkage analysis employing a limited number of markers. These analyses report wide QTL confidence intervals, making very difficult to identify the gene and polymorphisms underlying the QTL effects. The arrival of genome-wide panels of SNPs makes available thousands of markers increasing the information content and therefore the likelihood of detecting and fine mapping QTL regions. The aims of the current study are to confirm previous QTL regions for growth and body composition traits in different generations of an Iberian x Landrace intercross (IBMAP) and especially identify new ones with narrow confidence intervals by employing the PorcineSNP60 BeadChip in linkage analyses.

RESULTS

Three generations (F3, Backcross 1 and Backcross 2) of the IBMAP and their related animals were genotyped with PorcineSNP60 BeadChip. A total of 8,417 SNPs equidistantly distributed across autosomes were selected after filtering by quality, position and frequency to perform the QTL scan. The joint and separate analyses of the different IBMAP generations allowed confirming QTL regions previously identified in chromosomes 4 and 6 as well as new ones mainly for backfat thickness in chromosomes 4, 5, 11, 14 and 17 and shoulder weight in chromosomes 1, 2, 9 and 13; and many other to the chromosome-wide signification level. In addition, most of the detected QTLs displayed narrow confidence intervals, making easier the selection of positional candidate genes.

CONCLUSIONS

The use of higher density of markers has allowed to confirm results obtained in previous QTL scans carried out with microsatellites. Moreover several new QTL regions have been now identified in regions probably not covered by markers in previous scans, most of these QTLs displayed narrow confidence intervals. Finally, prominent putative biological and positional candidate genes underlying those QTL effects are listed based on recent porcine genome annotation.

A genome-wide homozygosity association study identifies runs of homozygosity associated with rheumatoid arthritis in the human major histocompatibility complex

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with a polygenic mode of inheritance. This study examined the hypothesis that runs of homozygosity (ROHs) play a recessive-acting role in the underlying RA genetic mechanism and identified RA-associated ROHs. Ours is the first genome-wide homozygosity association study for RA and characterized the ROH patterns associated with RA in the genomes of 2,000 RA patients and 3,000 normal controls of the Wellcome Trust Case Control Consortium. Genome scans consistently pinpointed two regions within the human major histocompatibility complex region containing RA-associated ROHs. The first region is from 32,451,664 bp to 32,846,093 bp (−log10(p)>22.6591). RA-susceptibility genes, such as HLA-DRB1, are contained in this region. The second region ranges from 32,933,485 bp to 33,585,118 bp (−log10(p)>8.3644) and contains other HLA-DPA1 and HLA-DPB1 genes. These two regions are physically close but are located in different blocks of linkage disequilibrium, and ∼40% of the RA patients' genomes carry these ROHs in the two regions. By analyzing homozygote intensities, an ROH that is anchored by the single nucleotide polymorphism rs2027852 and flanked by HLA-DRB6 and HLA-DRB1 was found associated with increased risk for RA. The presence of this risky ROH provides a 62% accuracy to predict RA disease status. An independent genomic dataset from 868 RA patients and 1,194 control subjects of the North American Rheumatoid Arthritis Consortium successfully validated the results obtained using the Wellcome Trust Case Control Consortium data. In conclusion, this genome-wide homozygosity association study provides an alternative to allelic association mapping for the identification of recessive variants responsible for RA. The identified RA-associated ROHs uncover recessive components and missing heritability associated with RA and other autoimmune diseases.

Integrated genomic analysis of sézary syndrome

Sézary syndrome (SS) is a rare variant of primary cutaneous T-cell lymphoma. Little is known about the underlying pathogenesis of S. To address this issue, we used Affymetrix 10K SNP microarray to analyse 13 DNA samples isolated from 8 SS patients and qPCR with ABI TaqMan SNP genotyping assays for the validation of the SNP microarray results. In addition, we tested the impact of SNP loss of heterozygosity (LOH) identified in SS cases on the gene expression profiles of SS cases detected with Affymetrix GeneChip U133A. The results showed: (1) frequent SNP copy number change and LOH involving 1, 2p, 3, 4q, 5q, 6, 7p, 8, 9, 10, 11, 12q, 13, 14, 16q, 17, and 20, (2) reduced SNP copy number at FAT gene (4q35) in 75% of SS cases, and (3) the separation of all SS cases from normal control samples by SNP LOH gene clusters at chromosome regions of 9q31q34, 10p11q26, and 13q11q12. These findings provide some intriguing information for our current understanding of the molecular pathogenesis of this tumour and suggest the possibility of presence of functional SNP LOH in SS tumour cells.

