Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity.

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the 'missing heritability'. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10(-4) (95% confidence interval [9.6×10(-5)-3.1×10(-4)]); accounts overall for 0.5% [0.19%-0.82%] of severe childhood obesity cases (P = 3.8×10(-10); odds ratio = 25.0 [9.9-60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m(-2) [1.8-10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.

Bullous pemphigoid: simple measures for a complex disease

Simple descriptive population data are potentially helpful in understanding how bullous pemphigoid (BP) originates and evolves over time. Before embarking with etiological correlations, artifacts and biases should be ruled out. Ideally, epidemiological data should be complemented by immunological and genetic analyses aimed at providing better insight into the causation and prognosis of BP.

Vaccination of cattle with the N terminus of LppQ of Mycoplasma mycoides subsp. mycoides results in type III immune complex disease upon experimental infection.

Contagious bovine pleuropneumonia (CBPP) is a serious respiratory disease of cattle caused by Mycoplasma mycoides subsp. mycoides. Current vaccines against CBPP induce short-lived immunity and can cause severe postvaccine reactions. Previous studies have identified the N terminus of the transmembrane lipoprotein Q (LppQ-N') of M. mycoides subsp. mycoides as the major antigen and a possible virulence factor. We therefore immunized cattle with purified recombinant LppQ-N' formulated in Freund's adjuvant and challenged them with M. mycoides subsp. mycoides. Vaccinated animals showed a strong seroconversion to LppQ, but they exhibited significantly enhanced postchallenge glomerulonephritis compared to the placebo group (P = 0.021). Glomerulonephritis was characterized by features that suggested the development of antigen-antibody immune complexes. Clinical signs and gross pathological scores did not significantly differ between vaccinated and placebo groups. These findings reveal for the first time the pathogenesis of enhanced disease as a result of antibodies against LppQ during challenge and also argue against inclusion of LppQ-N' in a future subunit vaccine for CBPP.

Nonlinear tests for genetic studies of complex disease

Linkage and association studies are major analytical tools to search for susceptibility genes for complex diseases. With the availability of large collection of single nucleotide polymorphisms (SNPs) and the rapid progresses for high throughput genotyping technologies, together with the ambitious goals of the International HapMap Project, genetic markers covering the whole genome will be available for genome-wide linkage and association studies. In order not to inflate the type I error rate in performing genome-wide linkage and association studies, multiple adjustment for the significant level for each independent linkage and/or association test is required, and this has led to the suggestion of genome-wide significant cut-off as low as 5 × 10 −7. Almost no linkage and/or association study can meet such a stringent threshold by the standard statistical methods. Developing new statistics with high power is urgently needed to tackle this problem. This dissertation proposes and explores a class of novel test statistics that can be used in both population-based and family-based genetic data by employing a completely new strategy, which uses nonlinear transformation of the sample means to construct test statistics for linkage and association studies. Extensive simulation studies are used to illustrate the properties of the nonlinear test statistics. Power calculations are performed using both analytical and empirical methods. Finally, real data sets are analyzed with the nonlinear test statistics. Results show that the nonlinear test statistics have correct type I error rates, and most of the studied nonlinear test statistics have higher power than the standard chi-square test. This dissertation introduces a new idea to design novel test statistics with high power and might open new ways to mapping susceptibility genes for complex diseases. ^

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, intersample variation in the proportion of linked families ( a) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage. findings obtained using allele- sharing LOD scores ( LODexp) - which assume homogeneity - and heterogeneity LOD scores ( HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LODexp and two different heterogeneity statistics. One of these ( HLOD- P) estimates the heterogeneity parameter a only in aggregate data, while the second ( HLOD- S) determines a separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LODexp. Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD- S, but not HLOD- P. Using HLOD- S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Entropy Based Approach to Finding Interacting Genes Responsible for Complex Human Disease