Genome-wide linkage analysis of Swedish families to identify putative susceptibility loci for cutaneous malignant melanoma

Cutaneous malignant melanoma is a clinically and genetically heterogeneous disorder which is caused by an interaction between hereditary and environmental factors. In Sweden, a small portion of the inherited susceptibility is explained by the presence of germline mutations in the tumor suppressor gene CDKN2A. But still, the genetic background of melanoma susceptibility is largely unknown. Here, we conducted a genome-wide linkage scan on melanoma-prone families using high-density single-nucleotide polymorphisms (SNPs) arrays to identify novel melanoma susceptibility genes. We investigated 35 families of Swedish origin without CDKN2A mutations. Nonparametric and parametric multipoint linkage analyses were performed. After removal of SNPs in strong linkage disequilibrium, the strongest evidence of linkage was detected on chromosome 17p11-12 (logarithm (base 10) of odds (LOD) scores of 2.76) using parametric linkage analysis assuming a dominant trait with full penetrance. Analyses were also performed on a subset of families with low age at diagnosis (mean age ≤ 47 years), to obtain a more homogenous subset. This subgroup analysis based on 22 families yielded suggestive evidence of linkage to the chromosomal regions 11p12-p11 and 18q22 (multipoint LOD scores of 2.10 and 2.02, respectively). Also, the 17p region that was detected in the complete family set showed suggestive linkage in this cohort (multipoint LOD scores of 2.01). Our data suggest that these chromosomal regions, 17p12-p11 in particular as it was present in both analyses, may harbor genes involved in the susceptibility of malignant melanoma in the Swedish population.

A powerful approach for association analysis incorporating imprinting effects

MOTIVATION

For a diallelic marker locus, the transmission disequilibrium test (TDT) is a simple and powerful design for genetic studies. The TDT was originally proposed for use in families with both parents available (complete nuclear families) and has further been extended to 1-TDT for use in families with only one of the parents available (incomplete nuclear families). Currently, the increasing interest of the influence of parental imprinting on heritability indicates the importance of incorporating imprinting effects into the mapping of association variants.

RESULTS

In this article, we extend the TDT-type statistics to incorporate imprinting effects and develop a series of new test statistics in a general two-stage framework for association studies. Our test statistics enjoy the nature of family-based designs that need no assumption of Hardy-Weinberg equilibrium. Also, the proposed methods accommodate complete and incomplete nuclear families with one or more affected children. In the simulation study, we verify the validity of the proposed test statistics under various scenarios, and compare the powers of the proposed statistics with some existing test statistics. It is shown that our methods greatly improve the power for detecting association in the presence of imprinting effects. We further demonstrate the advantage of our methods by the application of the proposed test statistics to a rheumatoid arthritis dataset.

CONTACT

wingfung@hku.hk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-wide SNP-based linkage analysis for ADNSHL families identifies novel susceptibility loci with positive evidence for linkage

The linkage search for susceptibility loci using SNP markers in hereditary hearing loss has proven challenging due to genetic heterogeneity. We conducted a genome-wide linkage analysis using high-density SNP markers in two Korean families (families coded SD-J and SR-167) with autosomal dominant non-syndromic hearing loss (ADNSHL). Evidence was found of linkage at 8q24.13~q24.3 and 10p11.21~q22.2 (LOD 3.01) in the SD-J family. In the case of family SR-167, which had the most affected members, the parametric LOD score was low owing to the lack of power for linkage analysis. However, using non-parametric linkage analysis, it was possible to obtain significant evidence for linkage at 10q22.1~q23.31 (LOD 1.79; NPL 6.47, P<0.00001). There is an overlapping region with a significant LOD score between the SD-J and SR-167 families, which encompasses 4 cM at 10q22.1~22.2. Interestingly, the characteristics of hearing loss in both families were similar, and the haplotype within overlapping region was shared in the affected individuals of the two families. We performed direct sequencing of the candidate genes that are thought to be causing the condition, but no disease-causing mutations were identified.

Strategies for genotyping

The identification of genomic loci linked to or associated with human disease has been greatly facilitated by the evolution of genotyping strategies and techniques. The success of these strategies continues to be based upon clear clinical assessment, accurate sample handling, and careful data management, but also increasingly upon experimental design. Technological advances in the field of genotyping have permitted increasingly complex and large population studies to be performed. An understanding of publicly available genetic variation databases, including an awareness of the limitations of these data, and an appreciation of the strategic approaches that should be used to exploit this information will provide tremendous insight for researchers are aiming to utilize this accessible technology. As genome-wide association studies (GWAS) and Next Generation (NextGen) sequencing become the mainstays of genetic analyses, it is important that their technical strengths and limitations, as well as their impact on study design, be understood before use in a linkage or genetic association study.

Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies

In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up- or down-weight P-values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on-going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results (P-values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome-wide linkage study of colorectal cancer, and a genome-wide association study of colorectal polyps.

Detection of parent-of-origin effects using general pedigree data

Genomic imprinting is an important epigenetic factor in complex traits study, which has generally been examined by testing for parent-of-origin effects of alleles. For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to incomplete nuclear families (1-PAT and C-PAT) are simple and powerful for detecting parent-of-origin effects. However, these methods are suitable only for nuclear families and thus are not amenable to general pedigree data. Use of data from extended pedigrees, if available, may lead to more powerful methods than randomly selecting one two-generation nuclear family from each pedigree. In this study, we extend PAT to accommodate general pedigree data by proposing the pedigree PAT (PPAT) statistic, which uses all informative family trios from pedigrees. To fully utilize pedigrees with some missing genotypes, we further develop the Monte Carlo (MC) PPAT (MCPPAT) statistic based on MC sampling and estimation. Extensive simulations were carried out to evaluate the performance of the proposed methods. Under the assumption that the pedigrees and their associated affection patterns are randomly drawn from a population of pedigrees with at least one affected offspring, we demonstrated that MCPPAT is a valid test for parent-of-origin effects in the presence of association. Further, MCPPAT is much more powerful compared to PAT for trios or even PPAT for all informative family trios from the same pedigrees if there is missing data. Application of the proposed methods to a rheumatoid arthritis dataset further demonstrates the advantage of MCPPAT.

Identification of susceptibility loci at 7q31 and 9p13 for bipolar disorder in an isolated population

We performed a linkage analysis on 23 Finnish families with bipolar disorder and originating from the North-Eastern region of Finland, using the Illumina Linkage Panel IV (6K) Array with an average intermarker spacing of 0.65 cM across the genome. We detected genome-wide significant evidence for linkage of mood disorder (bipolar disorder type I, II, or not otherwise specified, manic type of schizoaffective psychosis, cyclothymia, or recurrent depression) to chromosomes 7q31 (LOD = 3.20) and 9p13.1 (LOD = 4.02). Analyzing the best markers on the complete set of 179 Finnish bipolar families supported the findings on chromosome 9p13 (maximum LOD score of 3.02 at position 383 Mb, immediately upstream of the centromere). This region harbors several interesting candidate genes, including contactin associated protein-like 3 (CNTNAP3) and aldehyde dehydrogenase 1 (ALDH1B1). For the 7q31 locus, only one extended pedigree and ten families originating from the same late settlement region in North-Eastern Finland provided evidence for linkage, suggesting that a gene predisposing to bipolar disorder is enriched in that region. Candidate genes of interest in this locus include potassium-voltage-gated channel, member 2 (KCND2) and calcium-dependent activator protein for secretion 2 (CADPS2). The loci on the centromeric region of 9p13 and the telomeric region of 7q31 may represent susceptibility loci for mood disorder in the Finnish population.

A comparison of SNPs and microsatellites as linkage mapping markers: lessons from the zebra finch (Taeniopygia guttata)

BACKGROUND

Genetic linkage maps are essential tools when searching for quantitative trait loci (QTL). To maximize genome coverage and provide an evenly spaced marker distribution a combination of different types of genetic marker are sometimes used. In this study we created linkage maps of four zebra finch (Taeniopygia guttata) chromosomes (1, 1A, 2 and 9) using two types of marker, Single Nucleotide Polymorphisms (SNPs) and microsatellites. To assess the effectiveness and accuracy of each kind of marker we compared maps built with each marker type separately and with both types of marker combined. Linkage map marker order was validated by making comparisons to the assembled zebra finch genome sequence.

RESULTS

We showed that marker order was less reliable and linkage map lengths were inflated for microsatellite maps relative to SNP maps, apparently due to differing error rates between the two types of marker. Guidelines on how to minimise the effects of error are provided. In particular, we show that when combining both types of marker the conventional process of building linkage maps, whereby the most informative markers are added to the map first, has to be modified in order to improve map accuracy.

CONCLUSIONS

When using multiple types and large numbers of markers to create dense linkage maps, the least error prone loci (SNPs) rather than the most informative should be used to create framework maps before the addition of other potentially more error prone markers (microsatellites). This raises questions about the accuracy of marker order and predicted recombination rates in previous microsatellite linkage maps which were created using the conventional building process, however, provided suitable error detection strategies are followed microsatellite-based maps can continue to be regarded as reasonably reliable.

A genome-wide linkage analysis in 181 German sarcoidosis families using clustered biallelic markers

BACKGROUND

Sarcoidosis (SA) is a systemic granulomatous inflammatory disorder with complex etiology and strong clustering in families. Genome-wide association studies have been successful in the identification of common risk variants for the disease. To reveal susceptibility variants with low frequencies but strong effects, we performed a genome-wide linkage scan in a large sample of SA families.

METHODS

We genotyped 528 members of 181 German SA families for 3,882 single nucleotide polymorphism assays from the SNPlex System Human Linkage Mapping Set 4K.

RESULTS

Nonparametric linkage analysis revealed one region of suggestive linkage on chromosome 12p13.31 at 20 cM (logarithm of odds [LOD] = 2.53; local P value = .0003) and another linkage peak of nearly suggestive linkage on 9q33.1 at 134 cM (LOD = 2.12; local P value = .0009). The latter has been reported to show suggestive evidence for linkage in a sample of 229 African American SA families previously. Analysis of acute and chronically affected families revealed a subphenotype-specific linkage pattern and an additional, nearly suggestive linkage peak on chromosome 16p13.11 at 38 cM (LOD = 2.09; local P value = .001), which was confined to acute SA.