2000 Mathematics Subject Classification: 62P10, 92D10, 92D30, 94A17, 62L10.

THE PROCESS OF Leishmania INFECTION - DISEASE AND NEW PERSPECTIVES OF PALEOPARASITOLOGY

Species of the genus Leishmania (Kinetoplastida, Trypanosomatidae) are causative agents of leishmaniasis, a complex disease with variable clinical spectrum and epidemiological diversity, constituting, in some countries, a serious public health problem. The origin and evolution of leishmaniasis has been under discussion regarding some clinical and parasitological aspects. After the introduction of paleoparasitology, molecular methods and immunodiagnostic techniques have been applied allowing the recovery of parasite remains, as well as the diagnosis of past infections in humans and other hosts. The dating of archaeological samples has allowed the parasitological analysis in time and space. This manuscript presents the state of the art of leishmaniasis and prospects related to paleoparasitology studies and their contribution to the evolutionary and phylogenetic clarification of parasites belonging to the genus Leishmania, and the leishmaniasis caused by them.

Gene Expression Profiling and Association Studies Implicate the Neuregulin Signaling Pathway in Behçet's Disease Susceptibility

Behçet's disease (BD) is a complex disease with genetic and environmental risk factors implicated in its etiology; however, its pathophysiology is poorly understood. To decipher BD's genetic underpinnings, we combined gene expression profiling with pathway analysis and association studies. We compared the gene expression profiles in peripheral blood mononuclear cells (PBMCs) of 15 patients and 14 matched controls using Affymetrix microarrays and found that the neuregulin signaling pathway was over-represented among the differentially expressed genes. The Epiregulin (EREG), Amphiregulin (AREG), and Neuregulin-1 (NRG1) genes of this pathway stand out as they are also among the top differentially expressed genes. Twelve haplotype tagging SNPs at the EREG-AREG locus and 15 SNPs in NRG1 found associated in at least one published BD genome-wide association study were tested for association with BD in a dataset of 976 Iranian patients and 839 controls. We found a novel association with BD for the rs6845297 SNP located downstream of EREG, and replicated three associations at NRG1 (rs4489285, rs383632, and rs1462891). Multifactor dimensionality reduction analysis indicated the existence of epistatic interactions between EREG and NRG1 variants. EREG-AREG and NRG1, which are members of the epidermal growth factor (EGF) family, seem to modulate BD susceptibility through main effects and gene–gene interactions. These association findings support a role for the EGF/ErbB signaling pathway inBD pathogenesis that warrants further investigation and highlight the importance of combining genetic and genomic approaches to dissect the genetic architecture of complex diseases.

Schistosomal glomerular disease (a review)

In this review paper schistosomal glomerulopathy is defined as an immune-complex disease. The disease appears in 12-15 per cent of the individuals with hepatosplenic schistosomiasis. Portal hypertension with collateral circulation helps the by pass of the hepatic clearance process and the parasite antigens can bind to antibodies in the circulation and be trapped in the renal glomerulus. Chronic membranousproliferative glomerulonephritis is the most commom lesion present and the nephrotic syndrome is the usual form of clinical presentation. The disease can be experimentally produced, and schistosomal antigens and antibodies, as well as complement, can be demonstrated in the glomerular lesions. Specific treatment of schistosomiasis does not seem to alter the clinical course of schistosomal nephropathy.

Post-genomic update on a classical candidate gene for coronary artery disease: ESR1.

BACKGROUND: After age, sex is the most important risk factor for coronary artery disease (CAD). The mechanism through which women are protected from CAD is still largely unknown, but the observed sex difference suggests the involvement of the reproductive steroid hormone signaling system. Genetic association studies of the gene-encoding Estrogen Receptor α (ESR1) have shown conflicting results, although only a limited range of variation in the gene has been investigated. METHODS AND RESULTS: We exploited information made available by advanced new methods and resources in complex disease genetics to revisit the question of ESR1's role in risk of CAD. We performed a meta-analysis of 14 genome-wide association studies (CARDIoGRAM discovery analysis, N=≈87,000) to search for population-wide and sex-specific associations between CAD risk and common genetic variants throughout the coding, noncoding, and flanking regions of ESR1. In addition to samples from the MIGen (N=≈6000), WTCCC (N=≈7400), and Framingham (N=≈3700) studies, we extended this search to a larger number of common and uncommon variants by imputation into a panel of haplotypes constructed using data from the 1000 Genomes Project. Despite the widespread expression of ERα in vascular tissues, we found no evidence for involvement of common or low-frequency genetic variation throughout the ESR1 gene in modifying risk of CAD, either in the general population or as a function of sex. CONCLUSIONS: We suggest that future research on the genetic basis of sex-related differences in CAD risk should initially prioritize other genes in the reproductive steroid hormone biosynthesis system.