CONCLUSION

Our results propose that the respective regions might harbor yet-unidentified, possibly subphenotype-specific risk factors for the disease (eg, with immune-related functions like the tumor necrosis factor receptor 1). They should be proved to be important for SA pathogenesis and investigated in detail with an emphasis on rare variants. Subphenotype-specific risk factors might serve for prognosis of the clinical course of the disease.

Overview of linkage analysis in complex traits

Linkage analysis is a well-established and powerful method for mapping disease genes. While linkage analysis has been most successful when applied to disorders with clear patterns of Mendelian inheritance, it can also be a useful technique for mapping susceptibility genes for common complex diseases. In this unit, we outline the key concepts of complex disease, and how linkage analysis for complex traits differs from simple Mendelian traits. Optimal genetic studies require careful study design, ascertainment strategy, and analysis methods. We describe how disease parameters such as prevalence, heritability estimates, and mode of inheritance should be considered before data is collected. Furthermore, we outline a general strategic approach for conducting linkage analysis of a complex disease, along with several design considerations that can optimize statistical power to detect disease loci and generally improve the quality of a study. Finally, we discuss the benefits and weaknesses of linkage analysis in contrast to genome-wide association studies.

Use of supplementary phenotype to identify additional rheumatoid arthritis loci in a linkage analysis of 342 UK affected sibling pair families

Background

Although rheumatoid arthritis has been shown to have moderately strong genetic component, both linked loci identified in linkage analyses and susceptibility variants from association studies are short of adequately accounting for a comprehensive catalogue of the molecular factors underlying this complex disease. The objective of this study was to use supplementary phenotype based on cumulative hazard of rheumatoid arthritis to identify linkage evidence for new and additional rheumatoid arthritis loci in a genome-wide linkage analysis of 342 affected sibling pair families from the United Kingdom.

Methods

Using proportional hazards model, we estimated cumulative hazard of rheumatoid arthritis and then used it as a quantitative trait in a non-parametric multipoint variance component linkage analysis with 353 microsatellite markers distributed across the 22 autosomal chromosomes.

Results

We identified 3 new loci with genome-wide suggestive linkage evidence for rheumatoid arthritis on 9q21.13, 15p11.1 and 20q13.33. Our results also confirmed previously reported linkage evidence in the HLA-DRB1 region on chromosome 6 and on locus 1q32.1.

Conclusion

This study demonstrates the potential for information gain through the use of supplementary phenotypes in genetic study of complex diseases to identify new and additional potential linked loci that are not detected by linkage analysis of traditional phenotypes; and our results provide further evidence of the involvement of multiple loci in the genetic aetiology of rheumatoid arthritis.

Genome wide high density SNP-based linkage analysis of childhood absence epilepsy identifies a susceptibility locus on chromosome 3p23-p14

Childhood absence epilepsy (CAE) is an idiopathic generalised epilepsy (IGE) characterised by typical absence seizures manifested by transitory loss of awareness with 2.5-4 Hz spike-wave complexes on ictal EEG. A genetic component to the aetiology is well recognised but the mechanism of inheritance and the genes involved are yet to be fully established. A genome wide single nucleotide polymorphism (SNP)-based high density linkage scan was carried out using 41 nuclear pedigrees with at least two affected members. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1 and a susceptibility locus was identified on chromosome 3p23-p14 (Z(mean)=3.9, p<0.0001; HLOD=3.3, alpha=0.7). The linked region harbours the functional candidate genes TRAK1 and CACNA2D2. Fine-mapping using a tagSNP approach demonstrated disease association with variants in TRAK1.

Genetic susceptibility loci in rheumatoid arthritis establish transcriptional regulatory networks with other genes

Linkage studies have identified the human leukocyte antigen (HLA)-DRB1 as a putative rheumatoid arthritis (RA) susceptibility locus (SL). Nevertheless, it was estimated that its contribution was partial, suggesting that other non-HLA genes may play a role in RA susceptibility. To test this hypothesis, we conducted microarray transcription profiling of peripheral blood mononuclear cells in 15 RA patients and analyzed the data, using bioinformatics programs (significance analysis of microarrays method and GeneNetwork), which allowed us to determine the differentially expressed genes and to reconstruct transcriptional networks. The patients were grouped according to disease features or treatment with tumor necrosis factor blocker. Transcriptional networks that were reconstructed allowed us to identify the interactions occurring between RA SL and other genes, for example, HLA-DRB1 interacting with FNDC3A (fibronectin type III domain containing 3A). Given that fibronectin fragments can stimulate mediators of matrix and cartilage destruction in RA, this interaction is of special interest and may contribute to a clearer understanding of the functional role of HLA-DRB1 in RA pathogenesis.

Identification of Linkage Disequilibrium SNPs from a Kidney-Yang Deficiency Syndrome Pedigree

In order to probe the genetic traits of Kidney-yang Deficiency Syndrome (KDS), we employed a national standard of KDS diagnosis for the collection of KDS subjects. Each candidate KDS subject from a typical family was diagnosed by 5 independent physicians of Traditional Chinese Medicine (TCM), and repeated for 3 years, all on the first Saturday of December. Fifteen samples of genomic DNA were isolated and genotyped by Affymetrix 100 K arrays of single nucleotide polymorphism (SNP). Then appropriate tools were used for the analysis of linkage disequilibrium (LD) and bioinformatic mining of LD SNPs. The results indicated that our procedure of TCM diagnosis can effectively collect KDS subjects and therefore provide substantial basis for the linkage analysis of KDS. Five SNPs (i.e. rs514207, rs1054020, rs7685923, rs10515889 and rs10516202) were identified as LD SNPs from this KDS family, representing an unprecedented set of LD SNPs derived from TCM syndrome. These SNPs demonstrate midrange linkage disequilibrium within the KDS family. Two genes with established functions were identified within 100 bp of these SNPs. One is Homo sapiens double cortin domain containing 5, which interacts selectively with mono-, di- or tri-saccharide carbohydrate and involves certain signaling cascades. Another one, leucyl-tRNA synthetase, is also a pleiotropic gene response to cysteinyl-tRNA aminoacylation and protein biosynthesis. In conclusion, KDS is involved in special SNP linkage disequilibrium in the intragenic level, and genes within the flanks of these SNPs suggest some essential symptoms of KDS. However, definitive evidence to confirm or exclude these loci and to establish their biological activities will be required.

Association of the X-chromosomal genes TIMP1 and IL9R with rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis (RA) is an inflammatory joint disease with features of an autoimmune disease with female predominance. Candidate genes located on the X-chromosome were selected for a family trio-based association study.

METHODS

A total of 1452 individuals belonging to 3 different sample sets were genotyped for 16 single-nucleotide polymorphisms (SNP) in 7 genes. The first 2 sets consisted of 100 family trios, each of French Caucasian origin, and the third of 284 additional family trios of European Caucasian origin. Subgroups were analyzed according to sex of patient and presence of anti-cyclic citrullinated peptide (anti-CCP) autoantibodies.

RESULTS

Four SNP were associated with RA in the first sample set and were genotyped in the second set. In combined analysis of sets 1 and 2, evidence remained for association of 3 SNP in the genes UBA1, TIMP1, and IL9R. These were again genotyped in the third sample set. Two SNP were associated with RA in the joint analysis of all samples: rs6520278 (TIMP1) was associated with RA in general (p = 0.035) and rs3093457 (IL9R) with anti-CCP-positive RA patients (p = 0.037) and male RA patients (p = 0.010). A comparison of the results with data from whole-genome association studies further supports an association of RA with TIMPL The sex-specific association of rs3093457 (IL9R) was supported by the observation that men homozygous for rs3093457-CC are at a significantly higher risk to develop RA than women (risk ratio male/female = 2.98; p = 0.048).

CONCLUSION

We provide evidence for an association of at least 2 X-chromosomal genes with RA: TIMP1 (rs6520278) and IL9R (rs3093457).

Genetic association study of FOXP3 polymorphisms in allergic rhinitis in a Chinese population

The FOXP3 gene encodes a transcription factor thought to be essential for the development and function of regulatory T cells, which are prevailing mediators of immunological tolerance via suppression/modulation of both T helper (Th)-1 and Th-2 mediated immune responses. Previous studies have demonstrated an association between common polymorphisms in FOXP3 and a number of immune diseases. The aim of this study was to investigate whether genetic polymorphisms at the FOXP3 locus predispose to allergic rhinitis (AR) in a Chinese Han population. Six polymorphisms in promoter and intron areas were genotyped in 193 AR subjects and 191 healthy controls. Twelve exons were also analyzed among cohorts of 157 AR patients and 118 healthy controls. Whole-population and gender strata analyses revealed that no single nucleotide polymorphisms in FOXP3 were identified as significantly associated with AR. Regarding the stratified analysis for heterozygotes and homozygotes, the heterozygous allele in rs3761548 (p = 0.020, OR(Het) = 3.12) appeared significant. Subgroup analysis for the presence of different allergen allergies also demonstrated a significant association for house dust mites (rs3060515, p = 0.010, odds ratio (OR) = 2.18; rs3761547, p = 0.013, OR = 2.00). Additionally, no polymorphisms in coding regions contributing to a susceptibility to AR were noted. Our study provides the first evidence for the association of the FOXP3 polymorphism with AR in a Chinese population.

Finding common susceptibility variants for complex disease: past, present and future

The identification of complex disease susceptibility loci has been accelerated considerably by advances in high-throughput genotyping technologies, improved insight into correlation patterns of common variants and the availability of large-scale sample sets. Linkage scans and small-scale candidate gene studies have now given way to genome-wide association scans. In this review, we summarize insights gained from the past, highlight practical issues relating to the design and analysis of current state-of-the-art GWA studies and look into future trends in the field of human complex trait genetics.