Recent human evolution has shaped geographical differences in susceptibility to disease

Background: Searching for associations between genetic variants and complex diseases has been a very active area of research for over two decades. More than 51,000 potential associations have been studied and published, a figure that keeps increasing, especially with the recent explosion of array-based Genome-Wide Association Studies. Even if the number of true associations described so far is high, many of the putative risk variants detected so far have failed to be consistently replicated and are widely considered false positives. Here, we focus on the world-wide patterns of replicability of published association studies.Results: We report three main findings. First, contrary to previous results, genes associated to complex diseases present lower degrees of genetic differentiation among human populations than average genome-wide levels. Second, also contrary to previous results, the differences in replicability of disease associated-loci between Europeans and East Asians are highly correlated with genetic differentiation between these populations. Finally, highly replicated genes present increased levels of high-frequency derived alleles in European and Asian populations when compared to African populations. Conclusions: Our findings highlight the heterogeneous nature of the genetic etiology of complex disease, confirm the importance of the recent evolutionary history of our species in current patterns of disease susceptibility and could cast doubts on the status as false positives of some associations that have failed to replicate across populations.

Diabetes mellitus, a microRNA-related disease?

Diabetes mellitus is a complex disease resulting in altered glucose homeostasis. In both type 1 and type 2 diabetes mellitus, pancreatic β cells cannot secrete appropriate amounts of insulin to regulate blood glucose level. Moreover, in type 2 diabetes mellitus, altered insulin secretion is combined with a resistance of insulin-target tissues, mainly liver, adipose tissue, and skeletal muscle. Both environmental and genetic factors are known to contribute to the development of the disease. Growing evidence indicates that microRNAs (miRNAs), a class of small noncoding RNA molecules, are involved in the pathogenesis of diabetes. miRNAs function as translational repressors and are emerging as important regulators of key biological processes. Here, we review recent studies reporting changes in miRNA expression in tissues isolated from different diabetic animal models. We also describe the role of several miRNAs in pancreatic β cells and insulin-target tissues. Finally, we discuss the possible use of miRNAs as blood biomarkers to prevent diabetes development and as tools for gene-based therapy to treat both type 1 and type 2 diabetes mellitus.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Asthma Polymorphic Loci genotyping in Madeira population: identification of potential genetic markers of disease susceptibility, severity and clinical relevance

Asthma is a complex disease, influenced by both environmental and genetic factors. In this study, the analysis of multiple environmental factos assessed by questionnaire and the genotyping of SNPs IL131c.144 G/A, IL41590 C/T, IL41RP2 253183, ADRB21c.16 A/G, ADAM331V4 C/G, ADAM331S1 c.710 G/A, GSDML1236 C/T and STAT6121 C/T were performed in a sample of Madeiran asthmatic patients and their families, and their association to asthma susceptibility and severity was assessed. Family, environmental, social and individual factos such as the presence of rhinitis in one of the parents,the habitation conditions, the family smoking habits, individual food habits and allergen sensitivity, were found to account for asthma severity. IL41590*T and IL41RP2*183$ alleles as well as the combined genotypes IL41590*CT/IL41590*TT and IL41 RP2*253183/IL41RP2*253183 were associated to both asthma susceptibility and severity.GSDML1236*TT was found associated only to asthma severity.Allele ADAM331 V4*C was significantly overM transmitted to asthmatic offspring being linked with the disease by TDT. These findings suggest that in addition to environmental influences, IL41 590 C/T, IL41RP2 253183, ADAM331V4 C/G and GSDML1236 C/T SNPs may constitute important genetic factos contributing to asthmasusceptibility and/or severity in Madeira population.