Multivariate association test using haplotype trend regression

Genetic association analyses with haplotypes may be more powerful than analyses with single markers, under certain conditions. Furthermore, simultaneously considering multiple correlated traits may make use of additional information that would not be considered when analyzing individual traits. In this study, we propose a haplotype based test of association for multivariate quantitative traits in unrelated samples. Specifically, we extend a population based haplotype trend regression (HTR) approach to multivariate scenarios. We mainly focused on bivariate HTR, and the simulation results showed that the proposed method had correct pre-specified type-I error rates. The power of the proposed method was largely influenced by the size and source of correlation between variables, being greatest when correlation of a specific gene was opposite in sign to the residual correlation.

A high-density SNP genome-wide linkage scan in a large autism extended pedigree

We performed a high-density, single nucleotide polymorphism (SNP), genome-wide scan on a six-generation pedigree from Utah with seven affected males, diagnosed with autism spectrum disorder. Using a two-stage linkage design, we first performed a nonparametric analysis on the entire genome using a 10K SNP chip to identify potential regions of interest. To confirm potentially interesting regions, we eliminated SNPs in high linkage disequilibrium (LD) using a principal components analysis (PCA) method and repeated the linkage results. Three regions met genome-wide significance criteria after controlling for LD: 3q13.2-q13.31 (nonparametric linkage (NPL), 5.58), 3q26.31-q27.3 (NPL, 4.85) and 20q11.21-q13.12 (NPL, 5.56). Two regions met suggestive criteria for significance 7p14.1-p11.22 (NPL, 3.18) and 9p24.3 (NPL, 3.44). All five chromosomal regions are consistent with other published findings. Haplotype sharing results showed that five of the affected subjects shared more than a single chromosomal region of interest with other affected subjects. Although no common autism susceptibility genes were found for all seven autism cases, these results suggest that multiple genetic loci within these regions may contribute to the autism phenotype in this family, and further follow-up of these chromosomal regions is warranted.

Annotated chromosome maps for renal disease

A combination of linkage analyses and association studies are currently employed to promote the identification of genetic factors contributing to inherited renal disease. We have standardized and merged complex genetic data from disparate sources, creating unique chromosomal maps to enhance genetic epidemiological investigations. This database and novel renal maps effectively summarize genomic regions of suggested linkage, association, or chromosomal abnormalities implicated in renal disease. Chromosomal regions associated with potential intermediate clinical phenotypes have been integrated, adding support for particular genomic intervals. More than 500 reports from medical databases, published scientific literature, and the World Wide Web were interrogated for relevant renal-related information. Chromosomal regions highlighted for prioritized investigation of renal complications include 3q13-26, 6q22-27, 10p11-15, 16p11-13, and 18q22. Combined genetic and physical maps are effective tools to organize genetic data for complex diseases. These renal chromosome maps provide insights into renal phenotype-genotype relationships and act as a template for future genetic investigations into complex renal diseases. New data from individual researchers and/or future publications can be readily incorporated to this resource via a user-friendly web-form accessed from the website: www.qub.ac.uk/neph-res/CORGI/index.php.

SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data

BACKGROUND

During this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.

RESULTS

We have developed SNP HiTLink (SNP High Throughput Linkage analysis system). In this system, SNP chip data of the Affymetrix Mapping 100 k/500 k array set and Genome-Wide Human SNP array 5.0/6.0 can be directly imported and passed to parametric or model-free linkage analysis programs; MLINK, Superlink, Merlin and Allegro. Various marker-selecting functions are implemented to avoid the effect of typing-error data, markers in linkage equilibrium or to select informative data.

CONCLUSION

The results using the 100 k SNP dataset were comparable or even superior to those obtained from analyses using microsatellite markers in terms of LOD scores obtained. General personal computers are sufficient to execute the process, as runtime for whole-genome analysis was less than a few hours. This system can be widely applied to linkage analysis using microarray-based SNP data and with which one can expect high-throughput and reliable linkage analysis.

High resolution linkage and linkage disequilibrium analyses of chromosome 1p36 SNPs identify new positional candidate genes for low bone mineral density

A quantitative trait locus (QTL) for BMD maps to chromosome 1p36. We have analyzed a high density SNP panel from this region for linkage and association to BMD in 39 osteoporosis pedigrees. Our results support the presence of genes controlling BMD on 1p36 and indicate new candidates for further analyses.

INTRODUCTION

Low BMD is one of the major risk factors for osteoporosis. Following a genome scan in a sample of Caucasian families recruited through probands with low BMD, a region on 1p36 near marker D1S214 received support as a QTL for BMD from linkage (maximum lod-score = 2.87) and linkage disequilibrium (LD) analysis (p < 0.01).

METHODS

To better characterize the genetic risk factors for low BMD located in this genomic region, we have genotyped the same group of families for 1095 SNPs located across 11 Mb on 1p36. Linkage and LD analyses have been performed using the variance component approach.

RESULTS

Multivariate linkage analysis indicated two QTLs for femoral neck BMD, lumbar spine BMD and trochanter BMD simultaneously on 1p36, with maximum lod-scores of 4.37 at 12 cM and 3.59 at 22 cM. LD analysis identified several SNPs potentially associated with BMD, including the RERE gene SNP rs11121179 (p = 0.000005 for lumbar spine BMD). Other candidate genes include G1P2, SSU72 and CCDC27 (each containing 1 SNP with p < 0.001 for at least one BMD trait).

CONCLUSIONS

This study supports the presence in 1p36 of QTLs affecting BMD at multiple skeletal sites. Replication of our results in other independent cohorts is warranted.

Genome-wide linkage and admixture mapping of type 2 diabetes in African American families from the American Diabetes Association GENNID (Genetics of NIDDM) Study Cohort

OBJECTIVE

We used a single nucleotide polymorphism (SNP) map in a large cohort of 580 African American families to identify regions linked to type 2 diabetes, age of type 2 diabetes diagnosis, and BMI.

RESEARCH DESIGN AND METHODS

After removing outliers and problematic samples, we conducted linkage analysis using 5,914 SNPs in 1,344 individuals from 530 families. Linkage analysis was conducted using variance components for type 2 diabetes, age of type 2 diabetes diagnosis, and BMI and nonparametric linkage analyses. Ordered subset analyses were conducted ranking on age of type 2 diabetes diagnosis, BMI, waist circumference, waist-to-hip ratio, and amount of European admixture. Admixture mapping was conducted using 4,486 markers not in linkage disequilibrium.

RESULTS

The strongest signal for type 2 diabetes (logarithm of odds [LOD] 4.53) was a broad peak on chromosome 2, with weaker linkage to age of type 2 diabetes diagnosis (LOD 1.82). Type 2 diabetes and age of type 2 diabetes diagnosis were linked to chromosome 13p (3-22 cM; LOD 2.42 and 2.46, respectively). Age of type 2 diabetes diagnosis was linked to 18p (66 cM; LOD 2.96). We replicated previous reports on chromosome 7p (79 cM; LOD 2.93). Ordered subset analysis did not overlap with linkage of unselected families. The best admixture score was on chromosome 12 (90 cM; P = 0.0003).

CONCLUSIONS

The linkage regions on chromosomes 7 (27-78 cM) and 18p overlap prior reports, whereas regions on 2p and 13p linkage are novel. Among potential candidate genes implicated are TCF7L1, VAMP5, VAMP8, CDK8, INSIG2, IPF1, PAX8, IL18R1, members of the IL1 and IL1 receptor families, and MAP4K4. These studies provide a complementary approach to genome-wide association scans to identify causative genes for African American diabetes.

Differential gene expression of peripheral blood mononuclear cells from rheumatoid arthritis patients may discriminate immunogenetic, pathogenic and treatment features

This study aimed to evaluate the association between the differential gene expression profiling of peripheral blood mononuclear cells of rheumatoid arthritis patients with their immunogenetic (human leucocyte antigen shared-epitope, HLA-SE), autoimmune response [anti-cyclic citrullinated peptide (CCP) antibodies], disease activity score (DAS-28) and treatment (disease-modifying antirheumatic drugs and tumour necrosis factor blocker) features. Total RNA samples were copied into Cy3-labelled complementary DNA probes, hybridized onto a glass slide microarray containing 4500 human IMAGE complementary DNA target sequences. The Cy3-monocolour microarray images from patients were quantified and normalized. Analysis of the data using the significance analysis of microarrays algorithm together with a Venn diagram allowed the identification of shared and of exclusively modulated genes, according to patient features. Thirteen genes were exclusively associated with the presence of HLA-SE alleles, whose major biological function was related to signal transduction, phosphorylation and apoptosis. Ninety-one genes were associated with disease activity, being involved in signal transduction, apoptosis, response to stress and DNA damage. One hundred and one genes were associated with the presence of anti-CCP antibodies, being involved in signal transduction, cell proliferation and apoptosis. Twenty-eight genes were associated with tumour necrosis factor blocker treatment, being involved in intracellular signalling cascade, phosphorylation and protein transport. Some of these genes had been previously associated with rheumatoid arthritis pathogenesis, whereas others were unveiled for future research.

Gene expression profiling in rheumatoid arthritis: current concepts and future directions

Over the last years microarray technologies have generated new perspectives for the high-throughput analysis of biological systems. Nowadays, it is possible to monitor thousands of genes in a single experiment. This molecular profiling technology combined with standardised and validated clinical measurements can allow a more precise characterisation of a patient's phenotype, and may lead to the design of therapeutic protocols and procedures better tailored to an individual patient's needs. In this report we provide an overview of expression profiling studies in rheumatoid arthritis (RA). RA is a chronic inflammatory disease in which both genetic and environmental factors are involved. The precise molecular mechanisms underlying RA are not fully understood. A systematic literature search revealed nine array-based expression profiling studies in patients with RA. Findings from these studies were compared with those of linkage and genome-wide association (GWA) studies. Although we observed many differences in study design, analysis and interpretation of results between the different studies, we extracted two sets of genes: (1) those differentially expressed in more than one study, and (2) genes differentially expressed in at least one of the reviewed studies and present in RA linkage or GWA loci. We suggest that both sets of genes include interesting candidate genes for further study in RA.

Meta-analysis of genome-wide linkage studies across autoimmune diseases

Autoimmune diseases are chronic disorders initiated by a loss of immunologic tolerance to self-antigens. They cluster within families, and patients may be diagnosed with more than one disease, suggesting pleiotropic genes are involved in the aetiology of different diseases. To identify potential loci, which confer susceptibility to autoimmunity independent of disease phenotype, we pooled results from genome-wide linkage studies, using the genome scan meta-analysis method (GSMA). The meta-analysis included 42 independent studies for 11 autoimmune diseases, using 7350 families with 18 291 affected individuals. In addition to the HLA region, which showed highly significant genome-wide evidence for linkage, we obtained suggestive evidence for linkage on chromosome 16, with peak evidence at 10.0-19.8 Mb. This region may harbour a pleiotropic gene (or genes) conferring risk for several diseases, although no such gene has been identified through association studies. We did not identify evidence for linkage at several genes known to confer increased risk to different autoimmune diseases (PTPN22, CTLA4), even in subgroups of diseases consistently found to be associated with these genes. The relative risks conferred by variants in these genes are modest (<1.5 in most cases), and even a large study like this meta-analysis lacks power to detect linkage. This study illustrates the concept that linkage and association studies have power to identify very different types of disease-predisposing variants.

Shared genomic segment analysis. Mapping disease predisposition genes in extended pedigrees using SNP genotype assays

We examine the utility of high density genotype assays for predisposition gene localization using extended pedigrees. Results for the distribution of the number and length of genomic segments shared identical by descent among relatives previously derived in the context of genomic mismatch scanning are reviewed in the context of dense single nucleotide polymorphism maps. We use long runs of loci at which cases share a common allele identically by state to localize hypothesized predisposition genes. The distribution of such runs under the hypothesis of no genetic effect is evaluated by simulation. Methods are illustrated by analysis of an extended prostate cancer pedigree previously reported to show significant linkage to chromosome 1p23. Our analysis establishes that runs of simple single locus statistics can be powerful, tractable and robust for finding DNA shared between relatives, and that extended pedigrees offer powerful designs for gene detection based on these statistics.

Genome-wide high-density SNP-based linkage analysis of infantile hypertrophic pyloric stenosis identifies loci on chromosomes 11q14-q22 and Xq23

Infantile hypertrophic pyloric stenosis (IHPS) has an incidence of 1-8 per 1000 live births and is inherited as a complex sex-modified multifactorial trait with a striking male preponderance. Syndromic and monogenic forms exist, and two loci have been identified. Infants present with vomiting due to gastric-outlet obstruction caused by hypertrophy of the smooth muscle of the pylorus. A genome-wide SNP-based high-density linkage scan was carried out on 81 IHPS pedigrees. Nonparametric and parametric linkage analysis identified loci on chromosomes 11q14-q22 (Z(max) = 3.9, p < 0.0001; HLOD(max) = 3.4, alpha = 0.34) and Xq23 (Z(max) = 4.3, p < 0.00001; HLOD(max) = 4.8, alpha = 0.56). The two linked chromosomal regions each harbor functional candidate genes that are members of the canonical transient receptor potential (TRPC) family of ion channels and have a potential role in smooth-muscle control and hypertrophy.

Incorporating covariates in mapping heterogeneous traits: a hierarchical model using empirical Bayes estimation

Complex genetic traits are inherently heterogeneous, i.e., they may be caused by different genes, or non-genetic factors, in different individuals. So, for mapping genes responsible for these diseases using linkage analysis, heterogeneity must be accounted for in the model. Heterogeneity across different families can be modeled using a mixture distribution by letting each family have its own heterogeneity parameter denoting the probability that its disease-causing gene is linked to the marker map under consideration. A substantial gain in power is expected if covariates that can discriminate between the families of linked and unlinked types are incorporated in this modeling framework. To this end, we propose a hierarchical Bayesian model, in which the families are grouped according to various (categorized) levels of covariate(s). The heterogeneity parameters of families within each group are assigned a common prior, whose parameters are further assigned hyper-priors. The hyper-parameters are obtained by utilizing the empirical Bayes estimates. We also address related issues such as evaluating whether the covariate(s) under consideration are informative and grouping of families. We compare the proposed approach with one that does not utilize covariates and show that our approach leads to considerable gains in power to detect linkage and in precision of interval estimates through various simulation scenarios. An application to the asthma datasets of Genetic Analysis Workshop 12 also illustrates this gain in a real data analysis. Additionally, we compare the performances of microsatellite markers and single nucleotide polymorphisms for our approach and find that the latter clearly outperforms the former.